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Hans R, Sharma SK, Aickelin U. Optimised deep k-nearest neighbour's based diabetic retinopathy diagnosis(ODeep-NN) using retinal images. Health Inf Sci Syst 2024; 12:23. [PMID: 38469456 PMCID: PMC10924814 DOI: 10.1007/s13755-024-00282-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 02/18/2024] [Indexed: 03/13/2024] Open
Abstract
Diabetes mellitus has been regarded as one of the prime health issues in present days, which can often lead to diabetic retinopathy, a complication of the disease that affects the eyes, causing loss of vision. For precisely detecting the condition's existence, clinicians are required to recognise the presence of lesions in colour fundus images, making it an arduous and time-consuming task. To deal with this problem, a lot of work has been undertaken to develop deep learning-based computer-aided diagnosis systems that assist clinicians in making accurate diagnoses of the diseases in medical images. Contrariwise, the basic operations involved in deep learning models lead to the extraction of a bulky set of features, further taking a long period of training to predict the existence of the disease. For effective execution of these models, feature selection becomes an important task that aids in selecting the most appropriate features, with an aim to increase the classification accuracy. This research presents an optimised deep k-nearest neighbours'-based pipeline model in a bid to amalgamate the feature extraction capability of deep learning models with nature-inspired metaheuristic algorithms, further using k-nearest neighbour algorithm for classification. The proposed model attains an accuracy of 97.67 and 98.05% on two different datasets considered, outperforming Resnet50 and AlexNet deep learning models. Additionally, the experimental results also portray an analysis of five different nature-inspired metaheuristic algorithms, considered for feature selection on the basis of various evaluation parameters.
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Affiliation(s)
- Rahul Hans
- Department of Computer Science and Engineering, DAV University, Jalandhar, Punjab India
| | - Sanjeev Kumar Sharma
- Department of Computer Science and Applications, DAV University, Jalandhar, Punjab India
| | - Uwe Aickelin
- School of Computing and Information Systems, University of Melbourne, Melbourne, Australia
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Mohammadi SS, Nguyen QD. A User-friendly Approach for the Diagnosis of Diabetic Retinopathy Using ChatGPT and Automated Machine Learning. OPHTHALMOLOGY SCIENCE 2024; 4:100495. [PMID: 38690313 PMCID: PMC11059323 DOI: 10.1016/j.xops.2024.100495] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 02/15/2024] [Accepted: 02/15/2024] [Indexed: 05/02/2024]
Abstract
Purpose To assess the capabilities of Chat Generative Pre-trained Transformer (ChatGPT) and Vertex AI in executing code-free preprocessing, training machine learning (ML) models, and analyzing the data. Design Evaluation of diagnostic test or technology. Participants ChatGPT and Vetrex AI as publicly available large language model and ML platform, respectively. Methods ChatGPT was employed to improve the resolution of fundus photography images from the Methods to Evaluate Segmentation and Indexing Techniques in the field of Retinal Ophthalmology (Messidor-2) open-source dataset using the Contrast Limited Adaptive Histogram Equalization (CLAHE) technique by Fiji software. Subsequently, Vertex AI, an automated ML (AutoML) platform, was utilized to develop 2 classification models. The first model served as a binary classifier for detecting the presence of diabetic retinopathy (DR), while the second determined its severity. Finally, ChatGPT was used to provide scripts for R and Python programming languages for data analysis and was also directly employed in analyzing the data in a code-free method. Main Outcome Measures Evaluating the utility of ChatGPT in generating scripts for preprocessing images using Fiji and analyzing data across Python and R and assessing its potential in analyzing data through a code-free method. Investigating the capabilities of Vertex AI to train image classification models for detection of DR and its severity. Results Two ML models were trained using 1740 images from the Messidor-2 database. The first model, designed to detect the severity of DR, achieved an area under the precision-recall curve (AUPRC) of 0.81, with a precision rate of 81.81% and recall of 72.83%. The second model, tailored for the detection of the presence of DR, recorded a precision and recall of 84.48% with an AUPRC of 0.90. Conclusions ChatGPT and Vertex AI have the potential to enable physicians without coding expertise to preprocess images, analyze data, and train ML models. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- S. Saeed Mohammadi
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California
| | - Quan Dong Nguyen
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, California
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Serikbaeva A, Li Y, Ma S, Yi D, Kazlauskas A. Resilience to diabetic retinopathy. Prog Retin Eye Res 2024; 101:101271. [PMID: 38740254 DOI: 10.1016/j.preteyeres.2024.101271] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 05/03/2024] [Accepted: 05/10/2024] [Indexed: 05/16/2024]
Abstract
Chronic elevation of blood glucose at first causes relatively minor changes to the neural and vascular components of the retina. As the duration of hyperglycemia persists, the nature and extent of damage increases and becomes readily detectable. While this second, overt manifestation of diabetic retinopathy (DR) has been studied extensively, what prevents maximal damage from the very start of hyperglycemia remains largely unexplored. Recent studies indicate that diabetes (DM) engages mitochondria-based defense during the retinopathy-resistant phase, and thereby enables the retina to remain healthy in the face of hyperglycemia. Such resilience is transient, and its deterioration results in progressive accumulation of retinal damage. The concepts that co-emerge with these discoveries set the stage for novel intellectual and therapeutic opportunities within the DR field. Identification of biomarkers and mediators of protection from DM-mediated damage will enable development of resilience-based therapies that will indefinitely delay the onset of DR.
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Affiliation(s)
- Anara Serikbaeva
- Department of Physiology and Biophysics, University of Illinois at Chicago, 1905 W Taylor St, Chicago, IL 60612, USA
| | - Yanliang Li
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, 1905 W Taylor St, Chicago, IL 60612, USA
| | - Simon Ma
- Department of Bioengineering, University of Illinois at Chicago, 1905 W Taylor St, Chicago, IL 60612, USA
| | - Darvin Yi
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, 1905 W Taylor St, Chicago, IL 60612, USA; Department of Bioengineering, University of Illinois at Chicago, 1905 W Taylor St, Chicago, IL 60612, USA
| | - Andrius Kazlauskas
- Department of Physiology and Biophysics, University of Illinois at Chicago, 1905 W Taylor St, Chicago, IL 60612, USA; Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, 1905 W Taylor St, Chicago, IL 60612, USA.
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Chen D, Geevarghese A, Lee S, Plovnick C, Elgin C, Zhou R, Oermann E, Aphinyonaphongs Y, Al-Aswad LA. Transparency in Artificial Intelligence Reporting in Ophthalmology-A Scoping Review. OPHTHALMOLOGY SCIENCE 2024; 4:100471. [PMID: 38591048 PMCID: PMC11000111 DOI: 10.1016/j.xops.2024.100471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 11/18/2023] [Accepted: 01/12/2024] [Indexed: 04/10/2024]
Abstract
Topic This scoping review summarizes artificial intelligence (AI) reporting in ophthalmology literature in respect to model development and validation. We characterize the state of transparency in reporting of studies prospectively validating models for disease classification. Clinical Relevance Understanding what elements authors currently describe regarding their AI models may aid in the future standardization of reporting. This review highlights the need for transparency to facilitate the critical appraisal of models prior to clinical implementation, to minimize bias and inappropriate use. Transparent reporting can improve effective and equitable use in clinical settings. Methods Eligible articles (as of January 2022) from PubMed, Embase, Web of Science, and CINAHL were independently screened by 2 reviewers. All observational and clinical trial studies evaluating the performance of an AI model for disease classification of ophthalmic conditions were included. Studies were evaluated for reporting of parameters derived from reporting guidelines (CONSORT-AI, MI-CLAIM) and our previously published editorial on model cards. The reporting of these factors, which included basic model and dataset details (source, demographics), and prospective validation outcomes, were summarized. Results Thirty-seven prospective validation studies were included in the scoping review. Eleven additional associated training and/or retrospective validation studies were included if this information could not be determined from the primary articles. These 37 studies validated 27 unique AI models; multiple studies evaluated the same algorithms (EyeArt, IDx-DR, and Medios AI). Details of model development were variably reported; 18 of 27 models described training dataset annotation and 10 of 27 studies reported training data distribution. Demographic information of training data was rarely reported; 7 of the 27 unique models reported age and gender and only 2 reported race and/or ethnicity. At the level of prospective clinical validation, age and gender of populations was more consistently reported (29 and 28 of 37 studies, respectively), but only 9 studies reported race and/or ethnicity data. Scope of use was difficult to discern for the majority of models. Fifteen studies did not state or imply primary users. Conclusion Our scoping review demonstrates variable reporting of information related to both model development and validation. The intention of our study was not to assess the quality of the factors we examined, but to characterize what information is, and is not, regularly reported. Our results suggest the need for greater transparency in the reporting of information necessary to determine the appropriateness and fairness of these tools prior to clinical use. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Dinah Chen
- Department of Ophthalmology, NYU Langone Health, New York, New York
| | | | - Samuel Lee
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York
| | | | - Cansu Elgin
- Department of Ophthalmology, Istanbul University-Cerrahpasa, Istanbul, Turkey
| | - Raymond Zhou
- Department of Neurosurgery, Vanderbilt School of Medicine, Nashville, Tennessee
| | - Eric Oermann
- Department of Neurosurgery, NYU Grossman School of Medicine, New York, New York
- Department of Neurosurgery, NYU Langone Health, New York, New York
| | - Yindalon Aphinyonaphongs
- Department of Medicine, NYU Langone Health, New York, New York
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
| | - Lama A. Al-Aswad
- Department of Ophthalmology, NYU Langone Health, New York, New York
- Department of Population Health, NYU Grossman School of Medicine, New York, New York
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Yao J, Lim J, Lim GYS, Ong JCL, Ke Y, Tan TF, Tan TE, Vujosevic S, Ting DSW. Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema. EYE AND VISION (LONDON, ENGLAND) 2024; 11:23. [PMID: 38880890 PMCID: PMC11181581 DOI: 10.1186/s40662-024-00389-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 05/09/2024] [Indexed: 06/18/2024]
Abstract
BACKGROUND Diabetic retinopathy (DR) and diabetic macular edema (DME) are major causes of visual impairment that challenge global vision health. New strategies are needed to tackle these growing global health problems, and the integration of artificial intelligence (AI) into ophthalmology has the potential to revolutionize DR and DME management to meet these challenges. MAIN TEXT This review discusses the latest AI-driven methodologies in the context of DR and DME in terms of disease identification, patient-specific disease profiling, and short-term and long-term management. This includes current screening and diagnostic systems and their real-world implementation, lesion detection and analysis, disease progression prediction, and treatment response models. It also highlights the technical advancements that have been made in these areas. Despite these advancements, there are obstacles to the widespread adoption of these technologies in clinical settings, including regulatory and privacy concerns, the need for extensive validation, and integration with existing healthcare systems. We also explore the disparity between the potential of AI models and their actual effectiveness in real-world applications. CONCLUSION AI has the potential to revolutionize the management of DR and DME, offering more efficient and precise tools for healthcare professionals. However, overcoming challenges in deployment, regulatory compliance, and patient privacy is essential for these technologies to realize their full potential. Future research should aim to bridge the gap between technological innovation and clinical application, ensuring AI tools integrate seamlessly into healthcare workflows to enhance patient outcomes.
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Affiliation(s)
- Jie Yao
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Joshua Lim
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Gilbert Yong San Lim
- Duke-NUS Medical School, Singapore, Singapore
- SingHealth AI Health Program, Singapore, Singapore
| | - Jasmine Chiat Ling Ong
- Duke-NUS Medical School, Singapore, Singapore
- Division of Pharmacy, Singapore General Hospital, Singapore, Singapore
| | - Yuhe Ke
- Department of Anesthesiology and Perioperative Science, Singapore General Hospital, Singapore, Singapore
| | - Ting Fang Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Tien-En Tan
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore
- Duke-NUS Medical School, Singapore, Singapore
| | - Stela Vujosevic
- Department of Biomedical, Surgical and Dental Sciences, University of Milan, Milan, Italy
- Eye Clinic, IRCCS MultiMedica, Milan, Italy
| | - Daniel Shu Wei Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, 11 Third Hospital Avenue, Singapore, 168751, Singapore.
- Duke-NUS Medical School, Singapore, Singapore.
- SingHealth AI Health Program, Singapore, Singapore.
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Farahat Z, Zrira N, Souissi N, Bennani Y, Bencherif S, Benamar S, Belmekki M, Ngote MN, Megdiche K. Diabetic retinopathy screening through artificial intelligence algorithms: A systematic review. Surv Ophthalmol 2024:S0039-6257(24)00051-1. [PMID: 38885761 DOI: 10.1016/j.survophthal.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Revised: 05/20/2024] [Accepted: 05/20/2024] [Indexed: 06/20/2024]
Abstract
Diabetic retinopathy (DR) poses a significant challenge in diabetes management, with its progression often asymptomatic until advanced stages. This underscores the urgent need for cost-effective and reliable screening methods. Consequently, the integration of Artificial Intelligence tools presents a promising avenue to address this need effectively. We provide an overview of the current state of the art results and techniques in DR screening using AI, while also identifying gaps in research for future exploration. By synthesizing existing database and pinpointing areas requiring further investigation, this paper seeks to guide the direction of future research in the field of automatic diabetic retinopathy screening. There has been a continuous rise in the number of articles detailing Deep Learning methods designed for the automatic screening of Diabetic Retinopathy especially by the year 2021. Researchers utilized various databases, with a primary focus on the IDRiD dataset. This dataset comprises color fundus images captured at an ophthalmological clinic situated in India. It comprises 516 images that depict various stages of diabetic retinopathy and diabetic macular edema. Each of the chosen papers concentrates on various DR signs. Nevertheless, a significant portion of the authors primarily focused on detecting exudates, which remains insufficient to assess the overall presence of this disease. Various AI methods have been employed to identify DR signs. Among the chosen papers, 4.7% utilized detection methods, 46.5% employed classification techniques, 41.9% relied on segmentation, and 7% opted for a combination of classification and segmentation. Metrics calculated from 80% of the articles employing preprocessing techniques demonstrated the significant benefits of this approach in enhancing results quality. In addition, multiple Deep Learning techniques, starting by classification, detection then segmentation. Researchers used mostly YOLO for detection, ViT for classification and U-Net for segmentation. Another perspective on the evolving landscape of AI models for diabetic retinopathy screening lies in the increasing adoption of Convolutional Neural Networks for classification tasks and U-Net architectures for segmentation purposes;However, there is a growing realization within the research community that these techniques, while powerful individually, can be even more effective when integrated. This integration holds promise for not only diagnosing DR but also accurately classifying its different stages, thereby enabling more tailored treatment strategies. Despite this potential, the development of AI models for DR screening is fraught with challenges. Chief among these is the difficulty in obtaining high-quality, labeled data necessary for training models to perform effectively. This scarcity of data poses significant barriers to achieving robust performance and can hinder progress in developing accurate screening systems. Moreover, managing the complexity of these models, particularly deep neural networks, presents its own set of challenges. Additionally, interpreting the outputs of these models and ensuring their reliability in real-world clinical settings remain ongoing concerns. Furthermore, the iterative process of training and adapting these models to specific datasets can be time-consuming and resource-intensive. These challenges underscore the multifaceted nature of developing effective AI models for DR screening. Addressing these obstacles requires concerted efforts from researchers, clinicians, and technologists to innovate new approaches and overcome existing limitations. By doing so, a full potential of AI may transform DR screening and improve patient outcomes.
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Affiliation(s)
- Zineb Farahat
- LISTD Laboratory, Mines School of Rabat, Rabat 10000, Morocco; Cheikh Zaïd Foundation Medical Simulation Center, Rabat 10000, Morocco.
| | - Nabila Zrira
- LISTD Laboratory, Mines School of Rabat, Rabat 10000, Morocco
| | | | - Yasmine Bennani
- Cheikh Zaïd Ophthalmic Center, Cheikh Zaïd International University Hospital, Rabat 10000, Morocco; Institut Supérieur d'Ingénierie et Technologies de Santé/Faculté de Médecine Abulcasis, Université Internationale Abulcasis des Sciences de la Santé, Rabat 10000, Morocco
| | - Soufiane Bencherif
- Cheikh Zaïd Ophthalmic Center, Cheikh Zaïd International University Hospital, Rabat 10000, Morocco; Institut Supérieur d'Ingénierie et Technologies de Santé/Faculté de Médecine Abulcasis, Université Internationale Abulcasis des Sciences de la Santé, Rabat 10000, Morocco
| | - Safia Benamar
- Cheikh Zaïd Ophthalmic Center, Cheikh Zaïd International University Hospital, Rabat 10000, Morocco; Institut Supérieur d'Ingénierie et Technologies de Santé/Faculté de Médecine Abulcasis, Université Internationale Abulcasis des Sciences de la Santé, Rabat 10000, Morocco
| | - Mohammed Belmekki
- Cheikh Zaïd Ophthalmic Center, Cheikh Zaïd International University Hospital, Rabat 10000, Morocco; Institut Supérieur d'Ingénierie et Technologies de Santé/Faculté de Médecine Abulcasis, Université Internationale Abulcasis des Sciences de la Santé, Rabat 10000, Morocco
| | - Mohamed Nabil Ngote
- LISTD Laboratory, Mines School of Rabat, Rabat 10000, Morocco; Institut Supérieur d'Ingénierie et Technologies de Santé/Faculté de Médecine Abulcasis, Université Internationale Abulcasis des Sciences de la Santé, Rabat 10000, Morocco
| | - Kawtar Megdiche
- Cheikh Zaïd Foundation Medical Simulation Center, Rabat 10000, Morocco
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Tan W, Wei Q, Xing Z, Fu H, Kong H, Lu Y, Yan B, Zhao C. Fairer AI in ophthalmology via implicit fairness learning for mitigating sexism and ageism. Nat Commun 2024; 15:4750. [PMID: 38834557 DOI: 10.1038/s41467-024-48972-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 05/21/2024] [Indexed: 06/06/2024] Open
Abstract
The transformative role of artificial intelligence (AI) in various fields highlights the need for it to be both accurate and fair. Biased medical AI systems pose significant potential risks to achieving fair and equitable healthcare. Here, we show an implicit fairness learning approach to build a fairer ophthalmology AI (called FairerOPTH) that mitigates sex (biological attribute) and age biases in AI diagnosis of eye diseases. Specifically, FairerOPTH incorporates the causal relationship between fundus features and eye diseases, which is relatively independent of sensitive attributes such as race, sex, and age. We demonstrate on a large and diverse collected dataset that FairerOPTH significantly outperforms several state-of-the-art approaches in terms of diagnostic accuracy and fairness for 38 eye diseases in ultra-widefield imaging and 16 eye diseases in narrow-angle imaging. This work demonstrates the significant potential of implicit fairness learning in promoting equitable treatment for patients regardless of their sex or age.
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Affiliation(s)
- Weimin Tan
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
| | - Qiaoling Wei
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Zhen Xing
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
| | - Hao Fu
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
| | - Hongyu Kong
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China
| | - Yi Lu
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
| | - Bo Yan
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China.
| | - Chen Zhao
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, China.
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Wang X, Li H, Zheng H, Sun G, Wang W, Yi Z, Xu A, He L, Wang H, Jia W, Li Z, Li C, Ye M, Du B, Chen C. Automatic Detection of 30 Fundus Diseases Using Ultra-Widefield Fluorescein Angiography with Deep Experts Aggregation. Ophthalmol Ther 2024; 13:1125-1144. [PMID: 38416330 DOI: 10.1007/s40123-024-00900-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Accepted: 01/26/2024] [Indexed: 02/29/2024] Open
Abstract
INTRODUCTION Inaccurate, untimely diagnoses of fundus diseases leads to vision-threatening complications and even blindness. We built a deep learning platform (DLP) for automatic detection of 30 fundus diseases using ultra-widefield fluorescein angiography (UWFFA) with deep experts aggregation. METHODS This retrospective and cross-sectional database study included a total of 61,609 UWFFA images dating from 2016 to 2021, involving more than 3364 subjects in multiple centers across China. All subjects were divided into 30 different groups. The state-of-the-art convolutional neural network architecture, ConvNeXt, was chosen as the backbone to train and test the receiver operating characteristic curve (ROC) of the proposed system on test data and external test date. We compared the classification performance of the proposed system with that of ophthalmologists, including two retinal specialists. RESULTS We built a DLP to analyze UWFFA, which can detect up to 30 fundus diseases, with a frequency-weighted average area under the receiver operating characteristic curve (AUC) of 0.940 in the primary test dataset and 0.954 in the external multi-hospital test dataset. The tool shows comparable accuracy with retina specialists in diagnosis and evaluation. CONCLUSIONS This is the first study on a large-scale UWFFA dataset for multi-retina disease classification. We believe that our UWFFA DLP advances the diagnosis by artificial intelligence (AI) in various retinal diseases and would contribute to labor-saving and precision medicine especially in remote areas.
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Affiliation(s)
- Xiaoling Wang
- Eye Center, Renmin Hospital of Wuhan University, No. 9 ZhangZhiDong Street, Wuhan, 430060, Hubei, China
| | - He Li
- National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, 430072, Hubei, China
| | - Hongmei Zheng
- Eye Center, Renmin Hospital of Wuhan University, No. 9 ZhangZhiDong Street, Wuhan, 430060, Hubei, China
| | - Gongpeng Sun
- Eye Center, Renmin Hospital of Wuhan University, No. 9 ZhangZhiDong Street, Wuhan, 430060, Hubei, China
| | - Wenyu Wang
- Eye Center, Renmin Hospital of Wuhan University, No. 9 ZhangZhiDong Street, Wuhan, 430060, Hubei, China
| | - Zuohuizi Yi
- Eye Center, Renmin Hospital of Wuhan University, No. 9 ZhangZhiDong Street, Wuhan, 430060, Hubei, China
| | - A'min Xu
- Eye Center, Renmin Hospital of Wuhan University, No. 9 ZhangZhiDong Street, Wuhan, 430060, Hubei, China
| | - Lu He
- Eye Center, Renmin Hospital of Wuhan University, No. 9 ZhangZhiDong Street, Wuhan, 430060, Hubei, China
| | - Haiyan Wang
- Shaanxi Eye Hospital, Xi'an People's Hospital (Xi'an Fourth Hospital), No. 21, Jiefang Road, Xi'an, 710004, Shaanxi, China
| | - Wei Jia
- Shaanxi Eye Hospital, Xi'an People's Hospital (Xi'an Fourth Hospital), No. 21, Jiefang Road, Xi'an, 710004, Shaanxi, China
| | - Zhiqing Li
- Tianjin Medical University Eye Hospital, No. 251, Fukang Road, Nankai District, Tianjin, 300384, China
| | - Chang Li
- Tianjin Medical University Eye Hospital, No. 251, Fukang Road, Nankai District, Tianjin, 300384, China
| | - Mang Ye
- National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, 430072, Hubei, China.
| | - Bo Du
- National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, 430072, Hubei, China.
| | - Changzheng Chen
- Eye Center, Renmin Hospital of Wuhan University, No. 9 ZhangZhiDong Street, Wuhan, 430060, Hubei, China.
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Musetti D, Cutolo CA, Bonetto M, Giacomini M, Maggi D, Viviani GL, Gandin I, Traverso CE, Nicolò M. Autonomous artificial intelligence versus teleophthalmology for diabetic retinopathy. Eur J Ophthalmol 2024:11206721241248856. [PMID: 38656241 DOI: 10.1177/11206721241248856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/26/2024]
Abstract
Purpose: To assess the role of artificial intelligence (AI) based automated software for detection of Diabetic Retinopathy (DR) compared with the evaluation of digital retinography by two double masked retina specialists. Methods: Two-hundred one patients (mean age 65 ± 13 years) with type 1 diabetes mellitus or type 2 diabetes mellitus were included. All patients were undergoing a retinography and spectral domain optical coherence tomography (SD-OCT, DRI 3D OCT-2000, Topcon) of the macula. The retinal photographs were graded using two validated AI DR screening software (Eye Art TM and IDx-DR) designed to identify more than mild DR. Results: Retinal images of 201 patients were graded. DR (more than mild DR) was detected by the ophthalmologists in 38 (18.9%) patients and by the AI-algorithms in 36 patients (with 30 eyes diagnosed by both algorithms). Ungradable patients by the AI software were 13 (6.5%) and 16 (8%) for the Eye Art and IDx-DR, respectively. Both AI software strategies showed a high sensitivity and specificity for detecting any more than mild DR without showing any statistically significant difference between them. Conclusions: The comparison between the diagnosis provided by artificial intelligence based automated software and the reference clinical diagnosis showed that they can work at a level of sensitivity that is similar to that achieved by experts.
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Affiliation(s)
- Donatella Musetti
- Clinica Oculistica DiNOGMI, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | - Carlo Alberto Cutolo
- Clinica Oculistica DiNOGMI, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | | | | | - Davide Maggi
- Clinica Diabetologica, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | - Giorgio Luciano Viviani
- Clinica Diabetologica, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | - Ilaria Gandin
- Sciences, Biostatistic Unit, University of Trieste, Italy
| | - Carlo Enrico Traverso
- Clinica Oculistica DiNOGMI, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
| | - Massimo Nicolò
- Clinica Oculistica DiNOGMI, Università di Genova, Ospedale Policlinico San Martino IRCCS, Genova, Italy
- Fondazione per la Macula onlus, Genova, Italy
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Sharma S, Belenje A, Takkar B, Narula R, Rathi VM, Tyagi M, Rani PK, Narayanan R, Kaur I. Tear Protein Markers for Diabetic Retinopathy and Diabetic Macular Edema - Towards an Early Diagnosis and Better Prognosis. Semin Ophthalmol 2024:1-11. [PMID: 38643349 DOI: 10.1080/08820538.2024.2342266] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 04/01/2024] [Indexed: 04/22/2024]
Abstract
BACKGROUND Diabetic eye disease is a highly prevalent and sight-threatening disorder. It is a disease of neuro-vascular unit of the retina, if left untreated can cause blindness. Therapeutic approaches followed for its treatment can only restrict the progression of the disease with highly variable results. There is no known biomarker for an early diagonsis of this disease, therefore by the time it is detected it goes beyond repair. This creates a massive demand for development of such biomarkers that help detect disease in its earlier stages. METHODS PUBMED (https://pubmed.ncbi.nlm.nih.gov/) was searched for articles relevant to the topic published till November 2023. The search was made using keywords such as Diabetic Retinopathy, inflammation, tear, biomarker, proteomics etc. The studies providing relevant information to prove the importance of biomarker discovery were chosen. After compiling the data, the manuscript writing was planned under relevant headings and sub-headings. RESULTS The review provides a comprehensive overview of all the tear protein biomarker studies in the field of DR and DME. Briefly, their potential in other diseases is also elucidated. While there are many studies pertaining to DR biomarkers, the identified markers lack validations which has restricted their usage in clinics. In case of DME, there was no such study towards biomarker discovery for its diagnosis and prognosis. CONCLUSIONS The review highlights major studies and their lacunae in the field of biomarkers discovery for DR and DME.
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Affiliation(s)
- Sarmeela Sharma
- Brien Holden Eye Research Centre, L V Prasad Eye Institute, Hyderabad, India
- Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India
| | - Akash Belenje
- Anant Bajaj Retina Institute, LV Prasad Eye Institue, Hyderabad, India
| | - Brijesh Takkar
- Anant Bajaj Retina Institute, LV Prasad Eye Institue, Hyderabad, India
| | - Ritesh Narula
- Anant Bajaj Retina Institute, LV Prasad Eye Institue, Hyderabad, India
| | | | - Mudit Tyagi
- Anant Bajaj Retina Institute, LV Prasad Eye Institue, Hyderabad, India
| | | | - Raja Narayanan
- Anant Bajaj Retina Institute, LV Prasad Eye Institue, Hyderabad, India
| | - Inderjeet Kaur
- Brien Holden Eye Research Centre, L V Prasad Eye Institute, Hyderabad, India
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11
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Wang XN, Li S, Cai X, Li T, Long D, Wu Q. Imaging Artifacts and Quality Evaluation with Ultrawide-Field Swept-Source OCTA in Diabetic Retinopathy. Curr Eye Res 2024; 49:410-416. [PMID: 38116796 DOI: 10.1080/02713683.2023.2296362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023]
Abstract
PURPOSE To evaluate the prevalence and types of artifacts in ultrawide-field swept-source optical coherence tomography angiography (SS-OCTA) scans of diabetic retinopathy (DR) patients. METHODS This study was a prospective, observational study conducted from May 2022 to October 2022. Participants comprised individuals with proliferative diabetic retinopathy (PDR), nonproliferative diabetic retinopathy (NPDR), no diabetic retinopathy, and healthy controls. SS-OCTA imaging was performed, and a 5-scan composite with a larger field of view (23.5 mm × 17.5 mm) was captured using built-in software. Two experienced ophthalmologists analyzed the images independently, and the image quality and artifact prevalence were recorded and analyzed. RESULTS The study included 70 eyes (16 with PDR, 24 with NPDR, 12 eyes of diabetic patients without DR, and 18 healthy eyes) in 70 subjects. Imaging artifacts were observed in a high percentage of eyes, with 98.57% of eyes presenting at least one type of artifact. A significant proportion of eyes (58.57%) exhibited a severe degree of artifacts. The most prevalent artifacts were loss of signal in 63 eyes (90%) and displacement artifact and masking artifact in 43 eyes (61.4%). Patients with more severe stages of DR had higher artifact scores (p < 0.05). Multivariate regression analysis indicated that DR severity was the most important factor influencing artifact scores (p < 0.05). CONCLUSIONS In OCTA photos, various artifacts arise at different frequencies. It is crucial to qualitatively evaluate the images to ensure their quality. The results demonstrate that DR severity has a significant correlation with artifact scores.
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Affiliation(s)
- Xiang-Ning Wang
- Department of Ophthalmology, Shanghai Sixth People's Hospital, Shanghai, China
| | - Shuting Li
- Department of Ophthalmology, The First People's Hospital of Changzhou, Changzhou, China
| | - Xuan Cai
- Department of Ophthalmology, Shanghai Sixth People's Hospital, Shanghai, China
| | - Tingting Li
- Department of Ophthalmology, Shanghai Sixth People's Hospital, Shanghai, China
| | - Da Long
- Department of Ophthalmology, Shanghai Sixth People's Hospital, Shanghai, China
| | - Qiang Wu
- Department of Ophthalmology, Shanghai Sixth People's Hospital, Shanghai, China
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12
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Hai Z, Zou B, Xiao X, Peng Q, Yan J, Zhang W, Yue K. A novel approach for intelligent diagnosis and grading of diabetic retinopathy. Comput Biol Med 2024; 172:108246. [PMID: 38471350 DOI: 10.1016/j.compbiomed.2024.108246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Revised: 03/05/2024] [Accepted: 03/05/2024] [Indexed: 03/14/2024]
Abstract
Diabetic retinopathy (DR) is a severe ocular complication of diabetes that can lead to vision damage and even blindness. Currently, traditional deep convolutional neural networks (CNNs) used for DR grading tasks face two primary challenges: (1) insensitivity to minority classes due to imbalanced data distribution, and (2) neglecting the relationship between the left and right eyes by utilizing the fundus image of only one eye for training without differentiating between them. To tackle these challenges, we proposed the DRGCNN (DR Grading CNN) model. To solve the problem caused by imbalanced data distribution, our model adopts a more balanced strategy by allocating an equal number of channels to feature maps representing various DR categories. Furthermore, we introduce a CAM-EfficientNetV2-M encoder dedicated to encoding input retinal fundus images for feature vector generation. The number of parameters of our encoder is 52.88 M, which is less than RegNet_y_16gf (80.57 M) and EfficientNetB7 (63.79 M), but the corresponding kappa value is higher. Additionally, in order to take advantage of the binocular relationship, we input fundus retinal images from both eyes of the patient into the network for features fusion during the training phase. We achieved a kappa value of 86.62% on the EyePACS dataset and 86.16% on the Messidor-2 dataset. Experimental results on these representative datasets for diabetic retinopathy (DR) demonstrate the exceptional performance of our DRGCNN model, establishing it as a highly competitive intelligent classification model in the field of DR. The code is available for use at https://github.com/Fat-Hai/DRGCNN.
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Affiliation(s)
- Zeru Hai
- School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan Province, 410208, China
| | - Beiji Zou
- School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan Province, 410208, China; School of Computer Science and Engineering, Central South University, Changsha, Hunan Province, 410083, China
| | - Xiaoxia Xiao
- School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan Province, 410208, China.
| | - Qinghua Peng
- School of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan Province, 410208, China
| | - Junfeng Yan
- School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan Province, 410208, China
| | - Wensheng Zhang
- School of Informatics, Hunan University of Chinese Medicine, Changsha, Hunan Province, 410208, China; University of Chinese Academy of Sciences (UCAS), Beijing, 100049, China; Research Center of Precision Sensing and Control, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
| | - Kejuan Yue
- School of Computer Science, Hunan First Normal University, Changsha, Hunan Province, 410205, China
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13
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Yim D, Khuntia J, Parameswaran V, Meyers A. Preliminary Evidence of the Use of Generative AI in Health Care Clinical Services: Systematic Narrative Review. JMIR Med Inform 2024; 12:e52073. [PMID: 38506918 PMCID: PMC10993141 DOI: 10.2196/52073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2023] [Revised: 10/12/2023] [Accepted: 01/30/2024] [Indexed: 03/21/2024] Open
Abstract
BACKGROUND Generative artificial intelligence tools and applications (GenAI) are being increasingly used in health care. Physicians, specialists, and other providers have started primarily using GenAI as an aid or tool to gather knowledge, provide information, train, or generate suggestive dialogue between physicians and patients or between physicians and patients' families or friends. However, unless the use of GenAI is oriented to be helpful in clinical service encounters that can improve the accuracy of diagnosis, treatment, and patient outcomes, the expected potential will not be achieved. As adoption continues, it is essential to validate the effectiveness of the infusion of GenAI as an intelligent technology in service encounters to understand the gap in actual clinical service use of GenAI. OBJECTIVE This study synthesizes preliminary evidence on how GenAI assists, guides, and automates clinical service rendering and encounters in health care The review scope was limited to articles published in peer-reviewed medical journals. METHODS We screened and selected 0.38% (161/42,459) of articles published between January 1, 2020, and May 31, 2023, identified from PubMed. We followed the protocols outlined in the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to select highly relevant studies with at least 1 element on clinical use, evaluation, and validation to provide evidence of GenAI use in clinical services. The articles were classified based on their relevance to clinical service functions or activities using the descriptive and analytical information presented in the articles. RESULTS Of 161 articles, 141 (87.6%) reported using GenAI to assist services through knowledge access, collation, and filtering. GenAI was used for disease detection (19/161, 11.8%), diagnosis (14/161, 8.7%), and screening processes (12/161, 7.5%) in the areas of radiology (17/161, 10.6%), cardiology (12/161, 7.5%), gastrointestinal medicine (4/161, 2.5%), and diabetes (6/161, 3.7%). The literature synthesis in this study suggests that GenAI is mainly used for diagnostic processes, improvement of diagnosis accuracy, and screening and diagnostic purposes using knowledge access. Although this solves the problem of knowledge access and may improve diagnostic accuracy, it is oriented toward higher value creation in health care. CONCLUSIONS GenAI informs rather than assisting or automating clinical service functions in health care. There is potential in clinical service, but it has yet to be actualized for GenAI. More clinical service-level evidence that GenAI is used to streamline some functions or provides more automated help than only information retrieval is needed. To transform health care as purported, more studies related to GenAI applications must automate and guide human-performed services and keep up with the optimism that forward-thinking health care organizations will take advantage of GenAI.
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Affiliation(s)
- Dobin Yim
- Loyola University, Maryland, MD, United States
| | - Jiban Khuntia
- University of Colorado Denver, Denver, CO, United States
| | | | - Arlen Meyers
- University of Colorado Denver, Denver, CO, United States
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14
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Qian B, Chen H, Wang X, Guan Z, Li T, Jin Y, Wu Y, Wen Y, Che H, Kwon G, Kim J, Choi S, Shin S, Krause F, Unterdechler M, Hou J, Feng R, Li Y, El Habib Daho M, Yang D, Wu Q, Zhang P, Yang X, Cai Y, Tan GSW, Cheung CY, Jia W, Li H, Tham YC, Wong TY, Sheng B. DRAC 2022: A public benchmark for diabetic retinopathy analysis on ultra-wide optical coherence tomography angiography images. PATTERNS (NEW YORK, N.Y.) 2024; 5:100929. [PMID: 38487802 PMCID: PMC10935505 DOI: 10.1016/j.patter.2024.100929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/25/2023] [Revised: 12/09/2023] [Accepted: 01/15/2024] [Indexed: 03/17/2024]
Abstract
We described a challenge named "DRAC - Diabetic Retinopathy Analysis Challenge" in conjunction with the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2022). Within this challenge, we provided the DRAC datset, an ultra-wide optical coherence tomography angiography (UW-OCTA) dataset (1,103 images), addressing three primary clinical tasks: diabetic retinopathy (DR) lesion segmentation, image quality assessment, and DR grading. The scientific community responded positively to the challenge, with 11, 12, and 13 teams submitting different solutions for these three tasks, respectively. This paper presents a concise summary and analysis of the top-performing solutions and results across all challenge tasks. These solutions could provide practical guidance for developing accurate classification and segmentation models for image quality assessment and DR diagnosis using UW-OCTA images, potentially improving the diagnostic capabilities of healthcare professionals. The dataset has been released to support the development of computer-aided diagnostic systems for DR evaluation.
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Affiliation(s)
- Bo Qian
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Hao Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China
| | - Xiangning Wang
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
- Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Zhouyu Guan
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Tingyao Li
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yixiao Jin
- Tsinghua Medicine, Tsinghua University, Beijing 100084, China
| | - Yilan Wu
- Tsinghua Medicine, Tsinghua University, Beijing 100084, China
| | - Yang Wen
- School of Electronic and Information Engineering, Shenzhen University, Shenzhen 518060, China
| | - Haoxuan Che
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong 999077, China
| | | | | | - Sungjin Choi
- AI/DX Convergence Business Group, KT, Seongnam 13606, Korea
| | - Seoyoung Shin
- AI/DX Convergence Business Group, KT, Seongnam 13606, Korea
| | - Felix Krause
- Johannes Kepler University Linz, Linz 4040, Austria
| | | | - Junlin Hou
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
| | - Rui Feng
- School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai 200433, China
- Academy for Engineering and Technology, Fudan University, Shanghai 200433, China
| | - Yihao Li
- LaTIM UMR 1101, INSERM, 29609 Brest, France
- University of Western Brittany, 29238 Brest, France
| | - Mostafa El Habib Daho
- LaTIM UMR 1101, INSERM, 29609 Brest, France
- University of Western Brittany, 29238 Brest, France
| | - Dawei Yang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - Qiang Wu
- Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200233, China
| | - Ping Zhang
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA
- Translational Data Analytics Institute, The Ohio State University, Columbus, OH 43210, USA
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yiyu Cai
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
| | - Gavin Siew Wei Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
| | - Carol Y. Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong 999077, China
| | - Weiping Jia
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Huating Li
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Yih Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- Centre for Innovation and Precision Eye Health; and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore 119228, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore 169857, Singapore
| | - Tien Yin Wong
- Tsinghua Medicine, Tsinghua University, Beijing 100084, China
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- School of Clinical Medicine, Beijing Tsinghua Changgung Hospital, Beijing 102218, China
| | - Bin Sheng
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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15
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Liu Y, Xie H, Zhao X, Tang J, Yu Z, Wu Z, Tian R, Chen Y, Chen M, Ntentakis DP, Du Y, Chen T, Hu Y, Zhang S, Lei B, Zhang G. Automated detection of nine infantile fundus diseases and conditions in retinal images using a deep learning system. EPMA J 2024; 15:39-51. [PMID: 38463622 PMCID: PMC10923762 DOI: 10.1007/s13167-024-00350-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 01/21/2024] [Indexed: 03/12/2024]
Abstract
Purpose We developed an Infant Retinal Intelligent Diagnosis System (IRIDS), an automated system to aid early diagnosis and monitoring of infantile fundus diseases and health conditions to satisfy urgent needs of ophthalmologists. Methods We developed IRIDS by combining convolutional neural networks and transformer structures, using a dataset of 7697 retinal images (1089 infants) from four hospitals. It identifies nine fundus diseases and conditions, namely, retinopathy of prematurity (ROP) (mild ROP, moderate ROP, and severe ROP), retinoblastoma (RB), retinitis pigmentosa (RP), Coats disease, coloboma of the choroid, congenital retinal fold (CRF), and normal. IRIDS also includes depth attention modules, ResNet-18 (Res-18), and Multi-Axis Vision Transformer (MaxViT). Performance was compared to that of ophthalmologists using 450 retinal images. The IRIDS employed a five-fold cross-validation approach to generate the classification results. Results Several baseline models achieved the following metrics: accuracy, precision, recall, F1-score (F1), kappa, and area under the receiver operating characteristic curve (AUC) with best values of 94.62% (95% CI, 94.34%-94.90%), 94.07% (95% CI, 93.32%-94.82%), 90.56% (95% CI, 88.64%-92.48%), 92.34% (95% CI, 91.87%-92.81%), 91.15% (95% CI, 90.37%-91.93%), and 99.08% (95% CI, 99.07%-99.09%), respectively. In comparison, IRIDS showed promising results compared to ophthalmologists, demonstrating an average accuracy, precision, recall, F1, kappa, and AUC of 96.45% (95% CI, 96.37%-96.53%), 95.86% (95% CI, 94.56%-97.16%), 94.37% (95% CI, 93.95%-94.79%), 95.03% (95% CI, 94.45%-95.61%), 94.43% (95% CI, 93.96%-94.90%), and 99.51% (95% CI, 99.51%-99.51%), respectively, in multi-label classification on the test dataset, utilizing the Res-18 and MaxViT models. These results suggest that, particularly in terms of AUC, IRIDS achieved performance that warrants further investigation for the detection of retinal abnormalities. Conclusions IRIDS identifies nine infantile fundus diseases and conditions accurately. It may aid non-ophthalmologist personnel in underserved areas in infantile fundus disease screening. Thus, preventing severe complications. The IRIDS serves as an example of artificial intelligence integration into ophthalmology to achieve better outcomes in predictive, preventive, and personalized medicine (PPPM / 3PM) in the treatment of infantile fundus diseases. Supplementary Information The online version contains supplementary material available at 10.1007/s13167-024-00350-y.
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Affiliation(s)
- Yaling Liu
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Hai Xie
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Xinyu Zhao
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Jiannan Tang
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Zhen Yu
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Zhenquan Wu
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Ruyin Tian
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Yi Chen
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
- Guizhou Medical University, Guiyang, Guizhou China
| | - Miaohong Chen
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
- Guizhou Medical University, Guiyang, Guizhou China
| | - Dimitrios P. Ntentakis
- Retina Service, Ines and Fred Yeatts Retina Research Laboratory, Angiogenesis Laboratory, Department of Ophthalmology, Massachusetts Eye and Ear, Harvard Medical School, Boston, MA USA
| | - Yueshanyi Du
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Tingyi Chen
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
- Guizhou Medical University, Guiyang, Guizhou China
| | - Yarou Hu
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
| | - Sifan Zhang
- Guizhou Medical University, Guiyang, Guizhou China
- Southern University of Science and Technology School of Medicine, Shenzhen, China
| | - Baiying Lei
- National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, China
| | - Guoming Zhang
- Shenzhen Eye Hospital, Shenzhen Eye Institute, Jinan University, Shenzhen, 518040 China
- Guizhou Medical University, Guiyang, Guizhou China
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16
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Tran C, Shen K, Liu K, Ashok A, Ramirez-Zamora A, Chen J, Li Y, Fang R. Deep learning predicts prevalent and incident Parkinson's disease from UK Biobank fundus imaging. Sci Rep 2024; 14:3637. [PMID: 38351326 PMCID: PMC10864361 DOI: 10.1038/s41598-024-54251-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Accepted: 02/10/2024] [Indexed: 02/16/2024] Open
Abstract
Parkinson's disease is the world's fastest-growing neurological disorder. Research to elucidate the mechanisms of Parkinson's disease and automate diagnostics would greatly improve the treatment of patients with Parkinson's disease. Current diagnostic methods are expensive and have limited availability. Considering the insidious and preclinical onset and progression of the disease, a desirable screening should be diagnostically accurate even before the onset of symptoms to allow medical interventions. We highlight retinal fundus imaging, often termed a window to the brain, as a diagnostic screening modality for Parkinson's disease. We conducted a systematic evaluation of conventional machine learning and deep learning techniques to classify Parkinson's disease from UK Biobank fundus imaging. Our results suggest Parkinson's disease individuals can be differentiated from age and gender-matched healthy subjects with 68% accuracy. This accuracy is maintained when predicting either prevalent or incident Parkinson's disease. Explainability and trustworthiness are enhanced by visual attribution maps of localized biomarkers and quantified metrics of model robustness to data perturbations.
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Affiliation(s)
- Charlie Tran
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Kai Shen
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA
| | - Kang Liu
- Department of Physics, University of Florida, Gainesville, FL, 32661, USA
| | - Akshay Ashok
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, 32611, USA
| | | | - Jinghua Chen
- Department of Ophthalmology, University of Florida, Gainesville, FL, 32661, USA
| | - Yulin Li
- Department of Biostatistics, University of Florida, Gainesville, FL, 32661, USA
| | - Ruogu Fang
- Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL, 32611, USA.
- J. Crayton Pruitt Family Department of Biomedical Engineering, Herbert Wertheim College of Engineering, University of Florida, 1275 Center Drive, PO Box 116131, Gainesville, FL, 32611-6131, USA.
- Center for Cognitive Aging and Memory, University of Florida, Gainesville, FL, 32611, USA.
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17
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Fu Y, Wei Y, Chen S, Chen C, Zhou R, Li H, Qiu M, Xie J, Huang D. UC-stack: a deep learning computer automatic detection system for diabetic retinopathy classification. Phys Med Biol 2024; 69:045021. [PMID: 38271723 DOI: 10.1088/1361-6560/ad22a1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2023] [Accepted: 01/25/2024] [Indexed: 01/27/2024]
Abstract
Object. The existing diagnostic paradigm for diabetic retinopathy (DR) greatly relies on subjective assessments by medical practitioners utilizing optical imaging, introducing susceptibility to individual interpretation. This work presents a novel system for the early detection and grading of DR, providing an automated alternative to the manual examination.Approach. First, we use advanced image preprocessing techniques, specifically contrast-limited adaptive histogram equalization and Gaussian filtering, with the goal of enhancing image quality and module learning capabilities. Second, a deep learning-based automatic detection system is developed. The system consists of a feature segmentation module, a deep learning feature extraction module, and an ensemble classification module. The feature segmentation module accomplishes vascular segmentation, the deep learning feature extraction module realizes the global feature and local feature extraction of retinopathy images, and the ensemble module performs the diagnosis and classification of DR for the extracted features. Lastly, nine performance evaluation metrics are applied to assess the quality of the model's performance.Main results. Extensive experiments are conducted on four retinal image databases (APTOS 2019, Messidor, DDR, and EyePACS). The proposed method demonstrates promising performance in the binary and multi-classification tasks for DR, evaluated through nine indicators, including AUC and quadratic weighted Kappa score. The system shows the best performance in the comparison of three segmentation methods, two convolutional neural network architecture models, four Swin Transformer structures, and the latest literature methods.Significance. In contrast to existing methods, our system demonstrates superior performance across multiple indicators, enabling accurate screening of DR and providing valuable support to clinicians in the diagnostic process. Our automated approach minimizes the reliance on subjective assessments, contributing to more consistent and reliable DR evaluations.
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Affiliation(s)
- Yong Fu
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Yuekun Wei
- School of Information and Management, Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Siying Chen
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Caihong Chen
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Rong Zhou
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Hongjun Li
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Mochan Qiu
- School of Basic Medical Sciences, Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Jin Xie
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
| | - Daizheng Huang
- The Life Sciences Research Institute of Guangxi Medical University, Nanning, 530021, People's Republic of China
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Brennan IG, Kelly SR, McBride E, Garrahy D, Acheson R, Harmon J, McMahon S, Keegan DJ, Kavanagh H, O’Toole L. Addressing Technical Failures in a Diabetic Retinopathy Screening Program. Clin Ophthalmol 2024; 18:431-440. [PMID: 38356695 PMCID: PMC10864767 DOI: 10.2147/opth.s442414] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Accepted: 01/18/2024] [Indexed: 02/16/2024] Open
Abstract
Purpose Diabetic retinopathy (DR) is a preventable cause of blindness detectable through screening using retinal digital photography. The Irish National Diabetic Retina Screening (DRS) programme, Diabetic RetinaScreen, provides free screening services to patients with diabetes from aged 12 years and older. A technical failure (TF) occurs when digital retinal imaging is ungradable, resulting in delays in the diagnosis and treatment of sight-threatening disease. Despite their impact, the causes of TFs, and indeed the utility of interventions to prevent them, have not been extensively examined. Aim Primary analysis aimed to identify factors associated with TF. Secondary analysis examined a subset of cases, assessing patient data from five time points between 2019 and 2021 to identify photographer/patient factors associated with TF. Methods Patient data from the DRS database for one provider were extracted for analysis between 2018 and 2022. Information on patient demographics, screening results, and other factors previously associated with TF were analyzed. Primary analysis involved using mixed-effects logistic regression models with nested patient-eye random effects. Secondary analysis reviewed a subset of cases in detail, checking for causes of TF. Results The primary analysis included a total of 366,528 appointments from 104,407 patients over 5 years. Most patients had Type 2 diabetes (89.2%), and the overall TF rate was 4.9%. Diabetes type and duration, dilate pupil status, and the presence of lens artefacts on the camera were significantly associated with TF. The Secondary analysis identified the primary cause of TF was found to be optically dense cataracts, accounting for over half of the TFs. Conclusion This study provides insight into the causes of TF within the Irish DRS program, highlighting cataracts as the primary contributing factor. The identification of patient-level factors associated with TF facilitates appropriate interventions that can be put in place to improve patient outcomes and minimize delays in treatment and diagnosis.
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Affiliation(s)
- Ian Gerard Brennan
- Diabetic RetinaScreen, National Screening Service, Health Service Executive, Dublin, Ireland
| | - Stephen R Kelly
- Diabetic RetinaScreen, National Screening Service, Health Service Executive, Dublin, Ireland
| | - Edel McBride
- Diabetic Retinal Screening Service, NEC Care, Cork City, Co. Cork, Ireland
| | - Darragh Garrahy
- Diabetic RetinaScreen, National Screening Service, Health Service Executive, Dublin, Ireland
| | - Robert Acheson
- Diabetic Retinal Screening Service, NEC Care, Cork City, Co. Cork, Ireland
| | - Joanne Harmon
- Diabetic Retinal Screening Service, NEC Care, Cork City, Co. Cork, Ireland
| | - Shane McMahon
- Diabetic Retinal Screening Service, NEC Care, Cork City, Co. Cork, Ireland
| | - David J Keegan
- Diabetic RetinaScreen, National Screening Service, Health Service Executive, Dublin, Ireland
| | - Helen Kavanagh
- Diabetic RetinaScreen, National Screening Service, Health Service Executive, Dublin, Ireland
| | - Louise O’Toole
- Diabetic Retinal Screening Service, NEC Care, Cork City, Co. Cork, Ireland
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19
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Dai L, Sheng B, Chen T, Wu Q, Liu R, Cai C, Wu L, Yang D, Hamzah H, Liu Y, Wang X, Guan Z, Yu S, Li T, Tang Z, Ran A, Che H, Chen H, Zheng Y, Shu J, Huang S, Wu C, Lin S, Liu D, Li J, Wang Z, Meng Z, Shen J, Hou X, Deng C, Ruan L, Lu F, Chee M, Quek TC, Srinivasan R, Raman R, Sun X, Wang YX, Wu J, Jin H, Dai R, Shen D, Yang X, Guo M, Zhang C, Cheung CY, Tan GSW, Tham YC, Cheng CY, Li H, Wong TY, Jia W. A deep learning system for predicting time to progression of diabetic retinopathy. Nat Med 2024; 30:584-594. [PMID: 38177850 PMCID: PMC10878973 DOI: 10.1038/s41591-023-02702-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Accepted: 11/10/2023] [Indexed: 01/06/2024]
Abstract
Diabetic retinopathy (DR) is the leading cause of preventable blindness worldwide. The risk of DR progression is highly variable among different individuals, making it difficult to predict risk and personalize screening intervals. We developed and validated a deep learning system (DeepDR Plus) to predict time to DR progression within 5 years solely from fundus images. First, we used 717,308 fundus images from 179,327 participants with diabetes to pretrain the system. Subsequently, we trained and validated the system with a multiethnic dataset comprising 118,868 images from 29,868 participants with diabetes. For predicting time to DR progression, the system achieved concordance indexes of 0.754-0.846 and integrated Brier scores of 0.153-0.241 for all times up to 5 years. Furthermore, we validated the system in real-world cohorts of participants with diabetes. The integration with clinical workflow could potentially extend the mean screening interval from 12 months to 31.97 months, and the percentage of participants recommended to be screened at 1-5 years was 30.62%, 20.00%, 19.63%, 11.85% and 17.89%, respectively, while delayed detection of progression to vision-threatening DR was 0.18%. Altogether, the DeepDR Plus system could predict individualized risk and time to DR progression over 5 years, potentially allowing personalized screening intervals.
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Grants
- the National Key Research and Development Program of China (2022YFA1004804), the Shanghai Municipal Key Clinical Specialty, Shanghai Research Center for Endocrine and Metabolic Diseases (2022ZZ01002), and the Chinese Academy of Engineering (2022-XY-08)
- the General Program of NSFC (62272298), the National Key Research and Development Program of China (2022YFC2407000), the Interdisciplinary Program of Shanghai Jiao Tong University (YG2023LC11 and YG2022ZD007), National Natural Science Foundation of China (62272298 and 62077037), the College-level Project Fund of Shanghai Jiao Tong University Affiliated Sixth People’s Hospital (ynlc201909), and the Medical-industrial Cross-fund of Shanghai Jiao Tong University (YG2022QN089)
- the Clinical Special Program of Shanghai Municipal Health Commission (20224044) and Three-year action plan to strengthen the construction of public health system in Shanghai (GWVI-11.1-28)
- the National Natural Science Foundation of China (82100879)
- the National Key Research and Development Program of China (2022YFA1004804), Excellent Young Scientists Fund of NSFC (82022012), General Fund of NSFC (81870598), Innovative research team of high-level local universities in Shanghai (SHSMU-ZDCX20212700)
- the National Key R & D Program of China (2022YFC2502800) and National Natural Science Fund of China (8238810007)
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Affiliation(s)
- Ling Dai
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Bin Sheng
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China.
| | - Tingli Chen
- Department of Ophthalmology, Huadong Sanatorium, Wuxi, China
| | - Qiang Wu
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruhan Liu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chun Cai
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Liang Wu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Dawei Yang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Haslina Hamzah
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Yuexing Liu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Zhouyu Guan
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Shujie Yu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Tingyao Li
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ziqi Tang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Anran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Haoxuan Che
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Hao Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
- Department of Chemical and Biological Engineering, The Hong Kong University of Science and Technology, Hong Kong, China
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Jia Shu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Shan Huang
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Chan Wu
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Shiqun Lin
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Dan Liu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Jiajia Li
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Zheyuan Wang
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Ziyao Meng
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Jie Shen
- Medical Records and Statistics Office, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Xuhong Hou
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Chenxin Deng
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lei Ruan
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Feng Lu
- National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Miaoli Chee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ten Cheer Quek
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Ramyaa Srinivasan
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Medical Research Foundation, Sankara Nethralaya, Chennai, India
| | - Xiaodong Sun
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ya Xing Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Science Key Laboratory, Beijing, China
| | - Jiarui Wu
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
- Center for Excellence in Molecular Science, Chinese Academy of Sciences, Shanghai, China
| | - Hai Jin
- National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
| | - Rongping Dai
- Department of Ophthalmology, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China
| | - Dinggang Shen
- School of Biomedical Engineering, Shanghai Tech University, Shanghai, China
- Shanghai United Imaging Intelligence, Shanghai, China
- Shanghai Clinical Research and Trial Center, Shanghai, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Minyi Guo
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China
| | - Cuntai Zhang
- Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Gavin Siew Wei Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Centre for Innovation and Precision Eye Health; and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore
- Centre for Innovation and Precision Eye Health; and Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Huating Li
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- Tsinghua Medicine, Beijing Tsinghua Changgung Hospital, Tsinghua University, Beijing, China.
| | - Weiping Jia
- Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai, China.
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20
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Liu C, Hu Y, Chen Y, Fang J, Liu R, Bi L, Tan X, Sheng B, Wu Q. Improvements to a GLCM-based machine-learning approach for quantifying posterior capsule opacification. J Appl Clin Med Phys 2024; 25:e14268. [PMID: 38259111 PMCID: PMC10860560 DOI: 10.1002/acm2.14268] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 12/26/2023] [Accepted: 01/02/2024] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Posterior capsular opacification (PCO) is a common complication following cataract surgery that leads to visual disturbances and decreased quality of vision. The aim of our study was to employ a machine-learning methodology to characterize and validate enhancements applied to the grey-level co-occurrence matrix (GLCM) while assessing its validity in comparison to clinical evaluations for evaluating PCO. METHODS One hundred patients diagnosed with age-related cataracts who were scheduled for phacoemulsification surgery were included in the study. Following mydriasis, anterior segment photographs were captured using a high-resolution photographic system. The GLCM was utilized as the feature extractor, and a supported vector machine as the regressor. Three variations, namely, GLCM, GLCM+C (+axial information), and GLCM+V (+regional voting), were analyzed. The reference value for regression was determined by averaging clinical scores obtained through subjective analysis. The relationships between the predicted PCO outcome scores and the ground truth were assessed using Pearson correlation analysis and a Bland-Altman plot, while agreement between them was assessed through the Bland-Altman plot. RESULTS Relative to the ground truth, the GLCM, GLCM+C, and GLCM+V methods exhibited correlation coefficients of 0.706, 0.768, and 0.829, respectively. The relationship between the PCO score predicted by the GLCM+V method and the ground truth was statistically significant (p < 0.001). Furthermore, the GLCM+V method demonstrated competitive performance comparable to that of two experienced clinicians (r = 0.825, 0.843) and superior to that of two junior clinicians (r = 0.786, 0.756). Notably, a high level of agreement was observed between predictions and the ground truth, without significant evidence of proportional bias (p > 0.05). CONCLUSIONS Overall, our findings suggest that a machine-learning approach incorporating the GLCM, specifically the GLCM+V method, holds promise as an objective and reliable tool for assessing PCO progression. Further studies in larger patient cohorts are warranted to validate these findings and explore their potential clinical applications.
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Affiliation(s)
- Chang Liu
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Hu
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yan Chen
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jian Fang
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ruhan Liu
- Furong Laboratory, Central South University, Changsha, Hunan, China
| | - Lei Bi
- Institute of Translational Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Xunan Tan
- Shanghai University of Sport School of Exercise and Health, Shanghai, China
| | - Bin Sheng
- Department of Computer Science and Engineering, Shanghai Jiao Tong University, Shanghai, China
| | - Qiang Wu
- Department of Ophthalmology, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
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21
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Valentim CCS, Wu AK, Yu S, Manivannan N, Zhang Q, Cao J, Song W, Wang V, Kang H, Kalur A, Iyer AI, Conti T, Singh RP, Talcott KE. Deep learning-based algorithm for the detection of idiopathic full thickness macular holes in spectral domain optical coherence tomography. Int J Retina Vitreous 2024; 10:9. [PMID: 38263402 PMCID: PMC10804727 DOI: 10.1186/s40942-024-00526-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2023] [Accepted: 01/04/2024] [Indexed: 01/25/2024] Open
Abstract
BACKGROUND Automated identification of spectral domain optical coherence tomography (SD-OCT) features can improve retina clinic workflow efficiency as they are able to detect pathologic findings. The purpose of this study was to test a deep learning (DL)-based algorithm for the identification of Idiopathic Full Thickness Macular Hole (IFTMH) features and stages of severity in SD-OCT B-scans. METHODS In this cross-sectional study, subjects solely diagnosed with either IFTMH or Posterior Vitreous Detachment (PVD) were identified excluding secondary causes of macular holes, any concurrent maculopathies, or incomplete records. SD-OCT scans (512 × 128) from all subjects were acquired with CIRRUS™ HD-OCT (ZEISS, Dublin, CA) and reviewed for quality. In order to establish a ground truth classification, each SD-OCT B-scan was labeled by two trained graders and adjudicated by a retina specialist when applicable. Two test sets were built based on different gold-standard classification methods. The sensitivity, specificity and accuracy of the algorithm to identify IFTMH features in SD-OCT B-scans were determined. Spearman's correlation was run to examine if the algorithm's probability score was associated with the severity stages of IFTMH. RESULTS Six hundred and one SD-OCT cube scans from 601 subjects (299 with IFTMH and 302 with PVD) were used. A total of 76,928 individual SD-OCT B-scans were labeled gradable by the algorithm and yielded an accuracy of 88.5% (test set 1, 33,024 B-scans) and 91.4% (test set 2, 43,904 B-scans) in identifying SD-OCT features of IFTMHs. A Spearman's correlation coefficient of 0.15 was achieved between the algorithm's probability score and the stages of the 299 (47 [15.7%] stage 2, 56 [18.7%] stage 3 and 196 [65.6%] stage 4) IFTMHs cubes studied. CONCLUSIONS The DL-based algorithm was able to accurately detect IFTMHs features on individual SD-OCT B-scans in both test sets. However, there was a low correlation between the algorithm's probability score and IFTMH severity stages. The algorithm may serve as a clinical decision support tool that assists with the identification of IFTMHs. Further training is necessary for the algorithm to identify stages of IFTMHs.
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Affiliation(s)
- Carolina C S Valentim
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA
| | - Anna K Wu
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Sophia Yu
- Carl Zeiss Meditec, Inc, Dublin, CA, USA
| | | | | | - Jessica Cao
- Cole Eye Institute, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Weilin Song
- Cleveland Clinic Lerner College of Medicine, Cleveland, OH, USA
| | - Victoria Wang
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Hannah Kang
- Case Western Reserve University School of Medicine, Cleveland, OH, USA
| | - Aneesha Kalur
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA
| | - Amogh I Iyer
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA
| | - Thais Conti
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA
| | - Rishi P Singh
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA
| | - Katherine E Talcott
- Center for Ophthalmic Bioinformatics, Cole Eye Institute, Cleveland Clinic Foundation, 9500 Euclid Ave. i32, Cleveland, OH, USA.
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22
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Liu X, Wu J, Shao A, Shen W, Ye P, Wang Y, Ye J, Jin K, Yang J. Uncovering Language Disparity of ChatGPT on Retinal Vascular Disease Classification: Cross-Sectional Study. J Med Internet Res 2024; 26:e51926. [PMID: 38252483 PMCID: PMC10845019 DOI: 10.2196/51926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2023] [Revised: 10/07/2023] [Accepted: 11/30/2023] [Indexed: 01/23/2024] Open
Abstract
BACKGROUND Benefiting from rich knowledge and the exceptional ability to understand text, large language models like ChatGPT have shown great potential in English clinical environments. However, the performance of ChatGPT in non-English clinical settings, as well as its reasoning, have not been explored in depth. OBJECTIVE This study aimed to evaluate ChatGPT's diagnostic performance and inference abilities for retinal vascular diseases in a non-English clinical environment. METHODS In this cross-sectional study, we collected 1226 fundus fluorescein angiography reports and corresponding diagnoses written in Chinese and tested ChatGPT with 4 prompting strategies (direct diagnosis or diagnosis with a step-by-step reasoning process and in Chinese or English). RESULTS Compared with ChatGPT using Chinese prompts for direct diagnosis that achieved an F1-score of 70.47%, ChatGPT using English prompts for direct diagnosis achieved the best diagnostic performance (80.05%), which was inferior to ophthalmologists (89.35%) but close to ophthalmologist interns (82.69%). As for its inference abilities, although ChatGPT can derive a reasoning process with a low error rate (0.4 per report) for both Chinese and English prompts, ophthalmologists identified that the latter brought more reasoning steps with less incompleteness (44.31%), misinformation (1.96%), and hallucinations (0.59%) (all P<.001). Also, analysis of the robustness of ChatGPT with different language prompts indicated significant differences in the recall (P=.03) and F1-score (P=.04) between Chinese and English prompts. In short, when prompted in English, ChatGPT exhibited enhanced diagnostic and inference capabilities for retinal vascular disease classification based on Chinese fundus fluorescein angiography reports. CONCLUSIONS ChatGPT can serve as a helpful medical assistant to provide diagnosis in non-English clinical environments, but there are still performance gaps, language disparities, and errors compared to professionals, which demonstrate the potential limitations and the need to continually explore more robust large language models in ophthalmology practice.
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Affiliation(s)
- Xiaocong Liu
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
- School of Public Health, Zhejiang University School of Medicine, Zhejiang, China
| | - Jiageng Wu
- School of Public Health, Zhejiang University School of Medicine, Zhejiang, China
| | - An Shao
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Wenyue Shen
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Panpan Ye
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Yao Wang
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Kai Jin
- Eye Center, The Second Affiliated Hospital, Zhejiang University, Zhejiang, China
| | - Jie Yang
- School of Public Health, Zhejiang University School of Medicine, Zhejiang, China
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23
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Chuter B, Huynh J, Bowd C, Walker E, Rezapour J, Brye N, Belghith A, Fazio MA, Girkin CA, De Moraes G, Liebmann JM, Weinreb RN, Zangwill LM, Christopher M. Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection. Transl Vis Sci Technol 2024; 13:23. [PMID: 38285462 PMCID: PMC10829806 DOI: 10.1167/tvst.13.1.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 12/26/2023] [Indexed: 01/30/2024] Open
Abstract
Purpose To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations. Methods Image quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC). Results The DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater (P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80-0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67-0.88). Conclusions The DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model. Translational Relevance Incorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.
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Affiliation(s)
- Benton Chuter
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Justin Huynh
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Christopher Bowd
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Evan Walker
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Jasmin Rezapour
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
- Department of Ophthalmology, University Medical Center Mainz, Germany
| | - Nicole Brye
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Akram Belghith
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Massimo A. Fazio
- School of Medicine, Callahan Eye Hospital, University of Alabama-Birmingham, Birmingham, Alabama, United States
| | - Christopher A. Girkin
- School of Medicine, Callahan Eye Hospital, University of Alabama-Birmingham, Birmingham, Alabama, United States
| | - Gustavo De Moraes
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Medical Center, New York, New York, United States
| | - Jeffrey M. Liebmann
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Medical Center, New York, New York, United States
| | - Robert N. Weinreb
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Linda M. Zangwill
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Mark Christopher
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
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24
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Wu P, Qu Y, Zhao Z, Cui Y, Xu Y, An P, Yu H. An adaptive weighted ensemble learning network for diabetic retinopathy classification. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2024; 32:285-301. [PMID: 38217630 DOI: 10.3233/xst-230252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Abstract
Diabetic retinopathy (DR) is one of the leading causes of blindness. However, because the data distribution of classes is not always balanced, it is challenging for automated early DR detection using deep learning techniques. In this paper, we propose an adaptive weighted ensemble learning method for DR detection based on optical coherence tomography (OCT) images. Specifically, we develop an ensemble learning model based on three advanced deep learning models for higher performance. To better utilize the cues implied in these base models, a novel decision fusion scheme is proposed based on the Bayesian theory in terms of the key evaluation indicators, to dynamically adjust the weighting distribution of base models to alleviate the negative effects potentially caused by the problem of unbalanced data size. Extensive experiments are performed on two public datasets to verify the effectiveness of the proposed method. A quadratic weighted kappa of 0.8487 and an accuracy of 0.9343 on the DRAC2022 dataset, and a quadratic weighted kappa of 0.9007 and an accuracy of 0.8956 on the APTOS2019 dataset are obtained, respectively. The results demonstrate that our method has the ability to enhance the ovearall performance of DR detection on OCT images.
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Affiliation(s)
- Panpan Wu
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
| | - Yue Qu
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
| | - Ziping Zhao
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
| | - Yue Cui
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
| | - Yurou Xu
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
| | - Peng An
- College of Computer and Information Engineering, Tianjin Normal University, Tianjin, China
| | - Hengyong Yu
- Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA, USA
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25
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Chen H, Liu P, Chen Z, Chen Q, Wen Z, Xie Z. Predicting sequenced dental treatment plans from electronic dental records using deep learning. Artif Intell Med 2024; 147:102734. [PMID: 38184358 DOI: 10.1016/j.artmed.2023.102734] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2022] [Revised: 02/26/2023] [Accepted: 11/28/2023] [Indexed: 01/08/2024]
Abstract
BACKGROUND Designing appropriate clinical dental treatment plans is an urgent need because a growing number of dental patients are suffering from partial edentulism with the population getting older. OBJECTIVES The aim of this study is to predict sequential treatment plans from electronic dental records. METHODS We construct a clinical decision support model, MultiTP, explores the unique topology of teeth information and the variation of complicated treatments, integrates deep learning models (convolutional neural network and recurrent neural network) adaptively, and embeds the attention mechanism to produce optimal treatment plans. RESULTS MultiTP shows its promising performance with an AUC of 0.9079 and an F score of 0.8472 over five treatment plans. The interpretability analysis also indicates its capability in mining clinical knowledge from the textual data. CONCLUSIONS MultiTP's novel problem formulation, neural network framework, and interpretability analysis techniques allow for broad applications of deep learning in dental healthcare, providing valuable support for predicting dental treatment plans in the clinic and benefiting dental patients. CLINICAL IMPLICATIONS The MultiTP is an efficient tool that can be implemented in clinical practice and integrated into the existing EDR system. By predicting treatment plans for partial edentulism, the model will help dentists improve their clinical decisions.
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Affiliation(s)
- Haifan Chen
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Xiangjiang Laboratory, Changsha, PR China
| | - Pufan Liu
- Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, PR China
| | - Zhaoxing Chen
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Xiangjiang Laboratory, Changsha, PR China
| | - Qingxiao Chen
- Peking University School and Hospital of Stomatology, Beijing, PR China; Georgia Institute of Technology, College of Computing, USA.
| | - Zaiwen Wen
- Beijing International Center for Mathematical Research, Peking University, Beijing, PR China
| | - Ziqing Xie
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China; Xiangjiang Laboratory, Changsha, PR China
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26
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Song A, Borkar DS. Advances in Teleophthalmology Screening for Diabetic Retinopathy. Int Ophthalmol Clin 2024; 64:97-113. [PMID: 38146884 DOI: 10.1097/iio.0000000000000505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2023]
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27
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Xia H, Long J, Song S, Tan Y. Multi-scale multi-attention network for diabetic retinopathy grading. Phys Med Biol 2023; 69:015007. [PMID: 38035368 DOI: 10.1088/1361-6560/ad111d] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2023] [Accepted: 11/30/2023] [Indexed: 12/02/2023]
Abstract
Objective.Diabetic retinopathy (DR) grading plays an important role in clinical diagnosis. However, automatic grading of DR is challenging due to the presence of intra-class variation and small lesions. On the one hand, deep features learned by convolutional neural networks often lose valid information about these small lesions. On the other hand, the great variability of lesion features, including differences in type and quantity, can exhibit considerable divergence even among fundus images of the same grade. To address these issues, we propose a novel multi-scale multi-attention network (MMNet).Approach.Firstly, to focus on different lesion features of fundus images, we propose a lesion attention module, which aims to encode multiple different lesion attention feature maps by combining channel attention and spatial attention, thus extracting global feature information and preserving diverse lesion features. Secondly, we propose a multi-scale feature fusion module to learn more feature information for small lesion regions, which combines complementary relationships between different convolutional layers to capture more detailed feature information. Furthermore, we introduce a Cross-layer Consistency Constraint Loss to overcome semantic differences between multi-scale features.Main results.The proposed MMNet obtains a high accuracy of 86.4% and a high kappa score of 88.4% for multi-class DR grading tasks on the EyePACS dataset, while 98.6% AUC, 95.3% accuracy, 92.7% recall, 95.0% precision, and 93.3% F1-score for referral and non-referral classification on the Messidor-1 dataset. Extensive experiments on two challenging benchmarks demonstrate that our MMNet achieves significant improvements and outperforms other state-of-the-art DR grading methods.Significance.MMNet has improved the diagnostic efficiency and accuracy of diabetes retinopathy and promoted the application of computer-aided medical diagnosis in DR screening.
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Affiliation(s)
- Haiying Xia
- School of Electronic and Information Engineering, Guangxi Normal University, Guilin 541004, People's Republic of China
| | - Jie Long
- School of Electronic and Information Engineering, Guangxi Normal University, Guilin 541004, People's Republic of China
| | - Shuxiang Song
- School of Electronic and Information Engineering, Guangxi Normal University, Guilin 541004, People's Republic of China
| | - Yumei Tan
- School of Computer Science and Engineering, Guangxi Normal University, Guilin 541004, People's Republic of China
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28
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Shi D, Zhang W, He S, Chen Y, Song F, Liu S, Wang R, Zheng Y, He M. Translation of Color Fundus Photography into Fluorescein Angiography Using Deep Learning for Enhanced Diabetic Retinopathy Screening. OPHTHALMOLOGY SCIENCE 2023; 3:100401. [PMID: 38025160 PMCID: PMC10630672 DOI: 10.1016/j.xops.2023.100401] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 08/23/2023] [Accepted: 09/08/2023] [Indexed: 12/01/2023]
Abstract
Purpose To develop and validate a deep learning model that can transform color fundus (CF) photography into corresponding venous and late-phase fundus fluorescein angiography (FFA) images. Design Cross-sectional study. Participants We included 51 370 CF-venous FFA pairs and 14 644 CF-late FFA pairs from 4438 patients for model development. External testing involved 50 eyes with CF-FFA pairs and 2 public datasets for diabetic retinopathy (DR) classification, with 86 952 CF from EyePACs, and 1744 CF from MESSIDOR2. Methods We trained a deep-learning model to transform CF into corresponding venous and late-phase FFA images. The translated FFA images' quality was evaluated quantitatively on the internal test set and subjectively on 100 eyes with CF-FFA paired images (50 from external), based on the realisticity of the global image, anatomical landmarks (macula, optic disc, and vessels), and lesions. Moreover, we validated the clinical utility of the translated FFA for classifying 5-class DR and diabetic macular edema (DME) in the EyePACs and MESSIDOR2 datasets. Main Outcome Measures Image generation was quantitatively assessed by structural similarity measures (SSIM), and subjectively by 2 clinical experts on a 5-point scale (1 refers real FFA); intragrader agreement was assessed by kappa. The DR classification accuracy was assessed by area under the receiver operating characteristic curve. Results The SSIM of the translated FFA images were > 0.6, and the subjective quality scores ranged from 1.37 to 2.60. Both experts reported similar quality scores with substantial agreement (all kappas > 0.8). Adding the generated FFA on top of CF improved DR classification in the EyePACs and MESSIDOR2 datasets, with the area under the receiver operating characteristic curve increased from 0.912 to 0.939 on the EyePACs dataset and from 0.952 to 0.972 on the MESSIDOR2 dataset. The DME area under the receiver operating characteristic curve also increased from 0.927 to 0.974 in the MESSIDOR2 dataset. Conclusions Our CF-to-FFA framework produced realistic FFA images. Moreover, adding the translated FFA images on top of CF improved the accuracy of DR screening. These results suggest that CF-to-FFA translation could be used as a surrogate method when FFA examination is not feasible and as a simple add-on to improve DR screening. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Danli Shi
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Weiyi Zhang
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Shuang He
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Guangdong Provincial Clinical Research Center for Ocular Diseases, Sun Yat-sen University, Guangzhou, China
| | - Yanxian Chen
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Fan Song
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong
| | - Shunming Liu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Ruobing Wang
- Department of Ophthalmology, Renji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
| | - Yingfeng Zheng
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Zhongshan Ophthalmic Center, Guangdong Provincial Clinical Research Center for Ocular Diseases, Sun Yat-sen University, Guangzhou, China
| | - Mingguang He
- School of Optometry, The Hong Kong Polytechnic University, Kowloon, Hong Kong
- Research Centre for SHARP Vision (RCSV), The Hong Kong Polytechnic University, Kowloon, Hong Kong
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
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Lin YT, Zhou Q, Tan J, Tao Y. Multimodal and multi-omics-based deep learning model for screening of optic neuropathy. Heliyon 2023; 9:e22244. [PMID: 38046141 PMCID: PMC10686864 DOI: 10.1016/j.heliyon.2023.e22244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 11/06/2023] [Accepted: 11/07/2023] [Indexed: 12/05/2023] Open
Abstract
Purpose To examine the use of multimodal data and multi-omics strategies for optic nerve disease screening. Methods This was a single-center retrospective study. A deep learning model was created from fundus photography and infrared reflectance (IR) images of patients with diabetic optic neuropathy, glaucomatous optic neuropathy, and optic neuritis. Patients who were seen at the Ophthalmology Department of First Affiliated Hospital of Nanchang University in Jiangxi Province from November 2019 to April 2023 were included in this study. The data were analyzed in single and multimodal modes following the traditional omics, Resnet101, and fusion models. The accuracy and area-under-the-curve (AUC) of each model were compared. Results A total of 312 images fundus and infrared fundus photographs were collected from 156 patients. When multi-modal data was used, the accuracy of the traditional omics mode, Resnet101, and fusion models with the training set were 0.97, 0.98, and 0.99, respectively. The accuracy of the same models with the test sets were 0.72, 0.87, and 0.88, respectively. We compared single- and multi-mode states by applying the data to the different groups in the learning model. In the traditional omics model, the macro-average AUCs of the features extracted from fundus photography, IR images, and multimodal data were 0.94, 0.90, and 0.96, respectively. When the same data were processed in the Resnet101 model, the scores were 0.97 equally. However, when multimodal data was utilized, the macro-average AUCs in the traditional omics, Resnet101, and fusion modesl were 0.96, 0.97, and 0.99, respectively. Conclusion The deep learning model based on multimodal data and multi-omics strategies can improve the accuracy of screening and diagnosing diabetic optic neuropathy, glaucomatous optic neuropathy, and optic neuritis.
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Affiliation(s)
- Ye-ting Lin
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, China
| | - Qiong Zhou
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, China
| | - Jian Tan
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, China
| | - Yulin Tao
- Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, China
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Liao X, Yao C, Zhang J, Liu LZ. Recent advancement in integrating artificial intelligence and information technology with real-world data for clinical decision-making in China: A scoping review. J Evid Based Med 2023; 16:534-546. [PMID: 37772921 DOI: 10.1111/jebm.12549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Accepted: 08/31/2023] [Indexed: 09/30/2023]
Abstract
OBJECTIVE Striking innovations and advancements have been achieved with the use of artificial intelligence and healthcare information technology being integrated into clinical real-world data. The current scoping review aimed to provide an overview of the current status of artificial intelligence-/information technology-based clinical decision support tools in China. METHODS PubMed/MEDLINE, Embase, China National Knowledge Internet, and Wanfang data were searched for both English and Chinese literature. The gray literature search was conducted for commercially available tools. Original studies that focused on clinical decision support tools driven by artificial intelligence or information technology in China and were published between 2010 and February 2022 were included. Information extracted from each article was further synthesized by themes based on three types of clinical decision-making. RESULTS A total of 37 peer-reviewed publications and 13 commercially available tools were included in the final analysis. Among them, 32.0% were developed for disease diagnosis, 54.0% for risk prediction and classification, and 14.0% for disease management. Chronic diseases were the most popular therapeutic areas of exploration, with particular emphasis on cardiovascular and cerebrovascular diseases. Single-center electronic medical records were the mainstream data sources leveraged to inform clinical decision-making, with internal validation being predominately used for model evaluation. CONCLUSIONS To effectively promote the extensive use of real-world data and drive a paradigm shift in clinical decision-making in China, multidisciplinary collaboration of key stakeholders is urgently needed.
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Affiliation(s)
- Xiwen Liao
- Peking University Clinical Research Institute, Peking University First Hospital, Beijing, China
| | - Chen Yao
- Peking University Clinical Research Institute, Peking University First Hospital, Beijing, China
- Hainan Institute of Real World Data, Qionghai, Hainan, China
| | - Jun Zhang
- Center for Observational and Real-world Evidence (CORE), MSD R&D (China) Co., Ltd., Beijing, China
| | - Larry Z Liu
- Center for Observational and Real-world Evidence (CORE), Merck & Co Inc, Rahway, Rahway, New Jersey, USA
- Department of Population Health Sciences, Weill Cornell Medical College, New York City, New York, USA
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31
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Araújo LR, Orefice JL, Gonçalves MA, Guimarães NS, Soares AN, Salomon T, de Souza AH. Use of digital retinography to detect vascular changes in pre-diabetic patients: a cross-sectional study. Diabetol Metab Syndr 2023; 15:225. [PMID: 37926814 PMCID: PMC10626765 DOI: 10.1186/s13098-023-01154-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 08/22/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND Diabetic retinopathy (DR) is a common complication of DM and may go unnoticed until irreversible damage occurs. Its screening can contribute to the early detection. Although, there are no studies which investigate the ability of digital retinography to detect vascular changes in pre-diabetic patients. OBJECTIVE Identify the prevalence and severity of RD in patients with pre-diabetes. METHODS Cross-sectionalstudy carried out in a sample of patients with pre-diabetes and weight excess characterized from January 2020 to April 2023. Sociodemographic and clinical variables were collected, in addition to lifestyle habits. Retinographic evaluation was also performed using a Digital Retinography. For the analysis of all variables, the adopted significance level was 5%. The software used for the analysis was SPSS version 25.0. RESULTS Of 108 patients selected 7.1% have alteration in the exam indicating DR. Among the participants with diabetic retinopathy, four had the moderate form (50%), three the moderate form (37%) and only one participant had the severe form (13%). CONCLUSIONS Our findings highlight the importance of preventive measures and adequate control of these conditions in pre-diabetic patients, in order to prevent or delay the progression of diabetic retinopathy and, consequently, reduce the risk of blindness and other ocular complications.
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Affiliation(s)
- Levimar Rocha Araújo
- Faculdade de Ciências Médicas de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | | | | | | | - Aleida Nazareth Soares
- Faculdade de Ciências Médicas de Minas Gerais and Faculdade de Saúde Santa Casa BH, Belo Horizonte, Brazil
| | - Tassila Salomon
- Faculdade de Ciências Médicas de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
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32
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Ma F, Wang S, Dai C, Qi F, Meng J. A new retinal OCT-angiography diabetic retinopathy dataset for segmentation and DR grading. JOURNAL OF BIOPHOTONICS 2023; 16:e202300052. [PMID: 37421596 DOI: 10.1002/jbio.202300052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 06/29/2023] [Accepted: 06/30/2023] [Indexed: 07/10/2023]
Abstract
PURPOSE Diabetic retinopathy (DR) is one of the most common diseases caused by diabetes and can lead to vision loss or even blindness. The wide-field optical coherence tomography (OCT) angiography is non-invasive imaging technology and convenient to diagnose DR. METHODS A newly constructed Retinal OCT-Angiography Diabetic retinopathy (ROAD) dataset is utilized for segmentation and grading tasks. It contains 1200 normal images, 1440 DR images, and 1440 ground truths for DR image segmentation. To handle the problem of grading DR, we propose a novel and effective framework, named projective map attention-based convolutional neural network (PACNet). RESULTS The experimental results demonstrate the effectiveness of our PACNet. The accuracy of the proposed framework for grading DR is 87.5% on the ROAD dataset. CONCLUSIONS The information on ROAD can be viewed at URL https://mip2019.github.io/ROAD. The ROAD dataset will be helpful for the development of the early detection of DR field and future research. TRANSLATIONAL RELEVANCE The novel framework for grading DR is a valuable research and clinical diagnosis method.
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Affiliation(s)
- Fei Ma
- Qufu Normal University, Rizhao, Shandong, China
| | | | - Cuixia Dai
- College Science, Shanghai Institute of Technology, Shanghai, China
| | - Fumin Qi
- National Supercomputing Center in Shenzhen, Shenzhen, Guangdong, China
| | - Jing Meng
- Qufu Normal University, Rizhao, Shandong, China
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Guan Z, Li H, Liu R, Cai C, Liu Y, Li J, Wang X, Huang S, Wu L, Liu D, Yu S, Wang Z, Shu J, Hou X, Yang X, Jia W, Sheng B. Artificial intelligence in diabetes management: Advancements, opportunities, and challenges. Cell Rep Med 2023; 4:101213. [PMID: 37788667 PMCID: PMC10591058 DOI: 10.1016/j.xcrm.2023.101213] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 08/07/2023] [Accepted: 09/08/2023] [Indexed: 10/05/2023]
Abstract
The increasing prevalence of diabetes, high avoidable morbidity and mortality due to diabetes and diabetic complications, and related substantial economic burden make diabetes a significant health challenge worldwide. A shortage of diabetes specialists, uneven distribution of medical resources, low adherence to medications, and improper self-management contribute to poor glycemic control in patients with diabetes. Recent advancements in digital health technologies, especially artificial intelligence (AI), provide a significant opportunity to achieve better efficiency in diabetes care, which may diminish the increase in diabetes-related health-care expenditures. Here, we review the recent progress in the application of AI in the management of diabetes and then discuss the opportunities and challenges of AI application in clinical practice. Furthermore, we explore the possibility of combining and expanding upon existing digital health technologies to develop an AI-assisted digital health-care ecosystem that includes the prevention and management of diabetes.
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Affiliation(s)
- Zhouyu Guan
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Huating Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Ruhan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; National Engineering Research Center of Personalized Diagnostic and Therapeutic Technology, Furong Laboratory, Changsha, Hunan 41000, China
| | - Chun Cai
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Yuexing Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Jiajia Li
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xiangning Wang
- Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
| | - Shan Huang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Liang Wu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Dan Liu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Shujie Yu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Zheyuan Wang
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Jia Shu
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xuhong Hou
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China
| | - Xiaokang Yang
- MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Weiping Jia
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China.
| | - Bin Sheng
- Shanghai International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases, Department of Computer Science and Engineering, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Clinical Center for Diabetes, Shanghai 200240, China; MOE Key Laboratory of AI, School of Electronic, Information, and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
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Zhao X, Lin Z, Yu S, Xiao J, Xie L, Xu Y, Tsui CK, Cui K, Zhao L, Zhang G, Zhang S, Lu Y, Lin H, Liang X, Lin D. An artificial intelligence system for the whole process from diagnosis to treatment suggestion of ischemic retinal diseases. Cell Rep Med 2023; 4:101197. [PMID: 37734379 PMCID: PMC10591037 DOI: 10.1016/j.xcrm.2023.101197] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2023] [Revised: 05/29/2023] [Accepted: 08/23/2023] [Indexed: 09/23/2023]
Abstract
Ischemic retinal diseases (IRDs) are a series of common blinding diseases that depend on accurate fundus fluorescein angiography (FFA) image interpretation for diagnosis and treatment. An artificial intelligence system (Ai-Doctor) was developed to interpret FFA images. Ai-Doctor performed well in image phase identification (area under the curve [AUC], 0.991-0.999, range), diabetic retinopathy (DR) and branch retinal vein occlusion (BRVO) diagnosis (AUC, 0.979-0.992), and non-perfusion area segmentation (Dice similarity coefficient [DSC], 89.7%-90.1%) and quantification. The segmentation model was expanded to unencountered IRDs (central RVO and retinal vasculitis), with DSCs of 89.2% and 83.6%, respectively. A clinically applicable ischemia index (CAII) was proposed to evaluate ischemic degree; patients with CAII values exceeding 0.17 in BRVO and 0.08 in DR may be associated with increased possibility for laser therapy. Ai-Doctor is expected to achieve accurate FFA image interpretation for IRDs, potentially reducing the reliance on retinal specialists.
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Affiliation(s)
- Xinyu Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China; Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen 518040, China
| | - Zhenzhe Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Shanshan Yu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Jun Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Liqiong Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Yue Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Ching-Kit Tsui
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Kaixuan Cui
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Lanqin Zhao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China
| | - Guoming Zhang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen 518040, China
| | - Shaochong Zhang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen 518040, China
| | - Yan Lu
- Foshan Second People's Hospital, Foshan 528001, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China; Hainan Eye Hospital and Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Haikou 570311, China; Center for Precision Medicine and Department of Genetics and Biomedical Informatics, Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.
| | - Xiaoling Liang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Duoru Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
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Williamson RC, Selvam A, Sant V, Patel M, Bollepalli SC, Vupparaboina KK, Sahel JA, Chhablani J. Radiomics-Based Prediction of Anti-VEGF Treatment Response in Neovascular Age-Related Macular Degeneration With Pigment Epithelial Detachment. Transl Vis Sci Technol 2023; 12:3. [PMID: 37792693 PMCID: PMC10565708 DOI: 10.1167/tvst.12.10.3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 09/01/2023] [Indexed: 10/06/2023] Open
Abstract
Purpose Machine learning models based on radiomic feature extraction from clinical imaging data provide effective and interpretable means for clinical decision making. This pilot study evaluated whether radiomics features in baseline optical coherence tomography (OCT) images of eyes with pigment epithelial detachment (PED) associated with neovascular age-related macular degeneration (nAMD) can predict treatment response to as-needed anti-vascular endothelial growth factor (VEGF) therapy. Methods Thirty-nine eyes of patients with PED undergoing anti-VEGF therapy were included. All eyes underwent a loading dose followed by as-needed therapy. OCT images at baseline, month 3, and month 6 were analyzed. Images were manually separated into non-responding, recurring, and responding eyes based on the presence or absence of subretinal fluid at month 6. PED radiomics features were then extracted from each image and images were classified as responding or recurring using a machine learning classifier applied to the radiomics features. Results Linear discriminant analysis classification of baseline features as responsive versus recurring resulted in classification performance of 64.0% (95% confidence interval [CI] = 0.63-0.65), area under the curve (AUC = 0.78, 95% CI = 0.72-0.82), sensitivity 0.79 (95% CI = 0.63-0.87), and specificity 0.58 (95% CI = 0.50-0.67). Further analysis of features in recurring eyes identified a significant shift toward non-responding mean feature values over 6 months. Conclusions Our results demonstrate the use of radiomics features as predictors for treatment response to as-needed anti-VEGF therapy. Our study demonstrates the potential for radiomics feature in clinical decision support for personalizing anti-VEGF therapy. Translational Relevance The ability to use PED texture features to predict treatment response facilitates personalized clinical decision making.
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Affiliation(s)
- Ryan Chace Williamson
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Amrish Selvam
- School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
| | | | - Manan Patel
- BJ Medical College, Ahmedabad, Gujarat, India
| | | | | | - Jose-Alain Sahel
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Jay Chhablani
- Department of Ophthalmology, University of Pittsburgh, Pittsburgh, PA, USA
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Xue W, Zhang J, Ma Y, Hou J, Xiao F, Feng R, Zhao R, Zou H. Deep learning-based analysis of infrared fundus photography for automated diagnosis of diabetic retinopathy with cataracts. J Cataract Refract Surg 2023; 49:1043-1048. [PMID: 37488748 DOI: 10.1097/j.jcrs.0000000000001269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Accepted: 07/16/2023] [Indexed: 07/26/2023]
Abstract
PURPOSE To develop deep learning-based networks for the diagnosis of diabetic retinopathy (DR) with cataracts based on infrared fundus images. SETTING Shanghai General Hospital, Shanghai Eye Disease Prevention & Treatment Center, Shanghai, China. DESIGN Development and evaluation of an artificial intelligence (AI) diagnostic method. METHODS A total of 10 665 infrared fundus images from 4553 patients with diabetes were used to train and test the model. For image quality assessment, left and right eye classification, DR diagnosis and grading, and segmentation of 3 DR lesions, an end-to-end software using EfficientNet and UNet was developed. The accuracy and performance of the software in comparison to human experts was evaluated. RESULTS The model achieved an accuracy of 75.31% for left and right eye classification, 100% for DR grading and diagnosis tasks, and 73.67% for internal test set, with corresponding areas under the curve (AUCs) of 0.88, 1.00, and 0.89, respectively. For DR lesion segmentation, the AUCs of hemorrhagic, microangioma, and exudative lesions were 0.86, 0.66, and 0.84, respectively. In addition, a contrast test of human-machine film reading confirmed the software's high sensitivity (96.3%) and specificity (90.0%) and consistency with the manual film reading group (κ = 0.869, P < .001). This easily deployable software generated reports quickly and promoted efficient DR screening with cataracts in clinical and community settings. CONCLUSIONS AI-assisted software can perform automatic analysis of infrared fundus images and has substantial application value for the diagnosis of DR patients with cataracts.
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Affiliation(s)
- Wenwen Xue
- From the Department of Ophthalmology, Shanghai Eye Disease Prevention & Treatment Center, Shanghai, China (Xue, Zou); Department of Ophthalmology, Shanghai General Hospital; National Clinical Research Center for Eye Diseases; Shanghai Key Laboratory of Ocular Fundus Diseases; Shanghai Engineering Center for Visual Science and Photomedicine; Shanghai Engineering Research Center of Precise Diagnosis and Treatment of Eye Diseases, Shanghai, China (Zhang, Ma, Zou); School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China (Hou, Feng); Academy for Engineering and Technology, Fudan University, Shanghai, China (Xiao, Feng, Zhao)
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Oikonomou EK, Khera R. Machine learning in precision diabetes care and cardiovascular risk prediction. Cardiovasc Diabetol 2023; 22:259. [PMID: 37749579 PMCID: PMC10521578 DOI: 10.1186/s12933-023-01985-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Accepted: 09/07/2023] [Indexed: 09/27/2023] Open
Abstract
Artificial intelligence and machine learning are driving a paradigm shift in medicine, promising data-driven, personalized solutions for managing diabetes and the excess cardiovascular risk it poses. In this comprehensive review of machine learning applications in the care of patients with diabetes at increased cardiovascular risk, we offer a broad overview of various data-driven methods and how they may be leveraged in developing predictive models for personalized care. We review existing as well as expected artificial intelligence solutions in the context of diagnosis, prognostication, phenotyping, and treatment of diabetes and its cardiovascular complications. In addition to discussing the key properties of such models that enable their successful application in complex risk prediction, we define challenges that arise from their misuse and the role of methodological standards in overcoming these limitations. We also identify key issues in equity and bias mitigation in healthcare and discuss how the current regulatory framework should ensure the efficacy and safety of medical artificial intelligence products in transforming cardiovascular care and outcomes in diabetes.
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Affiliation(s)
- Evangelos K Oikonomou
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA
| | - Rohan Khera
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
- Section of Biomedical Informatics and Data Science, Yale School of Medicine, New Haven, CT, USA.
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, 195 Church St, 6th floor, New Haven, CT, 06510, USA.
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Liu YF, Ji YK, Fei FQ, Chen NM, Zhu ZT, Fei XZ. Research progress in artificial intelligence assisted diabetic retinopathy diagnosis. Int J Ophthalmol 2023; 16:1395-1405. [PMID: 37724288 PMCID: PMC10475636 DOI: 10.18240/ijo.2023.09.05] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 06/14/2023] [Indexed: 09/20/2023] Open
Abstract
Diabetic retinopathy (DR) is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide. Early detection and treatment can effectively delay vision decline and even blindness in patients with DR. In recent years, artificial intelligence (AI) models constructed by machine learning and deep learning (DL) algorithms have been widely used in ophthalmology research, especially in diagnosing and treating ophthalmic diseases, particularly DR. Regarding DR, AI has mainly been used in its diagnosis, grading, and lesion recognition and segmentation, and good research and application results have been achieved. This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.
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Affiliation(s)
- Yun-Fang Liu
- Department of Ophthalmology, First People's Hospital of Huzhou, Huzhou University, Huzhou 313000, Zhejiang Province, China
| | - Yu-Ke Ji
- Eye Hospital, Nanjing Medical University, Nanjing 210000, Jiangsu Province, China
| | - Fang-Qin Fei
- Department of Endocrinology, First People's Hospital of Huzhou, Huzhou University, Huzhou 313000, Zhejiang Province, China
| | - Nai-Mei Chen
- Department of Ophthalmology, Huai'an Hospital of Huai'an City, Huai'an 223000, Jiangsu Province, China
| | - Zhen-Tao Zhu
- Department of Ophthalmology, Huai'an Hospital of Huai'an City, Huai'an 223000, Jiangsu Province, China
| | - Xing-Zhen Fei
- Department of Endocrinology, First People's Hospital of Huzhou, Huzhou University, Huzhou 313000, Zhejiang Province, China
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Zhou W, Bai W, Ji J, Yi Y, Zhang N, Cui W. Dual-path multi-scale context dense aggregation network for retinal vessel segmentation. Comput Biol Med 2023; 164:107269. [PMID: 37562323 DOI: 10.1016/j.compbiomed.2023.107269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 06/22/2023] [Accepted: 07/16/2023] [Indexed: 08/12/2023]
Abstract
There has been steady progress in the field of deep learning-based blood vessel segmentation. However, several challenging issues still continue to limit its progress, including inadequate sample sizes, the neglect of contextual information, and the loss of microvascular details. To address these limitations, we propose a dual-path deep learning framework for blood vessel segmentation. In our framework, the fundus images are divided into concentric patches with different scales to alleviate the overfitting problem. Then, a Multi-scale Context Dense Aggregation Network (MCDAU-Net) is proposed to accurately extract the blood vessel boundaries from these patches. In MCDAU-Net, a Cascaded Dilated Spatial Pyramid Pooling (CDSPP) module is designed and incorporated into intermediate layers of the model, enhancing the receptive field and producing feature maps enriched with contextual information. To improve segmentation performance for low-contrast vessels, we propose an InceptionConv (IConv) module, which can explore deeper semantic features and suppress the propagation of non-vessel information. Furthermore, we design a Multi-scale Adaptive Feature Aggregation (MAFA) module to fuse the multi-scale feature by assigning adaptive weight coefficients to different feature maps through skip connections. Finally, to explore the complementary contextual information and enhance the continuity of microvascular structures, a fusion module is designed to combine the segmentation results obtained from patches of different sizes, achieving fine microvascular segmentation performance. In order to assess the effectiveness of our approach, we conducted evaluations on three widely-used public datasets: DRIVE, CHASE-DB1, and STARE. Our findings reveal a remarkable advancement over the current state-of-the-art (SOTA) techniques, with the mean values of Se and F1 scores being an increase of 7.9% and 4.7%, respectively. The code is available at https://github.com/bai101315/MCDAU-Net.
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Affiliation(s)
- Wei Zhou
- College of Computer Science, Shenyang Aerospace University, Shenyang, China
| | - Weiqi Bai
- College of Computer Science, Shenyang Aerospace University, Shenyang, China
| | - Jianhang Ji
- College of Computer Science, Shenyang Aerospace University, Shenyang, China
| | - Yugen Yi
- School of Software, Jiangxi Normal University, Nanchang, China.
| | - Ningyi Zhang
- School of Software, Jiangxi Normal University, Nanchang, China
| | - Wei Cui
- Institute for Infocomm Research, The Agency for Science, Technology and Research (A*STAR), Singapore.
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Zhelev Z, Peters J, Rogers M, Allen M, Kijauskaite G, Seedat F, Wilkinson E, Hyde C. Test accuracy of artificial intelligence-based grading of fundus images in diabetic retinopathy screening: A systematic review. J Med Screen 2023; 30:97-112. [PMID: 36617971 PMCID: PMC10399100 DOI: 10.1177/09691413221144382] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 11/14/2022] [Accepted: 11/18/2022] [Indexed: 01/10/2023]
Abstract
OBJECTIVES To systematically review the accuracy of artificial intelligence (AI)-based systems for grading of fundus images in diabetic retinopathy (DR) screening. METHODS We searched MEDLINE, EMBASE, the Cochrane Library and the ClinicalTrials.gov from 1st January 2000 to 27th August 2021. Accuracy studies published in English were included if they met the pre-specified inclusion criteria. Selection of studies for inclusion, data extraction and quality assessment were conducted by one author with a second reviewer independently screening and checking 20% of titles. Results were analysed narratively. RESULTS Forty-three studies evaluating 15 deep learning (DL) and 4 machine learning (ML) systems were included. Nine systems were evaluated in a single study each. Most studies were judged to be at high or unclear risk of bias in at least one QUADAS-2 domain. Sensitivity for referable DR and higher grades was ≥85% while specificity varied and was <80% for all ML systems and in 6/31 studies evaluating DL systems. Studies reported high accuracy for detection of ungradable images, but the latter were analysed and reported inconsistently. Seven studies reported that AI was more sensitive but less specific than human graders. CONCLUSIONS AI-based systems are more sensitive than human graders and could be safe to use in clinical practice but have variable specificity. However, for many systems evidence is limited, at high risk of bias and may not generalise across settings. Therefore, pre-implementation assessment in the target clinical pathway is essential to obtain reliable and applicable accuracy estimates.
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Affiliation(s)
- Zhivko Zhelev
- Exeter Test Group, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Jaime Peters
- Exeter Test Group, University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Morwenna Rogers
- NIHR ARC South West Peninsula (PenARC), University of Exeter Medical School, University of Exeter, Exeter, UK
| | - Michael Allen
- University of Exeter Medical School, University of Exeter, Exeter, UK
| | | | | | | | - Christopher Hyde
- Exeter Test Group, University of Exeter Medical School, University of Exeter, Exeter, UK
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Ordoñez-Guillen NE, Gonzalez-Compean JL, Lopez-Arevalo I, Contreras-Murillo M, Aldana-Bobadilla E. Machine learning based study for the classification of Type 2 diabetes mellitus subtypes. BioData Min 2023; 16:24. [PMID: 37608329 PMCID: PMC10463725 DOI: 10.1186/s13040-023-00340-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Accepted: 08/07/2023] [Indexed: 08/24/2023] Open
Abstract
PURPOSE Data-driven diabetes research has increased its interest in exploring the heterogeneity of the disease, aiming to support in the development of more specific prognoses and treatments within the so-called precision medicine. Recently, one of these studies found five diabetes subgroups with varying risks of complications and treatment responses. Here, we tackle the development and assessment of different models for classifying Type 2 Diabetes (T2DM) subtypes through machine learning approaches, with the aim of providing a performance comparison and new insights on the matter. METHODS We developed a three-stage methodology starting with the preprocessing of public databases NHANES (USA) and ENSANUT (Mexico) to construct a dataset with N = 10,077 adult diabetes patient records. We used N = 2,768 records for training/validation of models and left the remaining (N = 7,309) for testing. In the second stage, groups of observations -each one representing a T2DM subtype- were identified. We tested different clustering techniques and strategies and validated them by using internal and external clustering indices; obtaining two annotated datasets Dset A and Dset B. In the third stage, we developed different classification models assaying four algorithms, seven input-data schemes, and two validation settings on each annotated dataset. We also tested the obtained models using a majority-vote approach for classifying unseen patient records in the hold-out dataset. RESULTS From the independently obtained bootstrap validation for Dset A and Dset B, mean accuracies across all seven data schemes were [Formula: see text] ([Formula: see text]) and [Formula: see text] ([Formula: see text]), respectively. Best accuracies were [Formula: see text] and [Formula: see text]. Both validation setting results were consistent. For the hold-out dataset, results were consonant with most of those obtained in the literature in terms of class proportions. CONCLUSION The development of machine learning systems for the classification of diabetes subtypes constitutes an important task to support physicians for fast and timely decision-making. We expect to deploy this methodology in a data analysis platform to conduct studies for identifying T2DM subtypes in patient records from hospitals.
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Affiliation(s)
- Nelson E Ordoñez-Guillen
- Cinvestav Tamaulipas, Carretera Victoria-Soto la Marina km 5.5, Victoria, 87130, Tamaulipas, Mexico
| | | | - Ivan Lopez-Arevalo
- Cinvestav Tamaulipas, Carretera Victoria-Soto la Marina km 5.5, Victoria, 87130, Tamaulipas, Mexico
| | - Miguel Contreras-Murillo
- Cinvestav Tamaulipas, Carretera Victoria-Soto la Marina km 5.5, Victoria, 87130, Tamaulipas, Mexico
| | - Edwin Aldana-Bobadilla
- CONAHCYT-Centro de Investigación y de Estudios Avanzados del IPN, Unidad Tamaulipas, Carretera Victoria-Soto la Marina km 5.5, Victoria, Tamaulipas, 87130, Mexico
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Ning JY, Yang HY, Xie TK, Chen YX, Han J. Down-regulation of histone deacetylase 7 reduces biological activities of retinal microvascular endothelial cells under high glucose condition and related mechanism. Int J Ophthalmol 2023; 16:1210-1217. [PMID: 37602334 PMCID: PMC10398535 DOI: 10.18240/ijo.2023.08.04] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Accepted: 06/09/2023] [Indexed: 08/22/2023] Open
Abstract
AIM To investigate the expression and effect of histone deacetylase 7 (HDAC7) in human retinal microvascular endothelial cells (HRMECs) under high glucose condition and related mechanism, and the expression of HDAC7 in the retinal tissue in diabetic rats. METHODS The expression of HDAC7 in HRMECs under high glucose and the retinal tissue from normal or diabetic rats were detected with immunohistochemistry and Western blot. LV-shHDAC7 HRMECs were used to study the effect of HDAC7 on cell activities. Cell count kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), flow cytometry, scratch test, Transwell test and tube formation assay were used to examine the ability of cell proliferation, migration, and angiogenesis. Finally, a preliminary exploration of its mechanism was performed by Western blot. RESULTS The expression of HDAC7 was both up-regulated in retinal tissues of diabetic rats and high glucose-treated HRMECs. Down-regulation of HDAC7 expression significantly reduced the ability of proliferation, migration, and tube formation, and reversed the high glucose-induced high expression of CDK1/Cyclin B1 and vascular endothelial growth factor in high glucose-treated HRMECs. CONCLUSION High glucose can up-regulate the expression of HDAC7 in HRMECs. Down-regulation of HDAC7 can inhibit HRMECs activities. HDAC7 is proposed to be involved in pathogenesis of diabetic retinopathy and a therapeutic target.
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Affiliation(s)
- Jia-Yi Ning
- Xi'an Medical University, Xi'an 710021, Shaanxi Province, China
- Department of Ophthalmology, Tangdu Hospital, the Air Force Military Medical University, Xi'an 710038, Shaanxi Province, China
| | - Han-Yi Yang
- Xi'an Medical University, Xi'an 710021, Shaanxi Province, China
- Department of Ophthalmology, Tangdu Hospital, the Air Force Military Medical University, Xi'an 710038, Shaanxi Province, China
| | - Ting-Ke Xie
- Xi'an Medical University, Xi'an 710021, Shaanxi Province, China
- Department of Ophthalmology, Tangdu Hospital, the Air Force Military Medical University, Xi'an 710038, Shaanxi Province, China
| | - Yi-Xuan Chen
- Xi'an Medical University, Xi'an 710021, Shaanxi Province, China
- Department of Ophthalmology, Tangdu Hospital, the Air Force Military Medical University, Xi'an 710038, Shaanxi Province, China
| | - Jing Han
- Department of Ophthalmology, Tangdu Hospital, the Air Force Military Medical University, Xi'an 710038, Shaanxi Province, China
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Chun JW, Kim HS. The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use. J Korean Med Sci 2023; 38:e253. [PMID: 37550811 PMCID: PMC10412032 DOI: 10.3346/jkms.2023.38.e253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/12/2023] [Indexed: 08/09/2023] Open
Abstract
Artificial intelligence (AI)-based diagnostic technology using medical images can be used to increase examination accessibility and support clinical decision-making for screening and diagnosis. To determine a machine learning algorithm for diabetes complications, a literature review of studies using medical image-based AI technology was conducted using the National Library of Medicine PubMed, and the Excerpta Medica databases. Lists of studies using diabetes diagnostic images and AI as keywords were combined. In total, 227 appropriate studies were selected. Diabetic retinopathy studies using the AI model were the most frequent (85.0%, 193/227 cases), followed by diabetic foot (7.9%, 18/227 cases) and diabetic neuropathy (2.7%, 6/227 cases). The studies used open datasets (42.3%, 96/227 cases) or directly constructed data from fundoscopy or optical coherence tomography (57.7%, 131/227 cases). Major limitations in AI-based detection of diabetes complications using medical images were the lack of datasets (36.1%, 82/227 cases) and severity misclassification (26.4%, 60/227 cases). Although it remains difficult to use and fully trust AI-based imaging analysis technology clinically, it reduces clinicians' time and labor, and the expectations from its decision-support roles are high. Various data collection and synthesis data technology developments according to the disease severity are required to solve data imbalance.
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Affiliation(s)
- Ji-Won Chun
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
| | - Hun-Sung Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, Korea
- Division of Endocrinology and Metabolism, Department of Internal Medicine, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Korea.
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Cheung HC, De Louche C, Komorowski M. Artificial Intelligence Applications in Space Medicine. Aerosp Med Hum Perform 2023; 94:610-622. [PMID: 37501303 DOI: 10.3357/amhp.6178.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
INTRODUCTION:During future interplanetary space missions, a number of health conditions may arise, owing to the hostile environment of space and the myriad of stressors experienced by the crew. When managing these conditions, crews will be required to make accurate, timely clinical decisions at a high level of autonomy, as telecommunication delays and increasing distances restrict real-time support from the ground. On Earth, artificial intelligence (AI) has proven successful in healthcare, augmenting expert clinical decision-making or enhancing medical knowledge where it is lacking. Similarly, deploying AI tools in the context of a space mission could improve crew self-reliance and healthcare delivery.METHODS: We conducted a narrative review to discuss existing AI applications that could improve the prevention, recognition, evaluation, and management of the most mission-critical conditions, including psychological and mental health, acute radiation sickness, surgical emergencies, spaceflight-associated neuro-ocular syndrome, infections, and cardiovascular deconditioning.RESULTS: Some examples of the applications we identified include AI chatbots designed to prevent and mitigate psychological and mental health conditions, automated medical imaging analysis, and closed-loop systems for hemodynamic optimization. We also discuss at length gaps in current technologies, as well as the key challenges and limitations of developing and deploying AI for space medicine to inform future research and innovation. Indeed, shifts in patient cohorts, space-induced physiological changes, limited size and breadth of space biomedical datasets, and changes in disease characteristics may render the models invalid when transferred from ground settings into space.Cheung HC, De Louche C, Komorowski M. Artificial intelligence applications in space medicine. Aerosp Med Hum Perform. 2023; 94(8):610-622.
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Hou X, Wang L, Zhu D, Guo L, Weng J, Zhang M, Zhou Z, Zou D, Ji Q, Guo X, Wu Q, Chen S, Yu R, Chen H, Huang Z, Zhang X, Wu J, Wu J, Jia W. Prevalence of diabetic retinopathy and vision-threatening diabetic retinopathy in adults with diabetes in China. Nat Commun 2023; 14:4296. [PMID: 37463878 DOI: 10.1038/s41467-023-39864-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Accepted: 06/28/2023] [Indexed: 07/20/2023] Open
Abstract
The current epidemic status of diabetic retinopathy in China is unclear. A national prevalence survey of diabetic complications was conducted. 50,564 participants with gradable non-mydriatic fundus photographs were enrolled. The prevalence rates (95% confidence intervals) of diabetic retinopathy and vision-threatening diabetic retinopathy were 16.3% (15.3%-17.2%) and 3.2% (2.9%-3.5%), significantly higher in the northern than in the southern regions. The differences in prevalence between those who had not attained a given metabolic goal and those who had were more pronounced for Hemoglobin A1c than for blood pressure and low-density lipoprotein cholesterol. The participants with vision-threatening diabetic retinopathy had significantly higher proportions of visual impairment and blindness than those with non-vision-threatening diabetic retinopathy. The likelihoods of diabetic retinopathy and vision-threatening diabetic retinopathy were also associated with education levels, household income, and multiple dietary intakes. Here, we show multi-level factors associated with the presence and the severity of diabetic retinopathy.
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Affiliation(s)
- Xuhong Hou
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai, China
| | - Limin Wang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Dalong Zhu
- Department of Endocrinology, Drum Tower Hospital Affiliated to Nanjing University Medical School, Nanjing, Jiangsu Province, China
| | - Lixin Guo
- Department of Endocrinology, Beijing Hospital, Beijing, China
| | - Jianping Weng
- Department of Endocrinology, the First Affiliated Hospital, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, Anhui Province, China
| | - Mei Zhang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Zhiguang Zhou
- Institute of Metabolism and Endocrinology, Key Laboratory of Diabetes Immunology, Ministry of Education, National Clinical Research Center for Metabolic Diseases, the Second Xiangya Hospital and the Diabetes Center, Central South University, Changsha, Hunan Province, China
| | - Dajin Zou
- Department of Endocrinology, Changhai Hospital, Second Military Medical University, Shanghai, China
| | - Qiuhe Ji
- Department of Endocrinology, Xijing Hospital, Xi'an, Shaanxi Province, China
| | - Xiaohui Guo
- Department of Endocrinology, Peking University First Hospital, Beijing, China
| | - Qiang Wu
- Department of Ophthalmology, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai, China
| | - Siyu Chen
- Department of Endocrinology and Metabolism, Suzhou Dushu Lake Hospital (Dushu Lake Hospital Affiliated to Soochow University), Suzhou, Jiangsu Province, China
| | - Rong Yu
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai, China
| | - Hongli Chen
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai, China
| | - Zhengjing Huang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Xiao Zhang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiarui Wu
- Key Laboratory of Systems Health Science of Zhejiang Province, Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, Zhejiang Province, China
- Center for Excellence in Molecular Science, Chinese Academy of Sciences, Shanghai, China
| | - Jing Wu
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China.
| | - Weiping Jia
- Department of Endocrinology and Metabolism, Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic Disease, Shanghai, China.
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Li Z, Wang L, Wu X, Jiang J, Qiang W, Xie H, Zhou H, Wu S, Shao Y, Chen W. Artificial intelligence in ophthalmology: The path to the real-world clinic. Cell Rep Med 2023:101095. [PMID: 37385253 PMCID: PMC10394169 DOI: 10.1016/j.xcrm.2023.101095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 04/17/2023] [Accepted: 06/07/2023] [Indexed: 07/01/2023]
Abstract
Artificial intelligence (AI) has great potential to transform healthcare by enhancing the workflow and productivity of clinicians, enabling existing staff to serve more patients, improving patient outcomes, and reducing health disparities. In the field of ophthalmology, AI systems have shown performance comparable with or even better than experienced ophthalmologists in tasks such as diabetic retinopathy detection and grading. However, despite these quite good results, very few AI systems have been deployed in real-world clinical settings, challenging the true value of these systems. This review provides an overview of the current main AI applications in ophthalmology, describes the challenges that need to be overcome prior to clinical implementation of the AI systems, and discusses the strategies that may pave the way to the clinical translation of these systems.
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Affiliation(s)
- Zhongwen Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
| | - Lei Wang
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Xuefang Wu
- Guizhou Provincial People's Hospital, Guizhou University, Guiyang 550002, China
| | - Jiewei Jiang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an 710121, China
| | - Wei Qiang
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - He Xie
- School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China
| | - Hongjian Zhou
- Department of Computer Science, University of Oxford, Oxford, Oxfordshire OX1 2JD, UK
| | - Shanjun Wu
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China
| | - Yi Shao
- Department of Ophthalmology, the First Affiliated Hospital of Nanchang University, Nanchang 330006, China.
| | - Wei Chen
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo 315000, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou 325027, China.
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Mohanty C, Mahapatra S, Acharya B, Kokkoras F, Gerogiannis VC, Karamitsos I, Kanavos A. Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy. SENSORS (BASEL, SWITZERLAND) 2023; 23:5726. [PMID: 37420891 DOI: 10.3390/s23125726] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 06/07/2023] [Accepted: 06/14/2023] [Indexed: 07/09/2023]
Abstract
Diabetic retinopathy (DR) is a common complication of long-term diabetes, affecting the human eye and potentially leading to permanent blindness. The early detection of DR is crucial for effective treatment, as symptoms often manifest in later stages. The manual grading of retinal images is time-consuming, prone to errors, and lacks patient-friendliness. In this study, we propose two deep learning (DL) architectures, a hybrid network combining VGG16 and XGBoost Classifier, and the DenseNet 121 network, for DR detection and classification. To evaluate the two DL models, we preprocessed a collection of retinal images obtained from the APTOS 2019 Blindness Detection Kaggle Dataset. This dataset exhibits an imbalanced image class distribution, which we addressed through appropriate balancing techniques. The performance of the considered models was assessed in terms of accuracy. The results showed that the hybrid network achieved an accuracy of 79.50%, while the DenseNet 121 model achieved an accuracy of 97.30%. Furthermore, a comparative analysis with existing methods utilizing the same dataset revealed the superior performance of the DenseNet 121 network. The findings of this study demonstrate the potential of DL architectures for the early detection and classification of DR. The superior performance of the DenseNet 121 model highlights its effectiveness in this domain. The implementation of such automated methods can significantly improve the efficiency and accuracy of DR diagnosis, benefiting both healthcare providers and patients.
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Affiliation(s)
- Cheena Mohanty
- Department of Electronics and Telecommunication, Biju Patnaik University of Technology, Rourkela 769012, Odisha, India
| | - Sakuntala Mahapatra
- Department of Electronics and Telecommunication Engineering, Trident Academy of Technology, Bhubaneswar 751016, Odisha, India
| | - Biswaranjan Acharya
- Department of Computer Engineering-AI, Marwadi University, Rajkot 360003, Gujarat, India
| | - Fotis Kokkoras
- Department of Digital Systems, University of Thessaly, 41500 Larissa, Greece
| | | | - Ioannis Karamitsos
- Department of Graduate and Research, Rochester Institute of Technology, Dubai 341055, United Arab Emirates
| | - Andreas Kanavos
- Department of Informatics, Ionian University, 49100 Corfu, Greece
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Wang Z, Li Z, Li K, Mu S, Zhou X, Di Y. Performance of artificial intelligence in diabetic retinopathy screening: a systematic review and meta-analysis of prospective studies. Front Endocrinol (Lausanne) 2023; 14:1197783. [PMID: 37383397 PMCID: PMC10296189 DOI: 10.3389/fendo.2023.1197783] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Accepted: 05/23/2023] [Indexed: 06/30/2023] Open
Abstract
Aims To systematically evaluate the diagnostic value of an artificial intelligence (AI) algorithm model for various types of diabetic retinopathy (DR) in prospective studies over the previous five years, and to explore the factors affecting its diagnostic effectiveness. Materials and methods A search was conducted in Cochrane Library, Embase, Web of Science, PubMed, and IEEE databases to collect prospective studies on AI models for the diagnosis of DR from January 2017 to December 2022. We used QUADAS-2 to evaluate the risk of bias in the included studies. Meta-analysis was performed using MetaDiSc and STATA 14.0 software to calculate the combined sensitivity, specificity, positive likelihood ratio, and negative likelihood ratio of various types of DR. Diagnostic odds ratios, summary receiver operating characteristic (SROC) plots, coupled forest plots, and subgroup analysis were performed according to the DR categories, patient source, region of study, and quality of literature, image, and algorithm. Results Finally, 21 studies were included. Meta-analysis showed that the pooled sensitivity, specificity, pooled positive likelihood ratio, pooled negative likelihood ratio, area under the curve, Cochrane Q index, and pooled diagnostic odds ratio of AI model for the diagnosis of DR were 0.880 (0.875-0.884), 0.912 (0.99-0.913), 13.021 (10.738-15.789), 0.083 (0.061-0.112), 0.9798, 0.9388, and 206.80 (124.82-342.63), respectively. The DR categories, patient source, region of study, sample size, quality of literature, image, and algorithm may affect the diagnostic efficiency of AI for DR. Conclusion AI model has a clear diagnostic value for DR, but it is influenced by many factors that deserve further study. Systematic review registration https://www.crd.york.ac.uk/prospero/, identifier CRD42023389687.
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Yusuf IH, Charbel Issa P, Ahn SJ. Unmet needs and future perspectives in hydroxychloroquine retinopathy. Front Med (Lausanne) 2023; 10:1196815. [PMID: 37359010 PMCID: PMC10288184 DOI: 10.3389/fmed.2023.1196815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Accepted: 05/17/2023] [Indexed: 06/28/2023] Open
Abstract
Retinopathy is a well-recognized toxic effect of hydroxychloroquine treatment. As hydroxychloroquine retinopathy is potentially a vision-threatening condition, early detection is imperative to minimize vision loss due to drug toxicity. However, early detection of hydroxychloroquine retinopathy is still challenging even with modern retinal imaging techniques. No treatment has been established for this condition, except for drug cessation to minimize further damage. In this perspective article, we aimed to summarize the knowledge gaps and unmet needs in current clinical practice and research in hydroxychloroquine retinopathy. The information presented in this article may help guide the future directions of screening practices and research in hydroxychloroquine retinopathy.
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Affiliation(s)
- Imran H. Yusuf
- Oxford Eye Hospital and Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Peter Charbel Issa
- Oxford Eye Hospital and Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, United Kingdom
| | - Seong Joon Ahn
- Department of Ophthalmology, Hanyang University Hospital, Hanyang University College of Medicine, Seoul, Republic of Korea
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50
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Zhao PY, Bommakanti N, Yu G, Aaberg MT, Patel TP, Paulus YM. Deep learning for automated detection of neovascular leakage on ultra-widefield fluorescein angiography in diabetic retinopathy. Sci Rep 2023; 13:9165. [PMID: 37280345 DOI: 10.1038/s41598-023-36327-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/01/2023] [Indexed: 06/08/2023] Open
Abstract
Diabetic retinopathy is a leading cause of blindness in working-age adults worldwide. Neovascular leakage on fluorescein angiography indicates progression to the proliferative stage of diabetic retinopathy, which is an important distinction that requires timely ophthalmic intervention with laser or intravitreal injection treatment to reduce the risk of severe, permanent vision loss. In this study, we developed a deep learning algorithm to detect neovascular leakage on ultra-widefield fluorescein angiography images obtained from patients with diabetic retinopathy. The algorithm, an ensemble of three convolutional neural networks, was able to accurately classify neovascular leakage and distinguish this disease marker from other angiographic disease features. With additional real-world validation and testing, our algorithm could facilitate identification of neovascular leakage in the clinical setting, allowing timely intervention to reduce the burden of blinding diabetic eye disease.
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Affiliation(s)
- Peter Y Zhao
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Nikhil Bommakanti
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Gina Yu
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Michael T Aaberg
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Tapan P Patel
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Yannis M Paulus
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA.
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