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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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2
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Toto L, Romano A, Pavan M, Degl'Innocenti D, Olivotto V, Formenti F, Viggiano P, Midena E, Mastropasqua R. A deep learning approach to hard exudates detection and disorganization of retinal inner layers identification on OCT images. Sci Rep 2024; 14:16652. [PMID: 39030181 DOI: 10.1038/s41598-024-63844-9] [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: 01/11/2024] [Accepted: 06/03/2024] [Indexed: 07/21/2024] Open
Abstract
The purpose of the study was to detect Hard Exudates (HE) and classify Disorganization of Retinal Inner Layers (DRIL) implementing a Deep Learning (DL) system on optical coherence tomography (OCT) images of eyes with diabetic macular edema (DME). We collected a dataset composed of 442 OCT images on which we annotated 6847 HE and the presence of DRIL. A complex operational pipeline was defined to implement data cleaning and image transformations, and train two DL models. The state-of-the-art neural network architectures (Yolov7, ConvNeXt, RegNetX) and advanced techniques were exploited to aggregate the results (Ensemble learning, Edge detection) and obtain a final model. The DL approach reached good performance in detecting HE and classifying DRIL. Regarding HE detection the model got an AP@0.5 score equal to 34.4% with Precision of 48.7% and Recall of 43.1%; while for DRIL classification an Accuracy of 91.1% with Sensitivity and Specificity both of 91.1% and AUC and AUPR values equal to 91% were obtained. The P-value was lower than 0.05 and the Kappa coefficient was 0.82. The DL models proved to be able to identify HE and DRIL in eyes with DME with a very good accuracy and all the metrics calculated confirmed the system performance. Our DL approach demonstrated to be a good candidate as a supporting tool for ophthalmologists in OCT images analysis.
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Affiliation(s)
- Lisa Toto
- Ophthalmology Clinic, Department of Medicine and Ageing Science, University "G. D'Annunzio" of Chieti-Pescara, Via Dei Vestini Snc, 66100, Chieti, Italy
| | - Anna Romano
- Ophthalmology Clinic, Department of Medicine and Ageing Science, University "G. D'Annunzio" of Chieti-Pescara, Via Dei Vestini Snc, 66100, Chieti, Italy.
| | - Marco Pavan
- Datamantix S.R.L. Artificial Intelligence Company, Via Paolo Sarpi, 14/15, 33100, Udine, Italy
| | - Dante Degl'Innocenti
- Datamantix S.R.L. Artificial Intelligence Company, Via Paolo Sarpi, 14/15, 33100, Udine, Italy
| | - Valentina Olivotto
- Datamantix S.R.L. Artificial Intelligence Company, Via Paolo Sarpi, 14/15, 33100, Udine, Italy
| | - Federico Formenti
- Ophthalmology Clinic, Department of Medicine and Ageing Science, University "G. D'Annunzio" of Chieti-Pescara, Via Dei Vestini Snc, 66100, Chieti, Italy
| | - Pasquale Viggiano
- Ophthalmology Clinic, Department of Translational Biomedicine Neuroscience, University of Bari "Aldo Moro", Bari, Italy
| | - Edoardo Midena
- Department of Ophthalmology, University of Padova, 35128, Padova, Italy
- IRCCS- Fondazione Bietti, 00198, Roma, Italy
| | - Rodolfo Mastropasqua
- Ophthalmology Clinic, Department of Neuroscience, Imaging and Clinical Science, "G. D'Annunzio" University of Chieti-Pescara, Via Dei Vestini Snc, 66100, Chieti, Italy
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Wu H, Jin K, Yip CC, Koh V, Ye J. A systematic review of economic evaluation of artificial intelligence-based screening for eye diseases: From possibility to reality. Surv Ophthalmol 2024; 69:499-507. [PMID: 38492584 DOI: 10.1016/j.survophthal.2024.03.008] [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: 08/29/2023] [Revised: 03/04/2024] [Accepted: 03/12/2024] [Indexed: 03/18/2024]
Abstract
Artificial Intelligence (AI) has become a focus of research in the rapidly evolving field of ophthalmology. Nevertheless, there is a lack of systematic studies on the health economics of AI in this field. We examine studies from the PubMed, Google Scholar, and Web of Science databases that employed quantitative analysis, retrieved up to July 2023. Most of the studies indicate that AI leads to cost savings and improved efficiency in ophthalmology. On the other hand, some studies suggest that using AI in healthcare may raise costs for patients, especially when taking into account factors such as labor costs, infrastructure, and patient adherence. Future research should cover a wider range of ophthalmic diseases beyond common eye conditions. Moreover, conducting extensive health economic research, designed to collect data relevant to its own context, is imperative.
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Affiliation(s)
- Hongkang Wu
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kai Jin
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chee Chew Yip
- Department of Ophthalmology & Visual Sciences, Khoo Teck Puat Hospital, Singapore, Singapore
| | - Victor Koh
- Department of Ophthalmology, National University Hospital, National University of Singapore, Singapore
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
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Melo GB, Nakayama LF, Cardoso VS, Dos Santos LA, Malerbi FK. Synchronous Diagnosis of Diabetic Retinopathy by a Handheld Retinal Camera, Artificial Intelligence, and Simultaneous Specialist Confirmation. Ophthalmol Retina 2024:S2468-6530(24)00236-7. [PMID: 38750937 DOI: 10.1016/j.oret.2024.05.009] [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: 12/11/2023] [Revised: 05/07/2024] [Accepted: 05/07/2024] [Indexed: 06/16/2024]
Abstract
PURPOSE Diabetic retinopathy (DR) is a leading cause of preventable blindness, particularly in underserved regions where access to ophthalmic care is limited. This study presents a proof of concept for utilizing a portable handheld retinal camera with an embedded artificial intelligence (AI) platform, complemented by a synchronous remote confirmation by retina specialists, for DR screening in an underserved rural area. DESIGN Retrospective cohort study. SUBJECTS A total of 1115 individuals with diabetes. METHODS A retrospective analysis of a screening initiative conducted in 4 municipalities in Northeastern Brazil, targeting the diabetic population. A portable handheld retinal camera captured macula-centered and disc-centered images, which were analyzed by the AI system. Immediate push notifications were sent out to retina specialists upon the detection of significant abnormalities, enabling synchronous verification and confirmation, with on-site patient feedback within minutes. Referral criteria were established, and all referred patients underwent a complete ophthalmic work-up and subsequent treatment. MAIN OUTCOME MEASURES Proof-of-concept implementation success. RESULTS Out of 2052 invited individuals, 1115 participated, with a mean age of 60.93 years and diabetes duration of 7.52 years; 66.03% were women. The screening covered 2222 eyes, revealing various retinal conditions. Referable eyes for DR were 11.84%, with an additional 13% for other conditions (diagnoses included various stages of DR, media opacity, nevus, drusen, enlarged cup-to-disc ratio, pigmentary changes, and other). Artificial intelligence performance for overall detection of referable cases (both DR and other conditions) was as follows: sensitivity 84.23% (95% confidence interval (CI), 82.63-85.84), specificity 80.79% (95% CI, 79.05-82.53). When we assessed whether AI matched any clinical diagnosis, be it referable or not, sensitivity was 85.67% (95% CI, 84.12-87.22), specificity was 98.86 (95% CI, 98.39-99.33), and area under the curve was 0.92 (95% CI, 0.91-0.94). CONCLUSIONS The integration of a portable device, AI analysis, and synchronous medical validation has the potential to play a crucial role in preventing blindness from DR, especially in socially unequal scenarios. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Gustavo Barreto Melo
- Department of Ophthalmology, Federal University of São Paulo, São Paulo-SP, Brazil; Hospital de Olhos de Sergipe, Aracaju-SE, Brazil; Retina Clinic, São Paulo-SP, Brazil.
| | - Luis Filipe Nakayama
- Department of Ophthalmology, Federal University of São Paulo, São Paulo-SP, Brazil; Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, Massachusetts
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Wang Y, Zhen L, Tan TE, Fu H, Feng Y, Wang Z, Xu X, Goh RSM, Ng Y, Calhoun C, Tan GSW, Sun JK, Liu Y, Ting DSW. Geometric Correspondence-Based Multimodal Learning for Ophthalmic Image Analysis. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:1945-1957. [PMID: 38206778 DOI: 10.1109/tmi.2024.3352602] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Color fundus photography (CFP) and Optical coherence tomography (OCT) images are two of the most widely used modalities in the clinical diagnosis and management of retinal diseases. Despite the widespread use of multimodal imaging in clinical practice, few methods for automated diagnosis of eye diseases utilize correlated and complementary information from multiple modalities effectively. This paper explores how to leverage the information from CFP and OCT images to improve the automated diagnosis of retinal diseases. We propose a novel multimodal learning method, named geometric correspondence-based multimodal learning network (GeCoM-Net), to achieve the fusion of CFP and OCT images. Specifically, inspired by clinical observations, we consider the geometric correspondence between the OCT slice and the CFP region to learn the correlated features of the two modalities for robust fusion. Furthermore, we design a new feature selection strategy to extract discriminative OCT representations by automatically selecting the important feature maps from OCT slices. Unlike the existing multimodal learning methods, GeCoM-Net is the first method that formulates the geometric relationships between the OCT slice and the corresponding region of the CFP image explicitly for CFP and OCT fusion. Experiments have been conducted on a large-scale private dataset and a publicly available dataset to evaluate the effectiveness of GeCoM-Net for diagnosing diabetic macular edema (DME), impaired visual acuity (VA) and glaucoma. The empirical results show that our method outperforms the current state-of-the-art multimodal learning methods by improving the AUROC score 0.4%, 1.9% and 2.9% for DME, VA and glaucoma detection, respectively.
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Xu X, Liu D, Huang G, Wang M, Lei M, Jia Y. Computer aided diagnosis of diabetic retinopathy based on multi-view joint learning. Comput Biol Med 2024; 174:108428. [PMID: 38631117 DOI: 10.1016/j.compbiomed.2024.108428] [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: 08/08/2023] [Revised: 04/02/2024] [Accepted: 04/04/2024] [Indexed: 04/19/2024]
Abstract
Diabetic retinopathy (DR) is a kind of ocular complication of diabetes, and its degree grade is an essential basis for early diagnosis of patients. Manual diagnosis is a long and expensive process with a specific risk of misdiagnosis. Computer-aided diagnosis can provide more accurate and practical treatment recommendations. In this paper, we propose a multi-view joint learning DR diagnostic model called RT2Net, which integrates the global features of fundus images and the local detailed features of vascular images to reduce the limitations of single fundus image learning. Firstly, the original image is preprocessed using operations such as contrast-limited adaptive histogram equalization, and the vascular structure of the extracted DR image is segmented. Then, the vascular image and fundus image are input into two branch networks of RT2Net for feature extraction, respectively, and the feature fusion module adaptively fuses the feature vectors' output from the branch networks. Finally, the optimized classification model is used to identify the five categories of DR. This paper conducts extensive experiments on the public datasets EyePACS and APTOS 2019 to demonstrate the method's effectiveness. The accuracy of RT2Net on the two datasets reaches 88.2% and 85.4%, and the area under the receiver operating characteristic curve (AUC) is 0.98 and 0.96, respectively. The excellent classification ability of RT2Net for DR can significantly help patients detect and treat lesions early and provide doctors with a more reliable diagnosis basis, which has significant clinical value for diagnosing DR.
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Affiliation(s)
- Xuebin Xu
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an 710121, Shaanxi, China; Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China.
| | - Dehua Liu
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an 710121, Shaanxi, China; Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China.
| | - Guohua Huang
- Weinan Central Hospital, Xi'an 714099, Shaanxi, China.
| | - Muyu Wang
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an 710121, Shaanxi, China; Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China.
| | - Meng Lei
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an 710121, Shaanxi, China; Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China.
| | - Yang Jia
- School of Computer Science and Technology, Xi'an University of Posts and Telecommunications, Xi'an 710121, Shaanxi, China; Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing, Xi'an 710121, Shaanxi, China; Xi'an Key Laboratory of Big Data and Intelligent Computing, Xi'an 710121, Shaanxi, China.
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7
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Bhulakshmi D, Rajput DS. A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images. PeerJ Comput Sci 2024; 10:e1947. [PMID: 38699206 PMCID: PMC11065411 DOI: 10.7717/peerj-cs.1947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 02/28/2024] [Indexed: 05/05/2024]
Abstract
Diabetic retinopathy (DR) is the leading cause of visual impairment globally. It occurs due to long-term diabetes with fluctuating blood glucose levels. It has become a significant concern for people in the working age group as it can lead to vision loss in the future. Manual examination of fundus images is time-consuming and requires much effort and expertise to determine the severity of the retinopathy. To diagnose and evaluate the disease, deep learning-based technologies have been used, which analyze blood vessels, microaneurysms, exudates, macula, optic discs, and hemorrhages also used for initial detection and grading of DR. This study examines the fundamentals of diabetes, its prevalence, complications, and treatment strategies that use artificial intelligence methods such as machine learning (ML), deep learning (DL), and federated learning (FL). The research covers future studies, performance assessments, biomarkers, screening methods, and current datasets. Various neural network designs, including recurrent neural networks (RNNs), generative adversarial networks (GANs), and applications of ML, DL, and FL in the processing of fundus images, such as convolutional neural networks (CNNs) and their variations, are thoroughly examined. The potential research methods, such as developing DL models and incorporating heterogeneous data sources, are also outlined. Finally, the challenges and future directions of this research are discussed.
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Affiliation(s)
- Dasari Bhulakshmi
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Dharmendra Singh Rajput
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, Tamil Nadu, India
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Russo C, Bria A, Marrocco C. GravityNet for end-to-end small lesion detection. Artif Intell Med 2024; 150:102842. [PMID: 38553147 DOI: 10.1016/j.artmed.2024.102842] [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: 10/27/2023] [Revised: 03/01/2024] [Accepted: 03/11/2024] [Indexed: 04/02/2024]
Abstract
This paper introduces a novel one-stage end-to-end detector specifically designed to detect small lesions in medical images. Precise localization of small lesions presents challenges due to their appearance and the diverse contextual backgrounds in which they are found. To address this, our approach introduces a new type of pixel-based anchor that dynamically moves towards the targeted lesion for detection. We refer to this new architecture as GravityNet, and the novel anchors as gravity points since they appear to be "attracted" by the lesions. We conducted experiments on two well-established medical problems involving small lesions to evaluate the performance of the proposed approach: microcalcifications detection in digital mammograms and microaneurysms detection in digital fundus images. Our method demonstrates promising results in effectively detecting small lesions in these medical imaging tasks.
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Affiliation(s)
- Ciro Russo
- Department of Electrical and Information Engineering, University of Cassino and L.M., Via G. Di Biasio 43, 03043 Cassino (FR), Italy.
| | - Alessandro Bria
- Department of Electrical and Information Engineering, University of Cassino and L.M., Via G. Di Biasio 43, 03043 Cassino (FR), Italy.
| | - Claudio Marrocco
- Department of Electrical and Information Engineering, University of Cassino and L.M., Via G. Di Biasio 43, 03043 Cassino (FR), Italy.
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Zang F, Ma H. CRA-Net: Transformer guided category-relation attention network for diabetic retinopathy grading. Comput Biol Med 2024; 170:107993. [PMID: 38277925 DOI: 10.1016/j.compbiomed.2024.107993] [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: 08/24/2023] [Revised: 12/30/2023] [Accepted: 01/13/2024] [Indexed: 01/28/2024]
Abstract
Automated grading of diabetic retinopathy (DR) is an important means for assisting clinical diagnosis and preventing further retinal damage. However, imbalances and similarities between categories in the DR dataset make it highly challenging to accurately grade the severity of the condition. Furthermore, DR images encompass various lesions, and the pathological relationship information among these lesions can be easily overlooked. For instance, under different severity levels, the varying contributions of different lesions to accurate model grading differ significantly. To address the aforementioned issues, we design a transformer guided category-relation attention network (CRA-Net). Specifically, we propose a novel category attention block that enhances feature information within the class from the perspective of DR image categories, thereby alleviating class imbalance problems. Additionally, we design a lesion relation attention block that captures relationships between lesions by incorporating attention mechanisms in two primary aspects: capsule attention models the relative importance of different lesions, allowing the model to focus on more "informative" ones. Spatial attention captures the global position relationship between lesion features under transformer guidance, facilitating more accurate localization of lesions. Experimental and ablation studies on two datasets DDR and APTOS 2019 demonstrate the effectiveness of CRA-Net and obtain competitive performance.
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Affiliation(s)
- Feng Zang
- School of Electronic Engineering, Heilongjiang University, Harbin 150080, China.
| | - Hui Ma
- School of Electronic Engineering, Heilongjiang University, Harbin 150080, China.
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Liu J, Xu S, He P, Wu S, Luo X, Deng Y, Huang H. VSG-GAN: A high-fidelity image synthesis method with semantic manipulation in retinal fundus image. Biophys J 2024:S0006-3495(24)00139-5. [PMID: 38414236 DOI: 10.1016/j.bpj.2024.02.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2023] [Revised: 01/29/2024] [Accepted: 02/22/2024] [Indexed: 02/29/2024] Open
Abstract
In recent years, advancements in retinal image analysis, driven by machine learning and deep learning techniques, have enhanced disease detection and diagnosis through automated feature extraction. However, challenges persist, including limited data set diversity due to privacy concerns and imbalanced sample pairs, hindering effective model training. To address these issues, we introduce the vessel and style guided generative adversarial network (VSG-GAN), an innovative algorithm building upon the foundational concept of GAN. In VSG-GAN, a generator and discriminator engage in an adversarial process to produce realistic retinal images. Our approach decouples retinal image generation into distinct modules: the vascular skeleton and background style. Leveraging style transformation and GAN inversion, our proposed hierarchical variational autoencoder module generates retinal images with diverse morphological traits. In addition, the spatially adaptive denormalization module ensures consistency between input and generated images. We evaluate our model on MESSIDOR and RITE data sets using various metrics, including structural similarity index measure, inception score, Fréchet inception distance, and kernel inception distance. Our results demonstrate the superiority of VSG-GAN, outperforming existing methods across all evaluation assessments. This underscores its effectiveness in addressing data set limitations and imbalances. Our algorithm provides a novel solution to challenges in retinal image analysis by offering diverse and realistic retinal image generation. Implementing the VSG-GAN augmentation approach on downstream diabetic retinopathy classification tasks has shown enhanced disease diagnosis accuracy, further advancing the utility of machine learning in this domain.
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Affiliation(s)
- Junjie Liu
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Zhuhai, China; BNU-HKBU United International College, Zhuhai, China; Faculty of Science, Hong Kong Baptist University, Hong Kong SAR, China; Trinity College Dublin, Dublin 2, Ireland
| | - Shixin Xu
- Data Science Research Center, Duke Kunshan University, Kunshan, Jiangsu, China
| | - Ping He
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Zhuhai, China; BNU-HKBU United International College, Zhuhai, China
| | - Sirong Wu
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Zhuhai, China; BNU-HKBU United International College, Zhuhai, China; Faculty of Science, Hong Kong Baptist University, Hong Kong SAR, China
| | - Xi Luo
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Zhuhai, China; BNU-HKBU United International College, Zhuhai, China; Faculty of Science, Hong Kong Baptist University, Hong Kong SAR, China
| | - Yuhui Deng
- Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Zhuhai, China; BNU-HKBU United International College, Zhuhai, China.
| | - Huaxiong Huang
- Research Center for Mathematics, Beijing Normal University, Zhuhai, China; Guangdong Provincial Key Laboratory of Interdisciplinary Research and Application for Data Science, Zhuhai, China; Department of Mathematics and Statistics, York University, Toronto, ON, Canada.
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11
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Lv Y, Zhai C, Sun G, He Y. Chitosan as a promising materials for the construction of nanocarriers for diabetic retinopathy: an updated review. J Biol Eng 2024; 18:18. [PMID: 38388386 PMCID: PMC10885467 DOI: 10.1186/s13036-024-00414-7] [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: 12/17/2023] [Accepted: 02/07/2024] [Indexed: 02/24/2024] Open
Abstract
Diabetic retinopathy (DR) is a condition that causes swelling of the blood vessels of the retina and leaks blood and fluids. It is the most severe form of diabetic eye disease. It causes vision loss in its advanced stage. Diabetic retinopathy is responsible for causing 26% of blindness. Very insufficient therapies are accessible for the treatment of DR. As compared to the conventional therapies, there should be enhanced research on the controlled release, shorter duration, and cost-effective therapy of diabetic retinopathy. The expansion of advanced nanocarriers-based drug delivery systems has been now employed to exploit as well as regulate the transport of many therapeutic agents to target sites via the increase in penetration or the extension of the duration of contact employing production by enclosing as well as distributing tiny molecules in nanostructured formulation. Various polymers have been utilized for the manufacturing of these nanostructured formulations. Chitosan possesses incredible biological and chemical properties, that have led to its extensive use in pharmaceutical and biomedical applications. Chitosan has been used in many studies because of its enhanced mucoadhesiveness and non-toxicity. Multiple studies have used chitosan as the best candidate for manufacturing nanocarriers and treating diabetic retinopathy. Numerous nanocarriers have been formulated by using chitosan such as nanostructured lipid carriers, solid lipid nanoparticles, liposomes, and dendrimers for treating diabetic retinopathy. This current review elaborates on the recent advancements of chitosan as a promising approach for the manufacturing of nanocarriers that can be used for treating diabetic retinopathy.
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Affiliation(s)
- Yan Lv
- Department of Ophthalmology, Jilin Province FAW General Hospital, Changchun, 130011, China
| | - Chenglei Zhai
- Department of Orthopaedics, Jilin Province FAW General Hospital, Changchun, 130011, China
| | - Gang Sun
- Department of General Surgery, Jilin Province FAW General Hospital, Changchun, 130011, China.
| | - Yangfang He
- Department of Endocrinology, the Second Hospital of Jilin University, Changchun, 130000, China
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12
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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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Skevas C, de Olaguer NP, Lleó A, Thiwa D, Schroeter U, Lopes IV, Mautone L, Linke SJ, Spitzer MS, Yap D, Xiao D. Implementing and evaluating a fully functional AI-enabled model for chronic eye disease screening in a real clinical environment. BMC Ophthalmol 2024; 24:51. [PMID: 38302908 PMCID: PMC10832120 DOI: 10.1186/s12886-024-03306-y] [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: 07/02/2023] [Accepted: 01/16/2024] [Indexed: 02/03/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) has the potential to increase the affordability and accessibility of eye disease screening, especially with the recent approval of AI-based diabetic retinopathy (DR) screening programs in several countries. METHODS This study investigated the performance, feasibility, and user experience of a seamless hardware and software solution for screening chronic eye diseases in a real-world clinical environment in Germany. The solution integrated AI grading for DR, age-related macular degeneration (AMD), and glaucoma, along with specialist auditing and patient referral decision. The study comprised several components: (1) evaluating the entire system solution from recruitment to eye image capture and AI grading for DR, AMD, and glaucoma; (2) comparing specialist's grading results with AI grading results; (3) gathering user feedback on the solution. RESULTS A total of 231 patients were recruited, and their consent forms were obtained. The sensitivity, specificity, and area under the curve for DR grading were 100.00%, 80.10%, and 90.00%, respectively. For AMD grading, the values were 90.91%, 78.79%, and 85.00%, and for glaucoma grading, the values were 93.26%, 76.76%, and 85.00%. The analysis of all false positive cases across the three diseases and their comparison with the final referral decisions revealed that only 17 patients were falsely referred among the 231 patients. The efficacy analysis of the system demonstrated the effectiveness of the AI grading process in the study's testing environment. Clinical staff involved in using the system provided positive feedback on the disease screening process, particularly praising the seamless workflow from patient registration to image transmission and obtaining the final result. Results from a questionnaire completed by 12 participants indicated that most found the system easy, quick, and highly satisfactory. The study also revealed room for improvement in the AMD model, suggesting the need to enhance its training data. Furthermore, the performance of the glaucoma model grading could be improved by incorporating additional measures such as intraocular pressure. CONCLUSIONS The implementation of the AI-based approach for screening three chronic eye diseases proved effective in real-world settings, earning positive feedback on the usability of the integrated platform from both the screening staff and auditors. The auditing function has proven valuable for obtaining efficient second opinions from experts, pointing to its potential for enhancing remote screening capabilities. TRIAL REGISTRATION Institutional Review Board of the Hamburg Medical Chamber (Ethik-Kommission der Ärztekammer Hamburg): 2021-10574-BO-ff.
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Affiliation(s)
- Christos Skevas
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | | | - Albert Lleó
- TeleMedC GmbH, Raboisen 32, 20095, Hamburg, Germany
| | - David Thiwa
- Department of Otorhinolaryngology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | - Ulrike Schroeter
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | - Inês Valente Lopes
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany.
| | - Luca Mautone
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | - Stephan J Linke
- Zentrum Sehestaerke, Martinistraße 64, 20251, Hamburg, Germany
| | - Martin Stephan Spitzer
- Department of Ophthalmology, University Medical Center Hamburg - Eppendorf, Martinistr. 52, 20249, Hamburg, Germany
| | - Daniel Yap
- TeleMedC Pty Ltd, 61 Ubi Avenue 1, #06-11 UBPoint, Singapore, 40894, Singapore
| | - Di Xiao
- TeleMedC Pty Ltd, Brisbane Technology Park, Level 2, 1 Westlink Court, Darra, QLD 4076, Australia
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Wang X, Fang J, Yang L. Research progress on ocular complications caused by type 2 diabetes mellitus and the function of tears and blepharons. Open Life Sci 2024; 19:20220773. [PMID: 38299009 PMCID: PMC10828665 DOI: 10.1515/biol-2022-0773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 09/20/2023] [Accepted: 10/19/2023] [Indexed: 02/02/2024] Open
Abstract
The purpose of this study was to explore the related research progress of ocular complications (OCs) caused by type 2 diabetes mellitus (T2DM), tear and tarsal function, and the application of deep learning (DL) in the diagnosis of diabetes and OCs caused by it, to provide reference for the prevention and control of OCs in T2DM patients. This study reviewed the pathogenesis and treatment of diabetes retinopathy, keratopathy, dry eye disease, glaucoma, and cataract, analyzed the relationship between OCs and tear function and tarsal function, and discussed the application value of DL in the diagnosis of diabetes and OCs. Diabetes retinopathy is related to hyperglycemia, angiogenic factors, oxidative stress, hypertension, hyperlipidemia, and other factors. The increase in water content in the corneal stroma leads to corneal relaxation, loss of transparency, and elasticity, and can lead to the occurrence of corneal lesions. Dry eye syndrome is related to abnormal stability of the tear film and imbalance in neural and immune regulation. Elevated intraocular pressure, inflammatory reactions, atrophy of the optic nerve head, and damage to optic nerve fibers are the causes of glaucoma. Cataract is a common eye disease in the elderly, which is a visual disorder caused by lens opacity. Oxidative stress is an important factor in the occurrence of cataracts. In clinical practice, blood sugar control, laser therapy, and drug therapy are used to control the above eye complications. The function of tear and tarsal plate will be affected by eye diseases. Retinopathy and dry eye disease caused by diabetes will cause dysfunction of tear and tarsal plate, which will affect the eye function of patients. Furthermore, DL can automatically diagnose and classify eye diseases, automatically analyze fundus images, and accurately diagnose diabetes retinopathy, macular degeneration, and other diseases by analyzing and processing eye images and data. The treatment of T2DM is difficult and prone to OCs, which seriously threatens the normal life of patients. The occurrence of OCs is closely related to abnormal tear and tarsal function. Based on DL, clinical diagnosis and treatment of diabetes and its OCs can be carried out, which has positive application value.
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Affiliation(s)
- Xiaohong Wang
- Department of Operating Room, Xinchang County Peoples Hospital, Xinchang, 312500, Shaoxing City, Zhejiang, China
| | - Jian Fang
- Department of Ophthalmolgy, Xinchang County Peoples Hospital, Xinchang, 312500, Shaoxing City, Zhejiang, China
| | - Lina Yang
- Department of Ophthalmolgy, Xinchang County Peoples Hospital, Xinchang, 312500, Shaoxing City, Zhejiang, China
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Dhirachaikulpanich D, Xie J, Chen X, Li X, Madhusudhan S, Zheng Y, Beare NAV. Using Deep Learning to Segment Retinal Vascular Leakage and Occlusion in Retinal Vasculitis. Ocul Immunol Inflamm 2024:1-8. [PMID: 38261457 DOI: 10.1080/09273948.2024.2305185] [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: 04/13/2023] [Accepted: 01/08/2024] [Indexed: 01/25/2024]
Abstract
PURPOSE Retinal vasculitis (RV) is characterised by retinal vascular leakage, occlusion or both on fluorescein angiography (FA). There is no standard scheme available to segment RV features. We aimed to develop a deep learning model to segment both vascular leakage and occlusion in RV. METHODS Four hundred and sixty-three FA images from 82 patients with retinal vasculitis were used to develop a deep learning model, in 60:20:20 ratio for training:validation:testing. Parameters, including deep learning architectures (DeeplabV3+, UNet++ and UNet), were altered to find the best binary segmentation model separately for retinal vascular leakage and occlusion, using a Dice score to determine the reliability of each model. RESULTS Our best model for vascular leakage had a Dice score of 0.6279 (95% confidence interval (CI) 0.5584-0.6974). For occlusion, the best model achieved a Dice score of 0.6992 (95% CI 0.6109-0.7874). CONCLUSION Our RV segmentation models could perform reliable segmentation for retinal vascular leakage and occlusion in FAs of RV patients.
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Affiliation(s)
- Dhanach Dhirachaikulpanich
- Department of Eye & Vision Sciences, University of Liverpool, Liverpool, UK
- Faculty of Medicine, Siriraj Hospital, Mahidol University, Bangkok, Thailand
- St Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Jianyang Xie
- Department of Eye & Vision Sciences, University of Liverpool, Liverpool, UK
| | - Xiuju Chen
- Xiamen Eye Center, Xiamen University, Xiamen, Fujian, China
| | - Xiaoxin Li
- Xiamen Eye Center, Xiamen University, Xiamen, Fujian, China
- Department of Ophthalmology, Peking University People's Hospital, Beijing, China
| | - Savita Madhusudhan
- Department of Eye & Vision Sciences, University of Liverpool, Liverpool, UK
- St Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
| | - Yalin Zheng
- Department of Eye & Vision Sciences, University of Liverpool, Liverpool, UK
- St Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
- Liverpool Centre for Cardiovascular Science, University of Liverpool and Liverpool Heart & Chest Hospital, Liverpool, UK
| | - Nicholas A V Beare
- Department of Eye & Vision Sciences, University of Liverpool, Liverpool, UK
- St Paul's Eye Unit, Liverpool University Hospitals NHS Foundation Trust, Liverpool, UK
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16
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Rák T, Kovács-Valasek A, Pöstyéni E, Csutak A, Gábriel R. Complementary Approaches to Retinal Health Focusing on Diabetic Retinopathy. Cells 2023; 12:2699. [PMID: 38067127 PMCID: PMC10705724 DOI: 10.3390/cells12232699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 11/20/2023] [Accepted: 11/20/2023] [Indexed: 12/18/2023] Open
Abstract
Diabetes mellitus affects carbohydrate homeostasis but also influences fat and protein metabolism. Due to ophthalmic complications, it is a leading cause of blindness worldwide. The molecular pathology reveals that nuclear factor kappa B (NFκB) has a central role in the progression of diabetic retinopathy, sharing this signaling pathway with another major retinal disorder, glaucoma. Therefore, new therapeutic approaches can be elaborated to decelerate the ever-emerging "epidemics" of diabetic retinopathy and glaucoma targeting this critical node. In our review, we emphasize the role of an improvement of lifestyle in its prevention as well as the use of phytomedicals associated with evidence-based protocols. A balanced personalized therapy requires an integrative approach to be more successful for prevention and early treatment.
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Affiliation(s)
- Tibor Rák
- Department of Ophthalmology, Clinical Centre, Medical School, University of Pécs, Rákóczi út 2., 7623 Pécs, Hungary; (T.R.)
| | - Andrea Kovács-Valasek
- Department of Neurobiology, University of Pécs, Ifjúság útja 6, 7624 Pécs, Hungary
- János Szentágothai Research Centre, University of Pécs, Ifjúság útja 20, 7624 Pécs, Hungary
| | - Etelka Pöstyéni
- Department of Neurobiology, University of Pécs, Ifjúság útja 6, 7624 Pécs, Hungary
| | - Adrienne Csutak
- Department of Ophthalmology, Clinical Centre, Medical School, University of Pécs, Rákóczi út 2., 7623 Pécs, Hungary; (T.R.)
| | - Róbert Gábriel
- Department of Neurobiology, University of Pécs, Ifjúság útja 6, 7624 Pécs, Hungary
- János Szentágothai Research Centre, University of Pécs, Ifjúság útja 20, 7624 Pécs, Hungary
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17
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Khosravi P, Huck NA, Shahraki K, Hunter SC, Danza CN, Kim SY, Forbes BJ, Dai S, Levin AV, Binenbaum G, Chang PD, Suh DW. Deep Learning Approach for Differentiating Etiologies of Pediatric Retinal Hemorrhages: A Multicenter Study. Int J Mol Sci 2023; 24:15105. [PMID: 37894785 PMCID: PMC10606803 DOI: 10.3390/ijms242015105] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 09/29/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
Retinal hemorrhages in pediatric patients can be a diagnostic challenge for ophthalmologists. These hemorrhages can occur due to various underlying etiologies, including abusive head trauma, accidental trauma, and medical conditions. Accurate identification of the etiology is crucial for appropriate management and legal considerations. In recent years, deep learning techniques have shown promise in assisting healthcare professionals in making more accurate and timely diagnosis of a variety of disorders. We explore the potential of deep learning approaches for differentiating etiologies of pediatric retinal hemorrhages. Our study, which spanned multiple centers, analyzed 898 images, resulting in a final dataset of 597 retinal hemorrhage fundus photos categorized into medical (49.9%) and trauma (50.1%) etiologies. Deep learning models, specifically those based on ResNet and transformer architectures, were applied; FastViT-SA12, a hybrid transformer model, achieved the highest accuracy (90.55%) and area under the receiver operating characteristic curve (AUC) of 90.55%, while ResNet18 secured the highest sensitivity value (96.77%) on an independent test dataset. The study highlighted areas for optimization in artificial intelligence (AI) models specifically for pediatric retinal hemorrhages. While AI proves valuable in diagnosing these hemorrhages, the expertise of medical professionals remains irreplaceable. Collaborative efforts between AI specialists and pediatric ophthalmologists are crucial to fully harness AI's potential in diagnosing etiologies of pediatric retinal hemorrhages.
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Affiliation(s)
- Pooya Khosravi
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA;
| | - Nolan A. Huck
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
| | - Kourosh Shahraki
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
| | - Stephen C. Hunter
- School of Medicine, University of California, 900 University Ave, Riverside, CA 92521, USA;
| | - Clifford Neil Danza
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
| | - So Young Kim
- Department of Ophthalmology, College of Medicine, Soonchunhyang University, Cheonan 31151, Chungcheongnam-do, Republic of Korea;
| | - Brian J. Forbes
- Division of Ophthalmology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (B.J.F.); (G.B.)
| | - Shuan Dai
- Department of Ophthalmology, Queensland Children’s Hospital, South Brisbane, QLD 4101, Australia;
| | - Alex V. Levin
- Department of Ophthalmology, Flaum Eye Institute, Golisano Children’s Hospital, Rochester, NY 14642, USA;
| | - Gil Binenbaum
- Division of Ophthalmology, Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA; (B.J.F.); (G.B.)
| | - Peter D. Chang
- Donald Bren School of Information and Computer Sciences, University of California, Irvine, CA 92697, USA;
- Department of Radiological Sciences, School of Medicine, University of California, Irvine, CA 92697, USA
| | - Donny W. Suh
- Department of Ophthalmology, School of Medicine, University of California, Irvine, CA 92697, USA; (P.K.); (N.A.H.); (K.S.); (C.N.D.)
- Gavin Herbert Eye Institute, University of California, Irvine, CA 92697, USA
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18
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Suman S, Tiwari AK, Singh K. Computer-aided diagnostic system for hypertensive retinopathy: A review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 240:107627. [PMID: 37320942 DOI: 10.1016/j.cmpb.2023.107627] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 05/03/2023] [Accepted: 05/27/2023] [Indexed: 06/17/2023]
Abstract
Hypertensive Retinopathy (HR) is a retinal disease caused by elevated blood pressure for a prolonged period. There are no obvious signs in the early stages of high blood pressure, but it affects various body parts over time, including the eyes. HR is a biomarker for several illnesses, including retinal diseases, atherosclerosis, strokes, kidney disease, and cardiovascular risks. Early microcirculation abnormalities in chronic diseases can be diagnosed through retinal examination prior to the onset of major clinical consequences. Computer-aided diagnosis (CAD) plays a vital role in the early identification of HR with improved diagnostic accuracy, which is time-efficient and demands fewer resources. Recently, numerous studies have been reported on the automatic identification of HR. This paper provides a comprehensive review of the automated tasks of Artery-Vein (A/V) classification, Arteriovenous ratio (AVR) computation, HR detection (Binary classification), and HR severity grading. The review is conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. The paper discusses the clinical features of HR, the availability of datasets, existing methods used for A/V classification, AVR computation, HR detection, and severity grading, and performance evaluation metrics. The reviewed articles are summarized with classifiers details, adoption of different kinds of methodologies, performance comparisons, datasets details, their pros and cons, and computational platform. For each task, a summary and critical in-depth analysis are provided, as well as common research issues and challenges in the existing studies. Finally, the paper proposes future research directions to overcome challenges associated with data set availability, HR detection, and severity grading.
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Affiliation(s)
- Supriya Suman
- Interdisciplinary Research Platform (IDRP): Smart Healthcare, Indian Institute of Technology, N.H. 62, Nagaur Road, Karwar, Jodhpur, Rajasthan 342030, India.
| | - Anil Kumar Tiwari
- Department of Electrical Engineering, Indian Institute of Technology, N.H. 62, Nagaur Road, Karwar, Jodhpur, Rajasthan 342030, India
| | - Kuldeep Singh
- Department of Pediatrics, All India Institute of Medical Sciences, Basni Industrial Area Phase-2, Jodhpur, Rajasthan 342005, India
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Guo H, Li W, Wang K, Nie Z, Zhang X, Bai S, Duan N, Li X, Hu B. Analysis of Risk Factors for Revitrectomy in Eyes with Diabetic Vitreous Hemorrhage. Diabetes Metab Syndr Obes 2023; 16:2865-2874. [PMID: 37753483 PMCID: PMC10518247 DOI: 10.2147/dmso.s429938] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 09/14/2023] [Indexed: 09/28/2023] Open
Abstract
Purpose We aimed to investigate the risk factors associated with revitrectomy in eyes with diabetic vitreous hemorrhage and to determine the prognosis of these patients at least one year postoperatively. Patients and Methods This retrospective case-control study had a minimum follow-up period of one year. Patients were divided into single vitrectomy group (control group, n=202) and revitrectomy group (case group, n=36) for analysis. The indications, number, and timing of revitrectomies were documented. And the revitrectomy group was further divided into two vitrectomies group (n=30) and three or more vitrectomies group (n=6). The best-corrected visual acuity (BCVA) at the last follow-up and the occurrence of neovascular glaucoma (NVG) were compared among the single vitrectomy, two vitrectomies and three or more vitrectomies groups. We conducted a thorough collection of patient data and used univariate and binary logistic regression analyses to identify the risk factors associated with revitrectomy. Results A total of 197 patients (238 eyes) were included. Thirty-six eyes (15.1%) required revitrectomy with six eyes (2.5%) undergoing three or more vitrectomies during the follow-up period. The median duration of the second vitrectomy was 3 (2-6) months. The indications for a second vitrectomy included 28 eyes (77.8%) of postoperative vitreous hemorrhage and 7 eyes (22.2%) combined with tractional retinal detachment. Patients undergoing three or more vitrectomies had significantly worse postoperative BCVA and a higher incidence of NVG (P<0.01). Fibrinogen> 4 g/L (P<0.001) and preoperative anti-vascular endothelial growth factor intravitreal injection (P=0.015) were independent risk factors for revitrectomy, and glycated hemoglobin A1c (HbA1c)>10% (P=0.049) showed significant difference only in univariate analysis. Conclusion Patients requiring revitrectomy tended to have higher fibrinogen levels, tightly adhered fibrovascular membranes, higher HbA1c levels, and worse prognoses.
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Affiliation(s)
- Haoxin Guo
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin University Eye Hospital, Tianjin, People’s Republic of China
| | - Wenbo Li
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin University Eye Hospital, Tianjin, People’s Republic of China
| | - Kuan Wang
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin University Eye Hospital, Tianjin, People’s Republic of China
- Cangzhou Eye Hospital, Cangzhou, People’s Republic of China
| | - Zetong Nie
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin University Eye Hospital, Tianjin, People’s Republic of China
| | - Xiang Zhang
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin University Eye Hospital, Tianjin, People’s Republic of China
| | - Siqiong Bai
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin University Eye Hospital, Tianjin, People’s Republic of China
| | - Naxin Duan
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin University Eye Hospital, Tianjin, People’s Republic of China
| | - Xiaorong Li
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin University Eye Hospital, Tianjin, People’s Republic of China
| | - Bojie Hu
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin University Eye Hospital, Tianjin, People’s Republic of China
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Dai Y, Qian Y, Lu F, Wang B, Gu Z, Wang W, Wan J, Zhang Y. Improving adversarial robustness of medical imaging systems via adding global attention noise. Comput Biol Med 2023; 164:107251. [PMID: 37480679 DOI: 10.1016/j.compbiomed.2023.107251] [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: 04/17/2023] [Revised: 06/14/2023] [Accepted: 07/07/2023] [Indexed: 07/24/2023]
Abstract
Recent studies have found that medical images are vulnerable to adversarial attacks. However, it is difficult to protect medical imaging systems from adversarial examples in that the lesion features of medical images are more complex with high resolution. Therefore, a simple and effective method is needed to address these issues to improve medical imaging systems' robustness. We find that the attackers generate adversarial perturbations corresponding to the lesion characteristics of different medical image datasets, which can shift the model's attention to other places. In this paper, we propose global attention noise (GATN) injection, including global noise in the example layer and attention noise in the feature layers. Global noise enhances the lesion features of the medical images, thus keeping the examples away from the sharp areas where the model is vulnerable. The attention noise further locally smooths the model from small perturbations. According to the characteristic of medical image datasets, we introduce Global attention lesion-unrelated noise (GATN-UR) for datasets with unclear lesion boundaries and Global attention lesion-related noise (GATN-R) for datasets with clear lesion boundaries. Extensive experiments on ChestX-ray, Dermatology, and Fundoscopy datasets show that GATN improves the robustness of medical diagnosis models against a variety of powerful attacks and significantly outperforms the existing adversarial defense methods. To be specific, the robust accuracy is 86.66% on ChestX-ray, 72.49% on Dermatology, and 90.17% on Fundoscopy under PGD attack. Under the AA attack, it achieves robust accuracy of 87.70% on ChestX-ray, 66.85% on Dermatology, and 87.83% on Fundoscopy.
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Affiliation(s)
- Yinyao Dai
- Zhejiang University of Science and Technology, Hangzhou 310023, China
| | - Yaguan Qian
- Zhejiang University of Science and Technology, Hangzhou 310023, China.
| | - Fang Lu
- Zhejiang University of Science and Technology, Hangzhou 310023, China
| | - Bin Wang
- Zhejiang Key Laboratory of Multidimensional Perception Technology, Application, and Cybersecurity, Hangzhou 310052, China.
| | - Zhaoquan Gu
- School of Computer Science and Technology, Harbin Institute of Technology (Shenzhen), Shenzhen 518071, China
| | - Wei Wang
- Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University, Beijing 100091, China
| | - Jian Wan
- Zhejiang University of Science and Technology, Hangzhou 310023, China
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Mahmood MAI, Aktar N, Kader MF. A hybrid approach for diagnosing diabetic retinopathy from fundus image exploiting deep features. Heliyon 2023; 9:e19625. [PMID: 37809795 PMCID: PMC10558873 DOI: 10.1016/j.heliyon.2023.e19625] [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: 01/20/2023] [Revised: 08/28/2023] [Accepted: 08/29/2023] [Indexed: 10/10/2023] Open
Abstract
One of the major causes of blindness in human beings is the diabetic retinopathy (DR). To prevent blindness, early detection of DR is therefore necessary. In this paper, a hybrid model is proposed for diagnosing DR from fundus images. A combination of morphological image processing and Inception v3 deep learning techniques are exploited to detect DR as well as to classify healthy, mild non-proliferative DR (NPDR), moderate NPDR, severe NPDR, and proliferative DR (PDR). The proposed algorithm was carried out in several steps such as segmentation of blood vessels, localization and removal of optic disc, and macula, abnormal features detection (microaneurysms, hemorrhages, and neovascularization), and classification. Microaneurysms and hemorrhages that appear in the retina are the early signs of DR. In this work, we have detected microaneurysms and hemorrhages by applying dynamic contrast limited adaptive histogram equalization and threshold value on overlapping patched images. An overall accuracy of 96.83% is obtained to classify DR into five different stages. The better performance demonstrates the effectiveness and novelty of the proposed work as compared to the recent reported work.
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Affiliation(s)
| | - Nasrin Aktar
- Department of Electrical and Electronic Engineering, University of Chittagong, Chittagong 4331, Bangladesh
| | - Md. Fazlul Kader
- Department of Electrical and Electronic Engineering, University of Chittagong, Chittagong 4331, Bangladesh
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Liu J, He Y, Kong L, Yang D, Lu N, Yu Y, Zhao Y, Wang Y, Ma Z. Study of Foveal Avascular Zone Growth in Individuals With Mild Diabetic Retinopathy by Optical Coherence Tomography. Invest Ophthalmol Vis Sci 2023; 64:21. [PMID: 37698529 PMCID: PMC10501493 DOI: 10.1167/iovs.64.12.21] [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: 04/06/2023] [Accepted: 08/18/2023] [Indexed: 09/13/2023] Open
Abstract
Purpose The purpose of this study was to investigate the association between foveal vessels and retinal thickness in individuals with diabetic retinopathy (DR) and control subjects, and to reveal foveal avascular zone (FAZ) growth in early individuals with DR. Methods The regions with a thickness less than 60 µm were marked from the intima thickness maps and named FAZThic. The avascular zones extracted from the deep vascular plexus were designated as FAZAngi. The boundary of the two FAZ forms a ring region, which we called FAZRing. The FAZ growth rate was defined as the ratio of the FAZRing area to the FAZThic area. Thirty healthy controls and 30 individuals with mild nonproliferative DR were recruited for this study. Results The FAZThic area in individuals with mild DR and control subjects showed similar distribution. The FAZAngi area in individuals with mild DR are higher than that in control subjects on the whole, but there was no significant difference (P > 0.05). The FAZRing area in individuals with mild DR was significantly higher than that in control subjects (P < 0.001). However, there is still a small amount of overlap data between the two groups. For the FAZ growth rate, the individuals with mild DR were also significantly larger than the control subjects (P < 0.001). But there were no overlapping data between the two groups. Conclusions The growth of FAZ in individuals with mild DR can be inferred by comparing FAZAngi with FAZThic. This method minimizes the impact of individual variations and helps researchers to understand the progression mechanism of DR more deeply.
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Affiliation(s)
- Jian Liu
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao City, China
- Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao City, China
| | - Yang He
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao City, China
| | - Linghui Kong
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao City, China
| | - Dongni Yang
- Department of Ophthalmology, The First Hospital of Qinhuangdao, Qinhuangdao City, Hebei Province, China
| | - Nan Lu
- Department of Ophthalmology, The First Hospital of Qinhuangdao, Qinhuangdao City, Hebei Province, China
| | - Yao Yu
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao City, China
- Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao City, China
| | - Yuqian Zhao
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao City, China
| | - Yi Wang
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao City, China
- Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao City, China
| | - Zhenhe Ma
- School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao City, China
- Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology, Qinhuangdao City, China
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Guo H, Wang Z, Nie Z, Zhang X, Wang K, Duan N, Bai S, Li W, Li X, Hu B. Establishment and validation of a prognostic nomogram for long-term low vision after diabetic vitrectomy. Front Endocrinol (Lausanne) 2023; 14:1196335. [PMID: 37693349 PMCID: PMC10485701 DOI: 10.3389/fendo.2023.1196335] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 08/09/2023] [Indexed: 09/12/2023] Open
Abstract
Purpose We aimed to evaluate the risk factors and develop a prognostic nomogram of long-term low vision after diabetic vitrectomy. Methods This retrospective study included 186 patients (250 eyes) that underwent primary vitrectomy for proliferative diabetic retinopathy with a minimum follow-up period of one year. Patients were assigned to the training cohort (200 eyes) or validation cohort (50 eyes) at a 4:1 ratio randomly. Based on a cutoff value of 0.3 in best-corrected visual acuity (BCVA) measurement, the training cohort was separated into groups with or without low vision. Univariate and multivariate logistic regression analyses were performed on preoperative systemic and ocular characteristics to develop a risk prediction model and nomogram. The calibration curve and the area under the receiver operating characteristic curves (AUC) were used to evaluate the calibration and discrimination of the model. The nomogram was internally validated using the bootstrapping method, and it was further verified in an external cohort. Results Four independent risk factors were selected by stepwise forward regression, including tractional retinal detachment (β=1.443, OR=4.235, P<0.001), symptom duration ≥6 months (β=0.954, OR=2.595, P=0.004), preoperative BCVA measurement (β=0.540, OR=1.716, P=0.033), and hypertension (β=0.645, OR=1.905, P=0.044). AUC values of 0.764 (95% CI: 0.699-0.829) in the training cohort and 0.755 (95% CI: 0.619-0.891) in the validation cohort indicated the good predictive ability of the model. Conclusion The prognostic nomogram established in this study is useful for predicting long-term low vision after diabetic vitrectomy.
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Affiliation(s)
- Haoxin Guo
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Zhaoxiong Wang
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
- Department of Ophthalmology, Tianjin Baodi Hospital, Tianjin, China
- Baodi Clinical College, Tianjin Medical University, Tianjin, China
| | - Zetong Nie
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Xiang Zhang
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Kuan Wang
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
- Department of Retinal Disease, Cangzhou Eye Hospital, Cangzhou, China
| | - Naxin Duan
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Siqiong Bai
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Wenbo Li
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Xiaorong Li
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Bojie Hu
- Tianjin Key Laboratory of Retinal Functions and Diseases, Tianjin Branch of National Clinical Research Center for Ocular Disease, Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
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24
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Multi-Level severity classification for diabetic retinopathy based on hybrid optimization enabled deep learning. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104736] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/05/2023]
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25
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Jiang H, Hou Y, Miao H, Ye H, Gao M, Li X, Jin R, Liu J. Eye tracking based deep learning analysis for the early detection of diabetic retinopathy: A pilot study. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/12/2023]
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Yang K, Lu Y, Xue L, Yang Y, Chang S, Zhou C. URNet: System for recommending referrals for community screening of diabetic retinopathy based on deep learning. Exp Biol Med (Maywood) 2023; 248:909-921. [PMID: 37466156 PMCID: PMC10525407 DOI: 10.1177/15353702231171898] [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: 11/17/2022] [Accepted: 02/01/2023] [Indexed: 07/20/2023] Open
Abstract
Diabetic retinopathy (DR) will cause blindness if the detection and treatment are not carried out in the early stages. To create an effective treatment strategy, the severity of the disease must first be divided into referral-warranted diabetic retinopathy (RWDR) and non-referral diabetic retinopathy (NRDR). However, there are usually no sufficient fundus examinations due to lack of professional service in the communities, particularly in the developing countries. In this study, we introduce UGAN_Resnet_CBAM (URNet; UGAN is a generative adversarial network that uses Unet for feature extraction), a two-stage end-to-end deep learning technique for the automatic detection of diabetic retinopathy. The characteristics of DDR fundus data set were used to design an adaptive image preprocessing module in the first stage. Gradient-weighted Class Activation Mapping (Grad-CAM) and t-distribution and stochastic neighbor embedding (t-SNE) were used as the evaluation indices to analyze the preprocessing results. In the second stage, we enhanced the performance of the Resnet50 network by integrating the convolutional block attention module (CBAM). The outcomes demonstrate that our proposed solution outperformed other current structures, achieving 94.5% and 94.4% precisions, and 96.2% and 91.9% recall for NRDR and RWDR, respectively.
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Affiliation(s)
- Kun Yang
- College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
- Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding 071002, China
| | - Yufei Lu
- College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
| | - Linyan Xue
- College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
- Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding 071002, China
| | - Yueting Yang
- College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
| | - Shilong Chang
- College of Quality and Technical Supervision, Hebei University, Baoding 071002, China
| | - Chuanqing Zhou
- College of Medical Instruments, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China
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Shoukat A, Akbar S, Hassan SA, Iqbal S, Mehmood A, Ilyas QM. Automatic Diagnosis of Glaucoma from Retinal Images Using Deep Learning Approach. Diagnostics (Basel) 2023; 13:diagnostics13101738. [PMID: 37238222 DOI: 10.3390/diagnostics13101738] [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: 03/06/2023] [Revised: 05/04/2023] [Accepted: 05/06/2023] [Indexed: 05/28/2023] Open
Abstract
Glaucoma is characterized by increased intraocular pressure and damage to the optic nerve, which may result in irreversible blindness. The drastic effects of this disease can be avoided if it is detected at an early stage. However, the condition is frequently detected at an advanced stage in the elderly population. Therefore, early-stage detection may save patients from irreversible vision loss. The manual assessment of glaucoma by ophthalmologists includes various skill-oriented, costly, and time-consuming methods. Several techniques are in experimental stages to detect early-stage glaucoma, but a definite diagnostic technique remains elusive. We present an automatic method based on deep learning that can detect early-stage glaucoma with very high accuracy. The detection technique involves the identification of patterns from the retinal images that are often overlooked by clinicians. The proposed approach uses the gray channels of fundus images and applies the data augmentation technique to create a large dataset of versatile fundus images to train the convolutional neural network model. Using the ResNet-50 architecture, the proposed approach achieved excellent results for detecting glaucoma on the G1020, RIM-ONE, ORIGA, and DRISHTI-GS datasets. We obtained a detection accuracy of 98.48%, a sensitivity of 99.30%, a specificity of 96.52%, an AUC of 97%, and an F1-score of 98% by using the proposed model on the G1020 dataset. The proposed model may help clinicians to diagnose early-stage glaucoma with very high accuracy for timely interventions.
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Affiliation(s)
- Ayesha Shoukat
- Department of Computer Science, Riphah International University, Faisalabad Campus, Faisalabad 44000, Pakistan
| | - Shahzad Akbar
- Department of Computer Science, Riphah International University, Faisalabad Campus, Faisalabad 44000, Pakistan
| | - Syed Ale Hassan
- Department of Computer Science, Riphah International University, Faisalabad Campus, Faisalabad 44000, Pakistan
| | - Sajid Iqbal
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Ahsa 31982, Saudi Arabia
| | - Abid Mehmood
- Department of Management Information Systems, College of Business Administration, King Faisal University, Al Ahsa 31982, Saudi Arabia
| | - Qazi Mudassar Ilyas
- Department of Information Systems, College of Computer Sciences and Information Technology, King Faisal University, Al Ahsa 31982, Saudi Arabia
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Zhao J, Chandrasekaran PR, Cheong KX, Wong M, Teo K. New Concepts for the Diagnosis of Polypoidal Choroidal Vasculopathy. Diagnostics (Basel) 2023; 13:diagnostics13101680. [PMID: 37238165 DOI: 10.3390/diagnostics13101680] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 05/06/2023] [Accepted: 05/07/2023] [Indexed: 05/28/2023] Open
Abstract
Polypoidal choroidal vasculopathy (PCV) is a subtype of neovascular age-related macular degeneration (nAMD) that is characterized by a branching neovascular network and polypoidal lesions. It is important to differentiate PCV from typical nAMD as there are differences in treatment response between subtypes. Indocyanine green angiography (ICGA) is the gold standard for diagnosing PCV; however, ICGA is an invasive detection method and impractical for extensive use for regular long-term monitoring. In addition, access to ICGA may be limited in some settings. The purpose of this review is to summarize the utilization of multimodal imaging modalities (color fundus photography, optical coherence tomography (OCT), OCT angiography (OCTA), and fundus autofluorescence (FAF)) in differentiating PCV from typical nAMD and predicting disease activity and prognosis. In particular, OCT shows tremendous potential in diagnosing PCV. Characteristics such as subretinal pigment epithelium (RPE) ring-like lesion, en face OCT-complex RPE elevation, and sharp-peaked pigment epithelial detachment provide high sensitivity and specificity for differentiating PCV from nAMD. With the use of more practical, non-ICGA imaging modalities, the diagnosis of PCV can be more easily made and treatment tailored as necessary for optimal outcomes.
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Affiliation(s)
- Jinzhi Zhao
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- Tianjin Medical University Eye Hospital, Tianjin 300392, China
| | - Priya R Chandrasekaran
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
| | - Kai Xiong Cheong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
| | - Mark Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
| | - Kelvin Teo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore 168751, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore 169857, Singapore
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Artificial Intelligence for Diabetic Retinopathy Screening Using Color Retinal Photographs: From Development to Deployment. Ophthalmol Ther 2023; 12:1419-1437. [PMID: 36862308 PMCID: PMC10164194 DOI: 10.1007/s40123-023-00691-3] [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: 01/09/2023] [Accepted: 02/14/2023] [Indexed: 03/03/2023] Open
Abstract
Diabetic retinopathy (DR), a leading cause of preventable blindness, is expected to remain a growing health burden worldwide. Screening to detect early sight-threatening lesions of DR can reduce the burden of vision loss; nevertheless, the process requires intensive manual labor and extensive resources to accommodate the increasing number of patients with diabetes. Artificial intelligence (AI) has been shown to be an effective tool which can potentially lower the burden of screening DR and vision loss. In this article, we review the use of AI for DR screening on color retinal photographs in different phases of application, ranging from development to deployment. Early studies of machine learning (ML)-based algorithms using feature extraction to detect DR achieved a high sensitivity but relatively lower specificity. Robust sensitivity and specificity were achieved with the application of deep learning (DL), although ML is still used in some tasks. Public datasets were utilized in retrospective validations of the developmental phases in most algorithms, which require a large number of photographs. Large prospective clinical validation studies led to the approval of DL for autonomous screening of DR although the semi-autonomous approach may be preferable in some real-world settings. There have been few reports on real-world implementations of DL for DR screening. It is possible that AI may improve some real-world indicators for eye care in DR, such as increased screening uptake and referral adherence, but this has not been proven. The challenges in deployment may include workflow issues, such as mydriasis to lower ungradable cases; technical issues, such as integration into electronic health record systems and integration into existing camera systems; ethical issues, such as data privacy and security; acceptance of personnel and patients; and health-economic issues, such as the need to conduct health economic evaluations of using AI in the context of the country. The deployment of AI for DR screening should follow the governance model for AI in healthcare which outlines four main components: fairness, transparency, trustworthiness, and accountability.
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McDermott MBA, Nestor B, Szolovits P. Clinical Artificial Intelligence: Design Principles and Fallacies. Clin Lab Med 2023; 43:29-46. [PMID: 36764807 DOI: 10.1016/j.cll.2022.09.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Clinical artificial intelligence (AI)/machine learning (ML) is anticipated to offer new abilities in clinical decision support, diagnostic reasoning, precision medicine, clinical operational support, and clinical research, but careful concern is needed to ensure these technologies work effectively in the clinic. Here, we detail the clinical ML/AI design process, identifying several key questions and detailing several common forms of issues that arise with ML tools, as motivated by real-world examples, such that clinicians and researchers can better anticipate and correct for such issues in their own use of ML/AI techniques.
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Affiliation(s)
| | - Bret Nestor
- Department of Computer Science, University of Toronto, 40 St George St, Toronto, ON M5S 2E4, Canada
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Jian M, Chen H, Tao C, Li X, Wang G. Triple-DRNet: A triple-cascade convolution neural network for diabetic retinopathy grading using fundus images. Comput Biol Med 2023; 155:106631. [PMID: 36805216 DOI: 10.1016/j.compbiomed.2023.106631] [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: 12/01/2022] [Revised: 01/29/2023] [Accepted: 02/04/2023] [Indexed: 02/10/2023]
Abstract
Diabetic Retinopathy (DR) is a universal ocular complication of diabetes patients and also the main disease that causes blindness in the world wide. Automatic and efficient DR grading acts a vital role in timely treatment. However, it is difficult to effectively distinguish different types of distinct lesions (such as neovascularization in proliferative DR, microaneurysms in mild NPDR, etc.) using traditional convolutional neural networks (CNN), which greatly affects the ultimate classification results. In this article, we propose a triple-cascade network model (Triple-DRNet) to solve the aforementioned issue. The Triple-DRNet effectively subdivides the classification of five types of DR as well as improves the grading performance which mainly includes the following aspects: (1) In the first stage, the network carries out two types of classification, namely DR and No DR. (2) In the second stage, the cascade network is intended to distinguish the two categories between PDR and NPDR. (3) The final cascade network will be designed to differentiate the mild, moderate and severe types in NPDR. Experimental results show that the ACC of the Triple-DRNet on the APTOS 2019 Blindness Detection dataset achieves 92.08%, and the QWK metric reaches 93.62%, which proves the effectiveness of the devised Triple-DRNet compared with other mainstream models.
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Affiliation(s)
- Muwei Jian
- School of Information Science and Technology, Linyi University, Linyi, China; School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China.
| | - Hongyu Chen
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Chen Tao
- School of Information Science and Technology, Linyi University, Linyi, China
| | - Xiaoguang Li
- Faculty of Information Tecnology, Beijing University of Technology, Beijing, China.
| | - Gaige Wang
- School of Computer Science and Technology, Ocean University of China, Qingdao, China
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Lalitha R, Krishna Prasad P, Rama Reddy T, Kavitha K, Srinivas R, Ravi Kiran B. Efficient adaptive enhanced adaboost based detection of spinal abnormalities by Machine learning approaches. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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Shah R, Petch J, Nelson W, Roth K, Noseworthy MD, Ghassemi M, Gerstein HC. Nailfold capillaroscopy and deep learning in diabetes. J Diabetes 2023; 15:145-151. [PMID: 36641812 PMCID: PMC9934957 DOI: 10.1111/1753-0407.13354] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 10/27/2022] [Accepted: 12/21/2022] [Indexed: 01/17/2023] Open
Abstract
OBJECTIVE To determine whether nailfold capillary images, acquired using video capillaroscopy, can provide diagnostic information about diabetes and its complications. RESEARCH DESIGN AND METHODS Nailfold video capillaroscopy was performed in 120 adult patients with and without type 1 or type 2 diabetes, and with and without cardiovascular disease. Nailfold images were analyzed using convolutional neural networks, a deep learning technique. Cross-validation was used to develop and test the ability of models to predict five5 prespecified states (diabetes, high glycosylated hemoglobin, cardiovascular event, retinopathy, albuminuria, and hypertension). The performance of each model for a particular state was assessed by estimating areas under the receiver operating characteristics curves (AUROC) and precision recall curves (AUPR). RESULTS A total of 5236 nailfold images were acquired from 120 participants (mean 44 images per participant) and were all available for analysis. Models were able to accurately identify the presence of diabetes, with AUROC 0.84 (95% confidence interval [CI] 0.76, 0.91) and AUPR 0.84 (95% CI 0.78, 0.93), respectively. Models were also able to predict a history of cardiovascular events in patients with diabetes, with AUROC 0.65 (95% CI 0.51, 0.78) and AUPR 0.72 (95% CI 0.62, 0.88) respectively. CONCLUSIONS This proof-of-concept study demonstrates the potential of machine learning for identifying people with microvascular capillary changes from diabetes based on nailfold images, and for possibly identifying those most likely to have diabetes-related complications.
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Affiliation(s)
- Reema Shah
- Population Health Research Institute, McMaster University and Hamilton Health SciencesHamiltonOntarioCanada
| | - Jeremy Petch
- Population Health Research Institute, McMaster University and Hamilton Health SciencesHamiltonOntarioCanada
- Centre for Data Science and Digital HealthHamilton Health SciencesHamiltonOntarioCanada
- Institute for Health Policy, Management and EvaluationUniversity of TorontoTorontoOntarioCanada
- Division of CardiologyMcMaster UniversityHamiltonOntarioCanada
| | - Walter Nelson
- Centre for Data Science and Digital HealthHamilton Health SciencesHamiltonOntarioCanada
- Department of Statistical SciencesUniversity of TorontoTorontoOntarioCanada
| | - Karsten Roth
- Cluster of Excellence Machine LearningUniversity of TübingenTübingenGermany
| | - Michael D. Noseworthy
- Electrical and Computer EngineeringMcMaster UniversityHamiltonOntarioCanada
- McMaster School of Biomedical EngineeringHamiltonOntarioCanada
- Department of RadiologyMcMaster UniversityHamiltonOntarioCanada
| | | | - Hertzel C. Gerstein
- Population Health Research Institute, McMaster University and Hamilton Health SciencesHamiltonOntarioCanada
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Identification of adaptor proteins using the ANOVA feature selection technique. Methods 2022; 208:42-47. [DOI: 10.1016/j.ymeth.2022.10.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Revised: 10/01/2022] [Accepted: 10/24/2022] [Indexed: 11/06/2022] Open
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Li H, Dong X, Shen W, Ge F, Li H. Resampling-based cost loss attention network for explainable imbalanced diabetic retinopathy grading. Comput Biol Med 2022; 149:105970. [DOI: 10.1016/j.compbiomed.2022.105970] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Revised: 07/23/2022] [Accepted: 08/13/2022] [Indexed: 11/03/2022]
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Minimized Computations of Deep Learning Technique for Early Diagnosis of Diabetic Retinopathy Using IoT-Based Medical Devices. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:7040141. [PMID: 36156979 PMCID: PMC9492354 DOI: 10.1155/2022/7040141] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 08/24/2022] [Accepted: 09/01/2022] [Indexed: 11/18/2022]
Abstract
Diabetes mellitus is the main cause of diabetic retinopathy, the most common cause of blindness worldwide. In order to slow down or prevent vision loss and degeneration, early detection and treatment are essential. For the purpose of detecting and classifying diabetic retinopathy on fundus retina images, numerous artificial intelligence-based algorithms have been put forth by the scientific community. Due to its real-time relevance to everyone's lives, smart healthcare is attracting a lot of interest. With the convergence of IoT, this attention has increased. The leading cause of blindness among persons in their working years is diabetic eye disease. Millions of people live in the most populous Asian nations, including China and India, and the number of diabetics among them is on the rise. To provide medical screening and diagnosis for this rising population of diabetes patients, skilled clinicians faced significant challenges. Our objective is to use deep learning techniques to automatically detect blind spots in eyes and determine how serious they may be. We suggest an enhanced convolutional neural network (ECNN) utilizing a genetic algorithm in this paper. The ECNN technique's accuracy results are compared to those of existing approaches like the K-nearest neighbor approach, convolutional neural network, and support vector machine with the genetic algorithm.
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37
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OHGCNet: Optimal feature selection-based hybrid graph convolutional network model for joint DR-DME classification. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103952] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Santos C, Aguiar M, Welfer D, Belloni B. A New Approach for Detecting Fundus Lesions Using Image Processing and Deep Neural Network Architecture Based on YOLO Model. SENSORS (BASEL, SWITZERLAND) 2022; 22:s22176441. [PMID: 36080898 PMCID: PMC9460625 DOI: 10.3390/s22176441] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2022] [Revised: 08/21/2022] [Accepted: 08/23/2022] [Indexed: 05/27/2023]
Abstract
Diabetic Retinopathy is one of the main causes of vision loss, and in its initial stages, it presents with fundus lesions, such as microaneurysms, hard exudates, hemorrhages, and soft exudates. Computational models capable of detecting these lesions can help in the early diagnosis of the disease and prevent the manifestation of more severe forms of lesions, helping in screening and defining the best form of treatment. However, the detection of these lesions through computerized systems is a challenge due to numerous factors, such as the characteristics of size and shape of the lesions, noise and the contrast of images available in the public datasets of Diabetic Retinopathy, the number of labeled examples of these lesions available in the datasets and the difficulty of deep learning algorithms in detecting very small objects in digital images. Thus, to overcome these problems, this work proposes a new approach based on image processing techniques, data augmentation, transfer learning, and deep neural networks to assist in the medical diagnosis of fundus lesions. The proposed approach was trained, adjusted, and tested using the public DDR and IDRiD Diabetic Retinopathy datasets and implemented in the PyTorch framework based on the YOLOv5 model. The proposed approach reached in the DDR dataset an mAP of 0.2630 for the IoU limit of 0.5 and F1-score of 0.3485 in the validation stage, and an mAP of 0.1540 for the IoU limit of 0.5 and F1-score of 0.2521, in the test stage. The results obtained in the experiments demonstrate that the proposed approach presented superior results to works with the same purpose found in the literature.
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Affiliation(s)
- Carlos Santos
- Computer Center, Federal Institute of Education, Science and Technology Farroupilha, Alegrete 97555-000, Brazil
- Postgraduate Program in Computing (PPGC), Federal University of Pelotas, Pelotas 96010-610, Brazil
| | - Marilton Aguiar
- Postgraduate Program in Computing (PPGC), Federal University of Pelotas, Pelotas 96010-610, Brazil
| | - Daniel Welfer
- Postgraduate Program in Computer Science (PPGCC), Departament of Applied Computing (DCOM), Federal University of Santa Maria, Santa Maria 97105-900, Brazil
| | - Bruno Belloni
- Federal Institute of Education, Science and Technology Sul-Rio-Grandense, Passo Fundo 99064-440, Brazil
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OLTU B, KARACA BK, ERDEM H, ÖZGÜR A. A systematic review of transfer learning-based approaches for diabetic retinopathy detection. GAZI UNIVERSITY JOURNAL OF SCIENCE 2022. [DOI: 10.35378/gujs.1081546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Cases of diabetes and related diabetic retinopathy (DR) have been increasing at an alarming rate in modern times. Early detection of DR is an important problem since it may cause permanent blindness in the late stages. In the last two decades, many different approaches have been applied in DR detection. Reviewing academic literature shows that deep neural networks (DNNs) have become the most preferred approach for DR detection. Among these DNN approaches, Convolutional Neural Network (CNN) models are the most used ones in the field of medical image classification. Designing a new CNN architecture is a tedious and time-consuming approach. Additionally, training an enormous number of parameters is also a difficult task. Due to this reason, instead of training CNNs from scratch, using pre-trained models has been suggested in recent years as transfer learning approach. Accordingly, the present study as a review focuses on DNN and Transfer Learning based applications of DR detection considering 43 publications between 2015 and 2021. The published papers are summarized using 3 figures and 10 tables, giving information about 29 pre-trained CNN models, 13 DR data sets and standard performance metrics.
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Affiliation(s)
- Burcu OLTU
- BAŞKENT ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ
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Liu Y, Huang H, Sun Y, Li Y, Luo B, Cui J, Zhu M, Bi F, Chen K, Liu Y. Monosodium Glutamate-Induced Mouse Model With Unique Diabetic Retinal Neuropathy Features and Artificial Intelligence Techniques for Quantitative Evaluation. Front Immunol 2022; 13:862702. [PMID: 35572527 PMCID: PMC9092070 DOI: 10.3389/fimmu.2022.862702] [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: 01/26/2022] [Accepted: 03/17/2022] [Indexed: 11/26/2022] Open
Abstract
Objective To establish an artificial intelligence-based method to quantitatively evaluate subtle pathological changes in retinal nerve cells and synapses in monosodium glutamate (MSG) mice and provide an effective animal model and technique for quantitative evaluation of retinal neurocytopathies. Methods ICR mice were subcutaneously injected with MSG to establish a model of metabolic syndrome. We then established a mouse model of type 1 diabetes, type 2 diabetes, and KKAy mouse model as control. The HE sections of the retina were visualized using an optical microscope. AI technology was used for quantitative evaluation of the retinal lesions in each group of rats. The surface area custom parameters of the retinal nerve fiber layer (RNFL), inner plexiform layer (IPL), inner nuclear layer (INL), and outer plexiform layer (OPL) were defined as SR, SIPL, SINL, and SOPL, respectively. Their heights were defined as HR, HIPL, HINL, and HOPL, and the number of ganglion cells was defined as A. Then, the attention-augmented fully convolutional Unet network was used to segment the retinal HE images, and AI technology to identify retinal neurocytopathies quantitatively. Results The attention-augmented fully convolutional Unet network increased PA and IOU parameters for INL, OPL, RNFL, and ganglion cells and was superior in recognizing fine structures. A quantitative AI identification of the height of each layer of the retina showed that the heights of the IPL and INL of the MSG model were significantly less than those of the control groups; the retinas of the other diabetic models did not exhibit this pathological feature. The RNFLs of type 2 diabetes were thinner, and the characteristics of retinopathy were not obvious in the other animal models. The pathological changes seen on HE images were consistent with the results of the quantitative AI evaluation. Immunohistochemistry results showed that NMDAR2A, GluR2, and NRG1 were significantly downregulated in the retina of MSG mice. Conclusions The MSG retinopathy model is closely associated with neurotransmitter abnormalities and exhibits important characteristics of retinal neurodegeneration, making it suitable for studying retinal neurocytopathies. The AI recognition technology for retinal images established in the present study can be used for the quantitative and objective evaluation of drug efficacy.
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Affiliation(s)
- Yanfei Liu
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Hui Huang
- Beijing Duan-Dian Pharmaceutical Research & Development Co., Ltd., Beijing, China
| | - Yu Sun
- North China University of Technology, Beijing, China
| | - Yiwen Li
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Binyu Luo
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Jing Cui
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Mengmeng Zhu
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Fukun Bi
- North China University of Technology, Beijing, China
| | - Keji Chen
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
| | - Yue Liu
- National Clinical Research Centre for Chinese Medicine Cardiology, Xiyuan Hospital of China Academy of Chinese Medical Sciences, Beijing, China
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Chen D, Li Y. PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features. Front Genet 2022; 13:875112. [PMID: 35547252 PMCID: PMC9081368 DOI: 10.3389/fgene.2022.875112] [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: 02/13/2022] [Accepted: 03/07/2022] [Indexed: 12/03/2022] Open
Abstract
The major histocompatibility complex (MHC) is a large locus on vertebrate DNA that contains a tightly linked set of polymorphic genes encoding cell surface proteins essential for the adaptive immune system. The groups of proteins encoded in the MHC play an important role in the adaptive immune system. Therefore, the accurate identification of the MHC is necessary to understand its role in the adaptive immune system. An effective predictor called PredMHC is established in this study to identify the MHC from protein sequences. Firstly, PredMHC encoded a protein sequence with mixed features including 188D, APAAC, KSCTriad, CKSAAGP, and PAAC. Secondly, three classifiers including SGD, SMO, and random forest were trained on the mixed features of the protein sequence. Finally, the prediction result was obtained by the voting of the three classifiers. The experimental results of the 10-fold cross-validation test in the training dataset showed that PredMHC can obtain 91.69% accuracy. Experimental results on comparison with other features, classifiers, and existing methods showed the effectiveness of PredMHC in predicting the MHC.
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Affiliation(s)
- Dong Chen
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
| | - Yanjuan Li
- College of Electrical and Information Engineering, Quzhou University, Quzhou, China
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42
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Nakayama LF, Ribeiro LZ, Malerbi FK, Regatieri CVS. Ophthalmology and Artificial Intelligence: Present or Future? A Diabetic Retinopathy Screening Perspective of the Pursuit for Fairness. FRONTIERS IN OPHTHALMOLOGY 2022; 2:898181. [PMID: 38983555 PMCID: PMC11182262 DOI: 10.3389/fopht.2022.898181] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 04/20/2022] [Indexed: 07/11/2024]
Affiliation(s)
- Luis Filipe Nakayama
- Retina and Vitreous Department, São Paulo Federal University (UNIFESP), Sao Paulo, Brazil
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43
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Mou L, Liang L, Gao Z, Wang X. A multi-scale anomaly detection framework for retinal OCT images based on the Bayesian neural network. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103619] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
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44
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Cao P, Hou Q, Song R, Wang H, Zaiane O. Collaborative learning of weakly-supervised domain adaptation for diabetic retinopathy grading on retinal images. Comput Biol Med 2022; 144:105341. [PMID: 35279423 DOI: 10.1016/j.compbiomed.2022.105341] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Revised: 02/20/2022] [Accepted: 02/20/2022] [Indexed: 11/25/2022]
Abstract
Early detection and treatment of diabetic retinopathy (DR) can significantly reduce the risk of vision loss in patients. In essence, we are faced with two challenges: (i) how to simultaneously achieve domain adaptation from the different domains and (ii) how to build an interpretable multi-instance learning (MIL) on the target domain in an end-to-end framework. In this paper, we address these issues and propose a unified weakly-supervised domain adaptation framework, which consists of three components: domain adaptation, instance progressive discriminator and multi-instance learning with attention. The method models the relationship between the patches and images in the target domain with a multi-instance learning scheme and an attention mechanism. Meanwhile, it incorporates all available information from both source and target domains for a jointly learning strategy. We validate the performance of the proposed framework for DR grading on the Messidor dataset and the large-scale Eyepacs dataset. The experimental results demonstrate that it achieves an average accuracy of 0.949 (95% CI 0.931-0.958)/0.764 (95% CI 0.755-0.772) and an average AUC value of 0.958 (95% CI 0.945-0.962)/0.749 (95% CI 0.732-0.761) for binary-class/multi-class classification tasks on the Messidor dataset. Moreover, the proposed method achieves an accuracy of 0.887 and a quadratic weighted kappa score value of 0.860 on the Eyepacs dataset, outperforming the state-of-the-art approaches. Comprehensive experiments confirm the effectiveness of the approach in terms of both grading performance and interpretability. The source code is available at https://github.com/HouQingshan/WAD-Net.
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Affiliation(s)
- Peng Cao
- Computer Science and Engineering, Northeastern University, Shenyang, China; Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, China.
| | - Qingshan Hou
- Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Ruoxian Song
- Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Haonan Wang
- Computer Science and Engineering, Northeastern University, Shenyang, China
| | - Osmar Zaiane
- Alberta Machine Intelligence Institute, University of Alberta, Edmonton, Alberta, Canada
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45
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Diagnosis of Retinal Diseases Based on Bayesian Optimization Deep Learning Network Using Optical Coherence Tomography Images. COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE 2022; 2022:8014979. [PMID: 35463234 PMCID: PMC9033334 DOI: 10.1155/2022/8014979] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 03/17/2022] [Indexed: 02/08/2023]
Abstract
Retinal abnormalities have emerged as a serious public health concern in recent years and can manifest gradually and without warning. These diseases can affect any part of the retina, causing vision impairment and indeed blindness in extreme cases. This necessitates the development of automated approaches to detect retinal diseases more precisely and, preferably, earlier. In this paper, we examine transfer learning of pretrained convolutional neural network (CNN) and then transfer it to detect retinal problems from Optical Coherence Tomography (OCT) images. In this study, pretrained CNN models, namely, VGG16, DenseNet201, InceptionV3, and Xception, are used to classify seven different retinal diseases from a dataset of images with and without retinal diseases. In addition, to choose optimum values for hyperparameters, Bayesian optimization is applied, and image augmentation is used to increase the generalization capabilities of the developed models. This research also provides a comparison of the proposed models as well as an analysis of them. The accuracy achieved using DenseNet201 on the Retinal OCT Image dataset is more than 99% and offers a good level of accuracy in classifying retinal diseases compared to other approaches, which only detect a small number of retinal diseases.
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46
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Hervella ÁS, Rouco J, Novo J, Ortega M. Multimodal image encoding pre-training for diabetic retinopathy grading. Comput Biol Med 2022; 143:105302. [PMID: 35219187 DOI: 10.1016/j.compbiomed.2022.105302] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Revised: 01/11/2022] [Accepted: 01/26/2022] [Indexed: 11/18/2022]
Abstract
Diabetic retinopathy is an increasingly prevalent eye disorder that can lead to severe vision impairment. The severity grading of the disease using retinal images is key to provide an adequate treatment. However, in order to learn the diverse patterns and complex relations that are required for the grading, deep neural networks require very large annotated datasets that are not always available. This has been typically addressed by reusing networks that were pre-trained for natural image classification, hence relying on additional annotated data from a different domain. In contrast, we propose a novel pre-training approach that takes advantage of unlabeled multimodal visual data commonly available in ophthalmology. The use of multimodal visual data for pre-training purposes has been previously explored by training a network in the prediction of one image modality from another. However, that approach does not ensure a broad understanding of the retinal images, given that the network may exclusively focus on the similarities between modalities while ignoring the differences. Thus, we propose a novel self-supervised pre-training that explicitly teaches the networks to learn the common characteristics between modalities as well as the characteristics that are exclusive to the input modality. This provides a complete comprehension of the input domain and facilitates the training of downstream tasks that require a broad understanding of the retinal images, such as the grading of diabetic retinopathy. To validate and analyze the proposed approach, we performed an exhaustive experimentation on different public datasets. The transfer learning performance for the grading of diabetic retinopathy is evaluated under different settings while also comparing against previous state-of-the-art pre-training approaches. Additionally, a comparison against relevant state-of-the-art works for the detection and grading of diabetic retinopathy is also provided. The results show a satisfactory performance of the proposed approach, which outperforms previous pre-training alternatives in the grading of diabetic retinopathy.
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Affiliation(s)
- Álvaro S Hervella
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, Spain.
| | - José Rouco
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, Spain.
| | - Jorge Novo
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, Spain.
| | - Marcos Ortega
- Centro de Investigación CITIC, Universidade da Coruña, A Coruña, Spain; VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidade da Coruña, A Coruña, Spain.
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47
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Shaik NS, Cherukuri TK. Hinge attention network: A joint model for diabetic retinopathy severity grading. APPL INTELL 2022. [DOI: 10.1007/s10489-021-03043-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/03/2023]
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48
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You A, Kim JK, Ryu IH, Yoo TK. Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. EYE AND VISION (LONDON, ENGLAND) 2022; 9:6. [PMID: 35109930 PMCID: PMC8808986 DOI: 10.1186/s40662-022-00277-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 01/11/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Recent advances in deep learning techniques have led to improved diagnostic abilities in ophthalmology. A generative adversarial network (GAN), which consists of two competing types of deep neural networks, including a generator and a discriminator, has demonstrated remarkable performance in image synthesis and image-to-image translation. The adoption of GAN for medical imaging is increasing for image generation and translation, but it is not familiar to researchers in the field of ophthalmology. In this work, we present a literature review on the application of GAN in ophthalmology image domains to discuss important contributions and to identify potential future research directions. METHODS We performed a survey on studies using GAN published before June 2021 only, and we introduced various applications of GAN in ophthalmology image domains. The search identified 48 peer-reviewed papers in the final review. The type of GAN used in the analysis, task, imaging domain, and the outcome were collected to verify the usefulness of the GAN. RESULTS In ophthalmology image domains, GAN can perform segmentation, data augmentation, denoising, domain transfer, super-resolution, post-intervention prediction, and feature extraction. GAN techniques have established an extension of datasets and modalities in ophthalmology. GAN has several limitations, such as mode collapse, spatial deformities, unintended changes, and the generation of high-frequency noises and artifacts of checkerboard patterns. CONCLUSIONS The use of GAN has benefited the various tasks in ophthalmology image domains. Based on our observations, the adoption of GAN in ophthalmology is still in a very early stage of clinical validation compared with deep learning classification techniques because several problems need to be overcome for practical use. However, the proper selection of the GAN technique and statistical modeling of ocular imaging will greatly improve the performance of each image analysis. Finally, this survey would enable researchers to access the appropriate GAN technique to maximize the potential of ophthalmology datasets for deep learning research.
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Affiliation(s)
- Aram You
- School of Architecture, Kumoh National Institute of Technology, Gumi, Gyeongbuk, South Korea
| | - Jin Kuk Kim
- B&VIIT Eye Center, Seoul, South Korea
- VISUWORKS, Seoul, South Korea
| | - Ik Hee Ryu
- B&VIIT Eye Center, Seoul, South Korea
- VISUWORKS, Seoul, South Korea
| | - Tae Keun Yoo
- B&VIIT Eye Center, Seoul, South Korea.
- Department of Ophthalmology, Aerospace Medical Center, Republic of Korea Air Force, 635 Danjae-ro, Namil-myeon, Cheongwon-gun, Cheongju, Chungcheongbuk-do, 363-849, South Korea.
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49
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Shao A, Jin K, Li Y, Lou L, Zhou W, Ye J. Overview of global publications on machine learning in diabetic retinopathy from 2011 to 2021: Bibliometric analysis. Front Endocrinol (Lausanne) 2022; 13:1032144. [PMID: 36589855 PMCID: PMC9797582 DOI: 10.3389/fendo.2022.1032144] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/05/2022] [Indexed: 12/23/2022] Open
Abstract
PURPOSE To comprehensively analyze and discuss the publications on machine learning (ML) in diabetic retinopathy (DR) following a bibliometric approach. METHODS The global publications on ML in DR from 2011 to 2021 were retrieved from the Web of Science Core Collection (WoSCC) database. We analyzed the publication and citation trend over time and identified highly-cited articles, prolific countries, institutions, journals and the most relevant research domains. VOSviewer and Wordcloud are used to visualize the mainstream research topics and evolution of subtopics in the form of co-occurrence maps of keywords. RESULTS By analyzing a total of 1147 relevant publications, this study found a rapid increase in the number of annual publications, with an average growth rate of 42.68%. India and China were the most productive countries. IEEE Access was the most productive journal in this field. In addition, some notable common points were found in the highly-cited articles. The keywords analysis showed that "diabetic retinopathy", "classification", and "fundus images" were the most frequent keywords for the entire period, as automatic diagnosis of DR was always the mainstream topic in the relevant field. The evolution of keywords highlighted some breakthroughs, including "deep learning" and "optical coherence tomography", indicating the advance in technologies and changes in the research attention. CONCLUSIONS As new research topics have emerged and evolved, studies are becoming increasingly diverse and extensive. Multiple modalities of medical data, new ML techniques and constantly optimized algorithms are the future trends in this multidisciplinary field.
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Affiliation(s)
- An Shao
- Department of Ophthalmology, the Second Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, China
| | - Kai Jin
- Department of Ophthalmology, the Second Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, China
| | - Yunxiang Li
- College of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China
| | - Lixia Lou
- Department of Ophthalmology, the Second Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, China
| | - Wuyuan Zhou
- Zhejiang Academy of Science and Technology Information, Hangzhou, China
- *Correspondence: Juan Ye, ; Wuyuan Zhou,
| | - Juan Ye
- Department of Ophthalmology, the Second Affiliated Hospital of Zhejiang University, College of Medicine, Hangzhou, China
- *Correspondence: Juan Ye, ; Wuyuan Zhou,
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50
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Arsalan M, Haider A, Choi J, Park KR. Diabetic and Hypertensive Retinopathy Screening in Fundus Images Using Artificially Intelligent Shallow Architectures. J Pers Med 2021; 12:jpm12010007. [PMID: 35055322 PMCID: PMC8777982 DOI: 10.3390/jpm12010007] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 12/20/2021] [Accepted: 12/20/2021] [Indexed: 12/25/2022] Open
Abstract
Retinal blood vessels are considered valuable biomarkers for the detection of diabetic retinopathy, hypertensive retinopathy, and other retinal disorders. Ophthalmologists analyze retinal vasculature by manual segmentation, which is a tedious task. Numerous studies have focused on automatic retinal vasculature segmentation using different methods for ophthalmic disease analysis. However, most of these methods are computationally expensive and lack robustness. This paper proposes two new shallow deep learning architectures: dual-stream fusion network (DSF-Net) and dual-stream aggregation network (DSA-Net) to accurately detect retinal vasculature. The proposed method uses semantic segmentation in raw color fundus images for the screening of diabetic and hypertensive retinopathies. The proposed method's performance is assessed using three publicly available fundus image datasets: Digital Retinal Images for Vessel Extraction (DRIVE), Structured Analysis of Retina (STARE), and Children Heart Health Study in England Database (CHASE-DB1). The experimental results revealed that the proposed method provided superior segmentation performance with accuracy (Acc), sensitivity (SE), specificity (SP), and area under the curve (AUC) of 96.93%, 82.68%, 98.30%, and 98.42% for DRIVE, 97.25%, 82.22%, 98.38%, and 98.15% for CHASE-DB1, and 97.00%, 86.07%, 98.00%, and 98.65% for STARE datasets, respectively. The experimental results also show that the proposed DSA-Net provides higher SE compared to the existing approaches. It means that the proposed method detected the minor vessels and provided the least false negatives, which is extremely important for diagnosis. The proposed method provides an automatic and accurate segmentation mask that can be used to highlight the vessel pixels. This detected vasculature can be utilized to compute the ratio between the vessel and the non-vessel pixels and distinguish between diabetic and hypertensive retinopathies, and morphology can be analyzed for related retinal disorders.
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