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Li Z, Wang L, Qiang W, Chen K, Wang Z, Zhang Y, Xie H, Wu S, Jiang J, Chen W. DeepMonitoring: a deep learning-based monitoring system for assessing the quality of cornea images captured by smartphones. Front Cell Dev Biol 2024; 12:1447067. [PMID: 39258227 PMCID: PMC11385315 DOI: 10.3389/fcell.2024.1447067] [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: 06/11/2024] [Accepted: 08/19/2024] [Indexed: 09/12/2024] Open
Abstract
Smartphone-based artificial intelligence (AI) diagnostic systems could assist high-risk patients to self-screen for corneal diseases (e.g., keratitis) instead of detecting them in traditional face-to-face medical practices, enabling the patients to proactively identify their own corneal diseases at an early stage. However, AI diagnostic systems have significantly diminished performance in low-quality images which are unavoidable in real-world environments (especially common in patient-recorded images) due to various factors, hindering the implementation of these systems in clinical practice. Here, we construct a deep learning-based image quality monitoring system (DeepMonitoring) not only to discern low-quality cornea images created by smartphones but also to identify the underlying factors contributing to the generation of such low-quality images, which can guide operators to acquire high-quality images in a timely manner. This system performs well across validation, internal, and external testing sets, with AUCs ranging from 0.984 to 0.999. DeepMonitoring holds the potential to filter out low-quality cornea images produced by smartphones, facilitating the application of smartphone-based AI diagnostic systems in real-world clinical settings, especially in the context of self-screening for corneal diseases.
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Affiliation(s)
- Zhongwen Li
- Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Lei Wang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Wei Qiang
- Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China
| | - Kuan Chen
- Cangnan Hospital, Wenzhou Medical University, Wenzhou, China
| | - Zhouqian Wang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Yi Zhang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, China
| | - He Xie
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Shanjun Wu
- Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China
| | - Jiewei Jiang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, China
| | - Wei Chen
- Ningbo Key Laboratory of Medical Research on Blinding Eye Diseases, Ningbo Eye Institute, Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, China
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Li F, Wang D, Yang Z, Zhang Y, Jiang J, Liu X, Kong K, Zhou F, Tham CC, Medeiros F, Han Y, Grzybowski A, Zangwill LM, Lam DSC, Zhang X. The AI revolution in glaucoma: Bridging challenges with opportunities. Prog Retin Eye Res 2024; 103:101291. [PMID: 39186968 DOI: 10.1016/j.preteyeres.2024.101291] [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: 04/29/2024] [Revised: 08/19/2024] [Accepted: 08/19/2024] [Indexed: 08/28/2024]
Abstract
Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the "black box" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.
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Affiliation(s)
- Fei Li
- 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.
| | - Deming Wang
- 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.
| | - Zefeng Yang
- 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.
| | - Yinhang Zhang
- 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.
| | - Jiaxuan Jiang
- 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.
| | - Xiaoyi Liu
- 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.
| | - Kangjie Kong
- 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.
| | - Fengqi Zhou
- Ophthalmology, Mayo Clinic Health System, Eau Claire, WI, USA.
| | - Clement C Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Felipe Medeiros
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Ying Han
- University of California, San Francisco, Department of Ophthalmology, San Francisco, CA, USA; The Francis I. Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, CA, USA.
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
| | - Linda M Zangwill
- Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, CA, USA.
| | - Dennis S C Lam
- The International Eye Research Institute of the Chinese University of Hong Kong (Shenzhen), Shenzhen, China; The C-MER Dennis Lam & Partners Eye Center, C-MER International Eye Care Group, Hong Kong, China.
| | - Xiulan Zhang
- 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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Scharre A, Scholler D, Gesell-May S, Müller T, Zablotski Y, Ertel W, May A. Comparison of veterinarians and a deep learning tool in the diagnosis of equine ophthalmic diseases. Equine Vet J 2024. [PMID: 38567426 DOI: 10.1111/evj.14087] [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: 08/14/2023] [Accepted: 02/25/2024] [Indexed: 04/04/2024]
Abstract
BACKGROUND/OBJECTIVES The aim was to compare ophthalmic diagnoses made by veterinarians to a deep learning (artificial intelligence) software tool which was developed to aid in the diagnosis of equine ophthalmic diseases. As equine ophthalmology is a very specialised field in equine medicine, the tool may be able to help in diagnosing equine ophthalmic emergencies such as uveitis. STUDY DESIGN In silico tool development and assessment of diagnostic performance. METHODS A deep learning tool which was developed and trained for classification of equine ophthalmic diseases was tested with 40 photographs displaying various equine ophthalmic diseases. The same data set was shown to different groups of veterinarians (equine, small animal, mixed practice, other) using an opinion poll to compare the results and evaluate the performance of the programme. Convolutional Neural Networks (CNN) were trained on 2346 photographs of equine eyes, which were augmented to 9384 images. Two hundred and sixty-one separate unmodified images were used to evaluate the trained network. The trained deep learning tool was used on 40 photographs of equine eyes (10 healthy, 12 uveitis, 18 other diseases). An opinion poll was used to evaluate the diagnostic performance of 148 veterinarians in comparison to the software tool. RESULTS The probability for the correct answer was 93% for the AI programme. Equine veterinarians answered correctly in 76%, whereas other veterinarians reached 67% probability for the correct diagnosis. MAIN LIMITATIONS Diagnosis was solely based on images of equine eyes without the possibility to evaluate the inner eye. CONCLUSIONS The deep learning tool proved to be at least equivalent to veterinarians in assessing ophthalmic diseases in photographs. We therefore conclude that the software tool may be useful in detecting potential emergency cases. In this context, blindness in horses may be prevented as the horse can receive accurate treatment or can be sent to an equine hospital. Furthermore, the tool gives less experienced veterinarians the opportunity to differentiate between uveitis and other ocular anterior segment disease and to support them in their decision-making regarding treatment.
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Affiliation(s)
- Annabel Scharre
- Equine Clinic, Ludwig Maximilians University, Oberschleissheim, Germany
| | - Dominik Scholler
- Equine Clinic, Ludwig Maximilians University, Oberschleissheim, Germany
| | | | | | - Yury Zablotski
- Clinic for Ruminants, Ludwig Maximilians University, Oberschleissheim, Germany
| | - Wolfgang Ertel
- Institute for Artificial Intelligence, Ravensburg-Weingarten University, Weingarten, Germany
| | - Anna May
- Equine Clinic, Ludwig Maximilians University, Oberschleissheim, Germany
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Hanif A, Prajna NV, Lalitha P, NaPier E, Parker M, Steinkamp P, Keenan JD, Campbell JP, Song X, Redd TK. Assessing the Impact of Image Quality on Deep Learning Classification of Infectious Keratitis. OPHTHALMOLOGY SCIENCE 2023; 3:100331. [PMID: 37920421 PMCID: PMC10618822 DOI: 10.1016/j.xops.2023.100331] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Revised: 04/13/2023] [Accepted: 05/08/2023] [Indexed: 11/04/2023]
Abstract
Objective To investigate the impact of corneal photograph quality on convolutional neural network (CNN) predictions. Design A CNN trained to classify bacterial and fungal keratitis was evaluated using photographs of ulcers labeled according to 5 corneal image quality parameters: eccentric gaze direction, abnormal eyelid position, over/under-exposure, inadequate focus, and malpositioned light reflection. Participants All eligible subjects with culture and stain-proven bacterial and/or fungal ulcers presenting to Aravind Eye Hospital in Madurai, India, between January 1, 2021 and December 31, 2021. Methods Convolutional neural network classification performance was compared for each quality parameter, and gradient class activation heatmaps were generated to visualize regions of highest influence on CNN predictions. Main Outcome Measures Area under the receiver operating characteristic and precision recall curves were calculated to quantify model performance. Bootstrapped confidence intervals were used for statistical comparisons. Logistic loss was calculated to measure individual prediction accuracy. Results Individual presence of either light reflection or eyelids obscuring the corneal surface was associated with significantly higher CNN performance. No other quality parameter significantly influenced CNN performance. Qualitative review of gradient class activation heatmaps generally revealed the infiltrate as having the highest diagnostic relevance. Conclusions The CNN demonstrated expert-level performance regardless of image quality. Future studies may investigate use of smartphone cameras and image sets with greater variance in image quality to further explore the influence of these parameters on model performance. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Adam Hanif
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | | | | | - Erin NaPier
- John A. Burns School of Medicine, University of Hawai'i, Honolulu, Hawaii
| | - Maria Parker
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Peter Steinkamp
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Jeremy D. Keenan
- Francis I. Proctor Foundation, University of California, San Francisco, San Francisco, California
| | - J. Peter Campbell
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Xubo Song
- Department of Medical Informatics and Clinical Epidemiology and Program of Computer Science and Electrical Engineering, Oregon Health & Science University, Portland, Oregon
| | - Travis K. Redd
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
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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: 8] [Impact Index Per Article: 8.0] [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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Li Z, Chen W. Solving data quality issues of fundus images in real-world settings by ophthalmic AI. Cell Rep Med 2023; 4:100951. [PMID: 36812885 PMCID: PMC9975325 DOI: 10.1016/j.xcrm.2023.100951] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
Liu et al.1 develop a deep-learning-based flow cytometry-like image quality classifier, DeepFundus, for the automated, high-throughput, and multidimensional classification of fundus image quality. DeepFundus significantly improves the real-world performance of established artificial intelligence diagnostics in detecting multiple retinopathies.
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Affiliation(s)
- Zhongwen Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, China
| | - Wei Chen
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, China; School of Ophthalmology and Optometry and Eye Hospital, Wenzhou Medical University, Wenzhou, China.
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Li Z, Qiang W, Chen H, Pei M, Yu X, Wang L, Li Z, Xie W, Wu X, Jiang J, Wu G. Artificial intelligence to detect malignant eyelid tumors from photographic images. NPJ Digit Med 2022; 5:23. [PMID: 35236921 PMCID: PMC8891262 DOI: 10.1038/s41746-022-00571-3] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Accepted: 02/04/2022] [Indexed: 11/23/2022] Open
Abstract
Malignant eyelid tumors can invade adjacent structures and pose a threat to vision and even life. Early identification of malignant eyelid tumors is crucial to avoiding substantial morbidity and mortality. However, differentiating malignant eyelid tumors from benign ones can be challenging for primary care physicians and even some ophthalmologists. Here, based on 1,417 photographic images from 851 patients across three hospitals, we developed an artificial intelligence system using a faster region-based convolutional neural network and deep learning classification networks to automatically locate eyelid tumors and then distinguish between malignant and benign eyelid tumors. The system performed well in both internal and external test sets (AUCs ranged from 0.899 to 0.955). The performance of the system is comparable to that of a senior ophthalmologist, indicating that this system has the potential to be used at the screening stage for promoting the early detection and treatment of malignant eyelid tumors.
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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.
| | - Wei Qiang
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, 315000, China
| | - Hongyun Chen
- Zunyi First People's Hospital, Zunyi Medical University, Zunyi, 563000, China
| | - Mengjie Pei
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China
| | - Xiaomei Yu
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, 315000, China
| | - Layi Wang
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, 315000, China
| | - Zhen Li
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, 315000, China
| | - Weiwei Xie
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, 315000, China
| | - Xuefang Wu
- Guizhou Provincial People's Hospital, Guizhou University, Guizhou, 550002, China
| | - Jiewei Jiang
- School of Electronic Engineering, Xi'an University of Posts and Telecommunications, Xi'an, 710121, China.
| | - Guohai Wu
- Ningbo Eye Hospital, Wenzhou Medical University, Ningbo, 315000, China.
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