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Li F, Wang D, Yang Z, Zhang Y, Jiang J, Liu X, Kong K, Zhou F, Tham CC, Medeiros F, Han Y, Grzybowski A, Zangwill LM, Lam DSC, Zhang X. The AI Revolution in Glaucoma: Bridging Challenges with Opportunities. Prog Retin Eye Res 2024:101291. [PMID: 39186968 DOI: 10.1016/j.preteyeres.2024.101291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/19/2024] [Accepted: 08/19/2024] [Indexed: 08/28/2024]
Abstract
Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the "black box" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.
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Affiliation(s)
- Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Deming Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Zefeng Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Yinhang Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Jiaxuan Jiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Xiaoyi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Kangjie Kong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Fengqi Zhou
- Ophthalmology, Mayo Clinic Health System, Eau Claire, Wisconsin, USA.
| | - Clement C Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Felipe Medeiros
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA.
| | - Ying Han
- University of California, San Francisco, Department of Ophthalmology, San Francisco, CA, USA; The Francis I. Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, California, USA.
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
| | - Linda M Zangwill
- Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, CA, USA.
| | - Dennis S C Lam
- The International Eye Research Institute of The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; The C-MER Dennis Lam & Partners Eye Center, C-MER International Eye Care Group, Hong Kong, China.
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
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Choi JY, Yoo TK. Development of a novel scoring system for glaucoma risk based on demographic and laboratory factors using ChatGPT-4. Med Biol Eng Comput 2024:10.1007/s11517-024-03182-0. [PMID: 39129037 DOI: 10.1007/s11517-024-03182-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2024] [Accepted: 08/01/2024] [Indexed: 08/13/2024]
Abstract
We developed a scoring system for assessing glaucoma risk using demographic and laboratory factors by employing a no-code approach (automated coding) using ChatGPT-4. Comprehensive health checkup data were collected from the Korea National Health and Nutrition Examination Survey. Using ChatGPT-4, logistic regression was conducted to predict glaucoma without coding or manual numerical processes, and the scoring system was developed based on the odds ratios (ORs). ChatGPT-4 also facilitated the no-code creation of an easy-to-use risk calculator for glaucoma. The ORs for the high-risk groups were calculated to measure performance. ChatGPT-4 automatically developed a scoring system based on demographic and laboratory factors, and successfully implemented a risk calculator tool. The predictive ability of the scoring system was comparable to that of traditional machine learning approaches. For high-risk groups with 1-2, 3-4, and 5 + points, the calculated ORs for glaucoma were 1.87, 2.72, and 15.36 in the validation set, respectively, compared with the group with 0 or fewer points. This study presented a novel no-code approach for developing a glaucoma risk assessment tool using ChatGPT-4, highlighting its potential for democratizing advanced predictive analytics, making them readily available for clinical use in glaucoma detection.
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Affiliation(s)
- Joon Yul Choi
- Department of Biomedical Engineering, Yonsei University, Wonju, South Korea
| | - Tae Keun Yoo
- Department of Ophthalmology, Hangil Eye Hospital, 35 Bupyeong-Daero, Bupyeong-Gu, Incheon, 21388, South Korea.
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Grzybowski A, Jin K, Zhou J, Pan X, Wang M, Ye J, Wong TY. Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review. Ophthalmol Ther 2024; 13:2125-2149. [PMID: 38913289 PMCID: PMC11246322 DOI: 10.1007/s40123-024-00981-4] [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: 02/19/2024] [Accepted: 04/15/2024] [Indexed: 06/25/2024] Open
Abstract
We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.
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Affiliation(s)
- Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznań , Poland.
| | - Kai Jin
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jingxin Zhou
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiangji Pan
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Meizhu Wang
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China
| | - Juan Ye
- Eye Center, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Tien Y Wong
- School of Clinical Medicine, Tsinghua Medicine, Tsinghua University, Beijing, China
- Singapore Eye Research Institute, Singapore National Eye Center, Singapore, Singapore
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Jan C, He M, Vingrys A, Zhu Z, Stafford RS. Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia. Eye (Lond) 2024; 38:2003-2013. [PMID: 38514852 PMCID: PMC11269618 DOI: 10.1038/s41433-024-03026-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 02/05/2024] [Accepted: 03/08/2024] [Indexed: 03/23/2024] Open
Abstract
Glaucoma is the commonest cause of irreversible blindness worldwide, with over 70% of people affected remaining undiagnosed. Early detection is crucial for halting progressive visual impairment in glaucoma patients, as there is no cure available. This narrative review aims to: identify reasons for the significant under-diagnosis of glaucoma globally, particularly in Australia, elucidate the role of primary healthcare in glaucoma diagnosis using Australian healthcare as an example, and discuss how recent advances in artificial intelligence (AI) can be implemented to improve diagnostic outcomes. Glaucoma is a prevalent disease in ageing populations and can have improved visual outcomes through appropriate treatment, making it essential for general medical practice. In countries such as Australia, New Zealand, Canada, USA, and the UK, optometrists serve as the gatekeepers for primary eye care, and glaucoma detection often falls on their shoulders. However, there is significant variation in the capacity for glaucoma diagnosis among eye professionals. Automation with Artificial Intelligence (AI) analysis of optic nerve photos can help optometrists identify high-risk changes and mitigate the challenges of image interpretation rapidly and consistently. Despite its potential, there are significant barriers and challenges to address before AI can be deployed in primary healthcare settings, including external validation, high quality real-world implementation, protection of privacy and cybersecurity, and medico-legal implications. Overall, the incorporation of AI technology in primary healthcare has the potential to reduce the global prevalence of undiagnosed glaucoma cases by improving diagnostic accuracy and efficiency.
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Affiliation(s)
- Catherine Jan
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia.
- Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia.
- Lost Child's Vision Project, Sydney, NSW, Australia.
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia
- Centre for Eye and Vision Research, The Hong Kong Polytechnic University, Kowloon, TU428, Hong Kong SAR
| | - Algis Vingrys
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Randall S Stafford
- Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, USA
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Founti P, Stuart K, Nolan WP, Khawaja AP, Foster PJ. Screening Strategies and Methodologies. J Glaucoma 2024; 33:S15-S20. [PMID: 39149948 DOI: 10.1097/ijg.0000000000002426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/02/2024] [Indexed: 08/17/2024]
Abstract
PRCIS While glaucoma is a leading cause of irreversible vision loss, it presents technical challenges in the design and implementation of screening. New technologies such as PRS and AI offer potential improvements in our ability to identify people at high risk of sight loss from glaucoma and may improve the viability of screening for this important disease. PURPOSE To review the current evidence and concepts around screening for glaucoma. METHODS/RESULTS A group of glaucoma-focused clinician scientists drew on knowledge and experience around glaucoma, its etiology, and the options for screening. Glaucoma is a chronic progressive optic neuropathy affecting around 76 million individuals worldwide and is the leading cause of irreversible blindness globally. Early stages of the disease are asymptomatic meaning a substantial proportion of cases remain undiagnosed. Early detection and timely intervention reduce the risk of glaucoma-related visual morbidity. However, imperfect tests and a relatively low prevalence currently limit the viability of population-based screening approaches. The diagnostic yield of opportunistic screening strategies, relying on the identification of disease during unrelated health care encounters, such as cataract clinics and diabetic retinopathy screening programs, focusing on older people and/or those with a family history, are hindered by a large number of false-positive and false-negative results. Polygenic risk scores (PRS) offer personalized risk assessment for adult-onset glaucoma. In addition, artificial intelligence (AI) algorithms have shown impressive performance, comparable to expert humans, in discriminating between potentially glaucomatous and non-glaucomatous eyes. These emerging technologies may offer a meaningful improvement in diagnostic yield in glaucoma screening. CONCLUSIONS While glaucoma is a leading cause of irreversible vision loss, it presents technical challenges in the design and implementation of screening. New technologies such as PRS and AI offer potential improvements in our ability to identify people at high risk of sight loss from glaucoma and may improve the viability of screening for this important disease.
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Affiliation(s)
| | - Kelsey Stuart
- Ocular Informatics Group, Population and Data Sciences Research Theme, University College London Institute of Ophthalmology
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology
| | - Winifred P Nolan
- Glaucoma Service, Moorfields Eye Hospital NHS Foundation Trust
- International Centre for Eye Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Anthony P Khawaja
- Glaucoma Service, Moorfields Eye Hospital NHS Foundation Trust
- Ocular Informatics Group, Population and Data Sciences Research Theme, University College London Institute of Ophthalmology
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology
| | - Paul J Foster
- Glaucoma Service, Moorfields Eye Hospital NHS Foundation Trust
- Ocular Informatics Group, Population and Data Sciences Research Theme, University College London Institute of Ophthalmology
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology
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Yew SME, Chen Y, Goh JHL, Chen DZ, Chun Jin Tan M, Cheng CY, Teck Chang Koh V, Tham YC. Ocular image-based deep learning for predicting refractive error: A systematic review. ADVANCES IN OPHTHALMOLOGY PRACTICE AND RESEARCH 2024; 4:164-172. [PMID: 39114269 PMCID: PMC11305245 DOI: 10.1016/j.aopr.2024.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 06/20/2024] [Accepted: 06/24/2024] [Indexed: 08/10/2024]
Abstract
Background Uncorrected refractive error is a major cause of vision impairment worldwide and its increasing prevalent necessitates effective screening and management strategies. Meanwhile, deep learning, a subset of Artificial Intelligence, has significantly advanced ophthalmological diagnostics by automating tasks that required extensive clinical expertise. Although recent studies have investigated the use of deep learning models for refractive power detection through various imaging techniques, a comprehensive systematic review on this topic is has yet be done. This review aims to summarise and evaluate the performance of ocular image-based deep learning models in predicting refractive errors. Main text We search on three databases (PubMed, Scopus, Web of Science) up till June 2023, focusing on deep learning applications in detecting refractive error from ocular images. We included studies that had reported refractive error outcomes, regardless of publication years. We systematically extracted and evaluated the continuous outcomes (sphere, SE, cylinder) and categorical outcomes (myopia), ground truth measurements, ocular imaging modalities, deep learning models, and performance metrics, adhering to PRISMA guidelines. Nine studies were identified and categorised into three groups: retinal photo-based (n = 5), OCT-based (n = 1), and external ocular photo-based (n = 3).For high myopia prediction, retinal photo-based models achieved AUC between 0.91 and 0.98, sensitivity levels between 85.10% and 97.80%, and specificity levels between 76.40% and 94.50%. For continuous prediction, retinal photo-based models reported MAE ranging from 0.31D to 2.19D, and R 2 between 0.05 and 0.96. The OCT-based model achieved an AUC of 0.79-0.81, sensitivity of 82.30% and 87.20% and specificity of 61.70%-68.90%. For external ocular photo-based models, the AUC ranged from 0.91 to 0.99, sensitivity of 81.13%-84.00% and specificity of 74.00%-86.42%, MAE ranges from 0.07D to 0.18D and accuracy ranges from 81.60% to 96.70%. The reported papers collectively showed promising performances, in particular the retinal photo-based and external eye photo -based DL models. Conclusions The integration of deep learning model and ocular imaging for refractive error detection appear promising. However, their real-world clinical utility in current screening workflow have yet been evaluated and would require thoughtful consideration in design and implementation.
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Affiliation(s)
- Samantha Min Er Yew
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yibing Chen
- School of Chemistry, Chemical Engineering, and Biotechnology, Nanyang Technological University, Singapore
| | | | - David Ziyou Chen
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Ophthalmology, National University Hospital, Singapore
| | - Marcus Chun Jin Tan
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Ophthalmology, National University Hospital, Singapore
| | - Ching-Yu Cheng
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology and Visual Sciences (Eye ACP), Duke-NUS Medical School, Singapore
| | - Victor Teck Chang Koh
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Ophthalmology, National University Hospital, Singapore
| | - Yih-Chung Tham
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology and Visual Sciences (Eye ACP), Duke-NUS Medical School, Singapore
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Beniz LAF, Campos VP, Medeiros FA. Optical Coherence Tomography Versus Optic Disc Photo Assessment in Glaucoma Screening. J Glaucoma 2024; 33:S21-S25. [PMID: 38546240 DOI: 10.1097/ijg.0000000000002392] [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: 11/28/2023] [Accepted: 03/17/2024] [Indexed: 04/18/2024]
Abstract
PRCIS Optical coherence tomography (OCT) and optic disc photography present valuable but distinct capabilities for glaucoma screening. OBJECTIVE This review article examines the strengths and limitations of OCT and optic disc photography in glaucoma screening. METHODS A comprehensive literature review was conducted, focusing on the accuracy, feasibility, cost-effectiveness, and technological advancements in OCT and optic disc photography for glaucoma screening. RESULTS OCT is highly accurate and reproducible but faces limitations due to its cost and less portable nature, making widespread screening challenging. In contrast, optic disc photos are more accessible and cost-effective but are hindered by subjective interpretation and inconsistent grading reliability. A critical challenge in glaucoma screening is achieving a high PPV, particularly given the low prevalence of the disease, which can lead to a significant number of false positives. The advent of artificial intelligence (AI) and deep learning models shows potential in improving the diagnostic accuracy of optic disc photos by automating the detection of glaucomatous optic neuropathy and reducing subjectivity. However, the effectiveness of these AI models hinges on the quality of training data. Using subjective gradings as training data, will carry the limitations of human assessment into the AI system, leading to potential inaccuracies. Conversely, training AI models using objective data from OCT, such as retinal nerve fiber layer thickness, may offer a promising direction. CONCLUSION Both OCT and optic disc photography present valuable but distinct capabilities for glaucoma screening. An approach integrating AI technology might be key in optimizing these methods for effective, large-scale screening programs.
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Affiliation(s)
- Luiz Arthur F Beniz
- Department of Ophthalmology, Bascom Palmer Eye Institute, Miller School of Medicine, University of Miami, Miami, FL
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Xu Y, Liu H, Sun R, Wang H, Huo Y, Wang N, Hu M. Deep learning for predicting circular retinal nerve fiber layer thickness from fundus photographs and diagnosing glaucoma. Heliyon 2024; 10:e33813. [PMID: 39040392 PMCID: PMC11261845 DOI: 10.1016/j.heliyon.2024.e33813] [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: 03/15/2024] [Revised: 06/27/2024] [Accepted: 06/27/2024] [Indexed: 07/24/2024] Open
Abstract
Purpose This study aimed to propose a new deep learning (DL) approach to automatically predict the retinal nerve fiber layer thickness (RNFLT) around optic disc regions in fundus photography trained by optical coherence tomography (OCT) and diagnose glaucoma based on the predicted comprehensive information about RNFLT. Methods A total of 1403 pairs of fundus photographs and OCT RNFLT scans from 1403 eyes of 1196 participants were included. A residual deep neural network was trained to predict the RNFLT for each local image in a fundus photograph, and then a RNFLT report was generated based on the local images. Two indicators were designed based on the generated report. The support vector machines (SVM) algorithm was used to diagnose glaucoma based on the two indicators. Results A strong correlation was found between the predicted and actual RNFLT values on local images. On three testing datasets, we found the Pearson r to be 0.893, 0.850, and 0.831, respectively, and the mean absolute error of the prediction to be 14.345, 17.780, and 19.250 μm, respectively. The area under the receiver operating characteristic curves for discriminating glaucomatous from healthy eyes was 0.860 (95 % confidence interval, 0.799-0.921). Conclusions We established a novel local image-based DL approach to provide comprehensive quantitative information on RNFLT in fundus photographs, which was used to diagnose glaucoma. In addition, training a deep neural network based on local images to predict objective detail information in fundus photographs provided a new paradigm for the diagnosis of ophthalmic diseases.
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Affiliation(s)
- Yongli Xu
- College of Statistics and Data Science, Beijing University of Technology, Beijing, China
- Department of Mathematics, Beijing University of Chemical Technology, Beijing, China
| | - Hanruo Liu
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, China
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China
| | - Run Sun
- Department of Mathematics, Beijing University of Chemical Technology, Beijing, China
| | - Huaizhou Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, China
| | - Yanjiao Huo
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, China
| | - Ningli Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University & Capital Medical University, Beijing Tongren Hospital, Beijing, China
| | - Man Hu
- Department of Ophthalmology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
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Xu Y, Sun R, Hu M, Zeng H. A Dual-Modal Fusion Network Using Optical Coherence Tomography and Fundus Images in Detection of Glaucomatous Optic Neuropathy. Curr Eye Res 2024:1-7. [PMID: 38979787 DOI: 10.1080/02713683.2024.2375401] [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: 05/03/2024] [Accepted: 06/27/2024] [Indexed: 07/10/2024]
Abstract
PURPOSE We designed a dual-modal fusion network to detect glaucomatous optic neuropathy, which utilized both retinal nerve fiber layer thickness from optical coherence tomography reports and fundus images. METHODS A total of 327 healthy subjects (410 eyes) and 87 glaucomatous optic neuropathy patients (113 eyes) were included. The retinal nerve fiber layer thickness from optical coherence tomography reports and fundus images were used as predictors in the dual-modal fusion network to diagnose glaucoma. The area under the receiver operation characteristic curve, accuracy, sensitivity, and specificity were measured to compare our method and other approaches. RESULTS The accuracy of our dual-modal fusion network using both retinal nerve fiber layer thickness from optical coherence tomography reports and fundus images was 0.935 and we achieved a significant larger area under the receiver operation characteristic curve of our method with 0.968 (95% confidence interval, 0.937-0.999). For only using retinal nerve fiber layer thickness, we compared the area under the receiver operation characteristic curves between our network and other three approaches: 0.916 (95% confidence interval, 0.855, 0.977) with our optical coherence tomography Net; 0.841 (95% confidence interval, 0.749, 0.933) with Clock sectors division; 0.862 (95% confidence interval, 0.757, 0.968) with inferior, superior, nasal temporal sectors division and 0.886 (95% confidence interval, 0.815, 0.957) with optic disc sectors division. For only using fundus images, we compared the area under the receiver operation characteristic curves between our network and other two approaches: 0.867 (95% confidence interval: 0.781-0.952) with our Image Net; 0.774 (95% confidence interval: 0.670, 0.878) with ResNet50; 0.747 (95% confidence interval: 0.628, 0.866) with VGG16. CONCLUSION Our dual-modal fusion network utilizing both retinal nerve fiber layer thickness from optical coherence tomography reports and fundus images can diagnose glaucoma with a much better performance than the current approaches based on optical coherence tomography only or fundus images only.
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Affiliation(s)
- Yongli Xu
- College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing, China
- College of Statistics and Data Science, Faculty of Science, Beijing University of Technology, Beijing, China
| | - Run Sun
- College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing, China
| | - Man Hu
- Department of Ophthalmology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
| | - Hui Zeng
- College of Mathematics and Physics, Beijing University of Chemical Technology, Beijing, China
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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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11
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Lim ZW, Li J, Wong D, Chung J, Toh A, Lee JL, Lam C, Balakrishnan M, Chia A, Chua J, Girard M, Hoang QV, Chong R, Wong CW, Saw SM, Schmetterer L, Brennan N, Ang M. Comparison of manual and artificial intelligence-automated choroidal thickness segmentation of optical coherence tomography imaging in myopic adults. EYE AND VISION (LONDON, ENGLAND) 2024; 11:21. [PMID: 38831465 PMCID: PMC11145894 DOI: 10.1186/s40662-024-00385-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 04/17/2024] [Indexed: 06/05/2024]
Abstract
BACKGROUND Myopia affects 1.4 billion individuals worldwide. Notably, there is increasing evidence that choroidal thickness plays an important role in myopia and risk of developing myopia-related conditions. With the advancements in artificial intelligence (AI), choroidal thickness segmentation can now be automated, offering inherent advantages such as better repeatability, reduced grader variability, and less reliance for manpower. Hence, we aimed to evaluate the agreement between AI-automated and manual segmented measurements of subfoveal choroidal thickness (SFCT) using two swept-source optical coherence tomography (OCT) systems. METHODS Subjects aged ≥ 16 years, with myopia of ≥ 0.50 diopters in both eyes, were recruited from the Prospective Myopia Cohort Study in Singapore (PROMYSE). OCT scans were acquired using Triton DRI-OCT and PLEX Elite 9000. OCT images were segmented both automatically with an established SA-Net architecture and manually using a standard technique with adjudication by two independent graders. SFCT was subsequently determined based on the segmentation. The Bland-Altman plot and intraclass correlation coefficient (ICC) were used to evaluate the agreement. RESULTS A total of 229 subjects (456 eyes) with mean [± standard deviation (SD)] age of 34.1 (10.4) years were included. The overall SFCT (mean ± SD) based on manual segmentation was 216.9 ± 82.7 µm with Triton DRI-OCT and 239.3 ± 84.3 µm with PLEX Elite 9000. ICC values demonstrated excellent agreement between AI-automated and manual segmented SFCT measurements (PLEX Elite 9000: ICC = 0.937, 95% CI: 0.922 to 0.949, P < 0.001; Triton DRI-OCT: ICC = 0.887, 95% CI: 0.608 to 0.950, P < 0.001). For PLEX Elite 9000, manual segmented measurements were generally thicker when compared to AI-automated segmented measurements, with a fixed bias of 6.3 µm (95% CI: 3.8 to 8.9, P < 0.001) and proportional bias of 0.120 (P < 0.001). On the other hand, manual segmented measurements were comparatively thinner than AI-automated segmented measurements for Triton DRI-OCT, with a fixed bias of - 26.7 µm (95% CI: - 29.7 to - 23.7, P < 0.001) and proportional bias of - 0.090 (P < 0.001). CONCLUSION We observed an excellent agreement in choroidal segmentation measurements when comparing manual with AI-automated techniques, using images from two SS-OCT systems. Given its edge over manual segmentation, automated segmentation may potentially emerge as the primary method of choroidal thickness measurement in the future.
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Affiliation(s)
- Zhi Wei Lim
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Jonathan Li
- Department of Ophthalmology, University of California, San Francisco, CA, USA
| | - Damon Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore Eye Research Institute and Nanyang Technological University, Singapore, Singapore
- Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
| | - Joey Chung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Angeline Toh
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Jia Ling Lee
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Crystal Lam
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Maithily Balakrishnan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Audrey Chia
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Jacqueline Chua
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore Eye Research Institute and Nanyang Technological University, Singapore, Singapore
| | - Michael Girard
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
| | - Quan V Hoang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Vagelos College of Physicians and Surgeons, New York, NY, USA
| | - Rachel Chong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Chee Wai Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
| | - Seang Mei Saw
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- SERI-NTU Advanced Ocular Engineering (STANCE), Singapore Eye Research Institute and Nanyang Technological University, Singapore, Singapore
- Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland
- School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore, Singapore
- Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria
| | | | - Marcus Ang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
- Ophthalmology and Visual Sciences Department, Duke-NUS Medical School, Singapore, Singapore.
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12
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Zeng Y, Zhang X, Wang J, Usui A, Ichiji K, Bukovsky I, Chou S, Funayama M, Homma N. Inconsistency between Human Observation and Deep Learning Models: Assessing Validity of Postmortem Computed Tomography Diagnosis of Drowning. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024; 37:1-10. [PMID: 38336949 PMCID: PMC11169324 DOI: 10.1007/s10278-024-00974-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 10/18/2023] [Accepted: 11/17/2023] [Indexed: 02/12/2024]
Abstract
Drowning diagnosis is a complicated process in the autopsy, even with the assistance of autopsy imaging and the on-site information from where the body was found. Previous studies have developed well-performed deep learning (DL) models for drowning diagnosis. However, the validity of the DL models was not assessed, raising doubts about whether the learned features accurately represented the medical findings observed by human experts. In this paper, we assessed the medical validity of DL models that had achieved high classification performance for drowning diagnosis. This retrospective study included autopsy cases aged 8-91 years who underwent postmortem computed tomography between 2012 and 2021 (153 drowning and 160 non-drowning cases). We first trained three deep learning models from a previous work and generated saliency maps that highlight important features in the input. To assess the validity of models, pixel-level annotations were created by four radiological technologists and further quantitatively compared with the saliency maps. All the three models demonstrated high classification performance with areas under the receiver operating characteristic curves of 0.94, 0.97, and 0.98, respectively. On the other hand, the assessment results revealed unexpected inconsistency between annotations and models' saliency maps. In fact, each model had, respectively, around 30%, 40%, and 80% of irrelevant areas in the saliency maps, suggesting the predictions of the DL models might be unreliable. The result alerts us in the careful assessment of DL tools, even those with high classification performance.
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Affiliation(s)
- Yuwen Zeng
- Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan.
| | - Xiaoyong Zhang
- National Institute of Technology, Sendai College, Sendai, Japan
| | - Jiaoyang Wang
- Department of Intelligent Biomedical System Engineering, Graduate School of Biomedical Engineering, Tohoku University, Sendai, Japan
| | - Akihito Usui
- Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Kei Ichiji
- Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Ivo Bukovsky
- Faculty of Science, University of South Bohemia in Ceske Budejovice, Ceske Budejovice, Czech Republic
- Mechanical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Shuoyan Chou
- Department of Industrial Management, National Taiwan University of Science and Technology, Taipei, Taiwan
| | - Masato Funayama
- Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Noriyasu Homma
- Department of Radiological Imaging and Informatics, Tohoku University Graduate School of Medicine, Sendai, Japan
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13
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Diener R, Renz AW, Eckhard F, Segbert H, Eter N, Malcherek A, Biermann J. Automated Classification of Physiologic, Glaucomatous, and Glaucoma-Suspected Optic Discs Using Machine Learning. Diagnostics (Basel) 2024; 14:1073. [PMID: 38893600 PMCID: PMC11171940 DOI: 10.3390/diagnostics14111073] [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: 04/15/2024] [Revised: 05/09/2024] [Accepted: 05/17/2024] [Indexed: 06/21/2024] Open
Abstract
In order to generate a machine learning algorithm (MLA) that can support ophthalmologists with the diagnosis of glaucoma, a carefully selected dataset that is based on clinically confirmed glaucoma patients as well as borderline cases (e.g., patients with suspected glaucoma) is required. The clinical annotation of datasets is usually performed at the expense of the data volume, which results in poorer algorithm performance. This study aimed to evaluate the application of an MLA for the automated classification of physiological optic discs (PODs), glaucomatous optic discs (GODs), and glaucoma-suspected optic discs (GSODs). Annotation of the data to the three groups was based on the diagnosis made in clinical practice by a glaucoma specialist. Color fundus photographs and 14 types of metadata (including visual field testing, retinal nerve fiber layer thickness, and cup-disc ratio) of 1168 eyes from 584 patients (POD = 321, GOD = 336, GSOD = 310) were used for the study. Machine learning (ML) was performed in the first step with the color fundus photographs only and in the second step with the images and metadata. Sensitivity, specificity, and accuracy of the classification of GSOD vs. GOD and POD vs. GOD were evaluated. Classification of GOD vs. GSOD and GOD vs. POD performed in the first step had AUCs of 0.84 and 0.88, respectively. By combining the images and metadata, the AUCs increased to 0.92 and 0.99, respectively. By combining images and metadata, excellent performance of the MLA can be achieved despite having only a small amount of data, thus supporting ophthalmologists with glaucoma diagnosis.
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Affiliation(s)
- Raphael Diener
- Department of Ophthalmology, University of Muenster Medical Center, 48149 Muenster, Germany; (H.S.); (N.E.); (J.B.)
| | - Alexander W. Renz
- Department of Informatics, University of Applied Sciences Darmstadt, 64295 Darmstadt, Germany; (A.W.R.); (A.M.)
| | - Florian Eckhard
- Department of Informatics, Technical University of Munich, 80333 Munich, Germany;
| | - Helmar Segbert
- Department of Ophthalmology, University of Muenster Medical Center, 48149 Muenster, Germany; (H.S.); (N.E.); (J.B.)
| | - Nicole Eter
- Department of Ophthalmology, University of Muenster Medical Center, 48149 Muenster, Germany; (H.S.); (N.E.); (J.B.)
| | - Arnim Malcherek
- Department of Informatics, University of Applied Sciences Darmstadt, 64295 Darmstadt, Germany; (A.W.R.); (A.M.)
| | - Julia Biermann
- Department of Ophthalmology, University of Muenster Medical Center, 48149 Muenster, Germany; (H.S.); (N.E.); (J.B.)
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14
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Martin E, Cook AG, Frost SM, Turner AW, Chen FK, McAllister IL, Nolde JM, Schlaich MP. Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs. Eye (Lond) 2024:10.1038/s41433-024-03085-2. [PMID: 38734746 DOI: 10.1038/s41433-024-03085-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2023] [Revised: 04/03/2024] [Accepted: 04/11/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND/OBJECTIVES Artificial intelligence can assist with ocular image analysis for screening and diagnosis, but it is not yet capable of autonomous full-spectrum screening. Hypothetically, false-positive results may have unrealized screening potential arising from signals persisting despite training and/or ambiguous signals such as from biomarker overlap or high comorbidity. The study aimed to explore the potential to detect clinically useful incidental ocular biomarkers by screening fundus photographs of hypertensive adults using diabetic deep learning algorithms. SUBJECTS/METHODS Patients referred for treatment-resistant hypertension were imaged at a hospital unit in Perth, Australia, between 2016 and 2022. The same 45° colour fundus photograph selected for each of the 433 participants imaged was processed by three deep learning algorithms. Two expert retinal specialists graded all false-positive results for diabetic retinopathy in non-diabetic participants. RESULTS Of the 29 non-diabetic participants misclassified as positive for diabetic retinopathy, 28 (97%) had clinically useful retinal biomarkers. The models designed to screen for fewer diseases captured more incidental disease. All three algorithms showed a positive correlation between severity of hypertensive retinopathy and misclassified diabetic retinopathy. CONCLUSIONS The results suggest that diabetic deep learning models may be responsive to hypertensive and other clinically useful retinal biomarkers within an at-risk, hypertensive cohort. Observing that models trained for fewer diseases captured more incidental pathology increases confidence in signalling hypotheses aligned with using self-supervised learning to develop autonomous comprehensive screening. Meanwhile, non-referable and false-positive outputs of other deep learning screening models could be explored for immediate clinical use in other populations.
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Affiliation(s)
- Eve Martin
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Kensington, WA, Australia.
- School of Population and Global Health, The University of Western Australia, Crawley, Australia.
- Dobney Hypertension Centre - Royal Perth Hospital Unit, Medical School, The University of Western Australia, Perth, Australia.
- Australian e-Health Research Centre, Floreat, WA, Australia.
| | - Angus G Cook
- School of Population and Global Health, The University of Western Australia, Crawley, Australia
| | - Shaun M Frost
- Commonwealth Scientific and Industrial Research Organisation (CSIRO), Kensington, WA, Australia
- Australian e-Health Research Centre, Floreat, WA, Australia
| | - Angus W Turner
- Lions Eye Institute, Nedlands, WA, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
| | - Fred K Chen
- Lions Eye Institute, Nedlands, WA, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
- Centre for Eye Research Australia, The Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, The University of Melbourne, East Melbourne, VIC, Australia
- Ophthalmology Department, Royal Perth Hospital, Perth, Australia
| | - Ian L McAllister
- Lions Eye Institute, Nedlands, WA, Australia
- Centre for Ophthalmology and Visual Science, The University of Western Australia, Perth, Australia
| | - Janis M Nolde
- Dobney Hypertension Centre - Royal Perth Hospital Unit, Medical School, The University of Western Australia, Perth, Australia
- Departments of Cardiology and Nephrology, Royal Perth Hospital, Perth, Australia
| | - Markus P Schlaich
- Dobney Hypertension Centre - Royal Perth Hospital Unit, Medical School, The University of Western Australia, Perth, Australia
- Departments of Cardiology and Nephrology, Royal Perth Hospital, Perth, Australia
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15
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Arias-Serrano I, Velásquez-López PA, Avila-Briones LN, Laurido-Mora FC, Villalba-Meneses F, Tirado-Espin A, Cruz-Varela J, Almeida-Galárraga D. Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB - retrained AlexNet convolutional neural network. F1000Res 2024; 12:14. [PMID: 38826575 PMCID: PMC11143403 DOI: 10.12688/f1000research.122288.2] [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] [Accepted: 03/18/2024] [Indexed: 06/04/2024] Open
Abstract
Background Glaucoma and diabetic retinopathy (DR) are the leading causes of irreversible retinal damage leading to blindness. Early detection of these diseases through regular screening is especially important to prevent progression. Retinal fundus imaging serves as the principal method for diagnosing glaucoma and DR. Consequently, automated detection of eye diseases represents a significant application of retinal image analysis. Compared with classical diagnostic techniques, image classification by convolutional neural networks (CNN) exhibits potential for effective eye disease detection. Methods This paper proposes the use of MATLAB - retrained AlexNet CNN for computerized eye diseases identification, particularly glaucoma and diabetic retinopathy, by employing retinal fundus images. The acquisition of the database was carried out through free access databases and access upon request. A transfer learning technique was employed to retrain the AlexNet CNN for non-disease (Non_D), glaucoma (Sus_G) and diabetic retinopathy (Sus_R) classification. Moreover, model benchmarking was conducted using ResNet50 and GoogLeNet architectures. A Grad-CAM analysis is also incorporated for each eye condition examined. Results Metrics for validation accuracy, false positives, false negatives, precision, and recall were reported. Validation accuracies for the NetTransfer (I-V) and netAlexNet ranged from 89.7% to 94.3%, demonstrating varied effectiveness in identifying Non_D, Sus_G, and Sus_R categories, with netAlexNet achieving a 93.2% accuracy in the benchmarking of models against netResNet50 at 93.8% and netGoogLeNet at 90.4%. Conclusions This study demonstrates the efficacy of using a MATLAB-retrained AlexNet CNN for detecting glaucoma and diabetic retinopathy. It emphasizes the need for automated early detection tools, proposing CNNs as accessible solutions without replacing existing technologies.
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Affiliation(s)
- Isaac Arias-Serrano
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
| | - Paolo A. Velásquez-López
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
| | - Laura N. Avila-Briones
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
| | - Fanny C. Laurido-Mora
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
| | - Fernando Villalba-Meneses
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
- Department of Design and Manufacturing Engineering, University of Zaragoza, Zaragoza, Aragon, 50018, Spain
| | - Andrés Tirado-Espin
- School of Mathematical and Computational Sciences, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
| | - Jonathan Cruz-Varela
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
| | - Diego Almeida-Galárraga
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
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16
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Arias-Serrano I, Velásquez-López PA, Avila-Briones LN, Laurido-Mora FC, Villalba-Meneses F, Tirado-Espin A, Cruz-Varela J, Almeida-Galárraga D. Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB - retrained AlexNet convolutional neural network. F1000Res 2024; 12:14. [PMID: 38826575 PMCID: PMC11143403 DOI: 10.12688/f1000research.122288.1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 03/18/2024] [Indexed: 08/15/2024] Open
Abstract
BACKGROUND Glaucoma and diabetic retinopathy (DR) are the leading causes of irreversible retinal damage leading to blindness. Early detection of these diseases through regular screening is especially important to prevent progression. Retinal fundus imaging serves as the principal method for diagnosing glaucoma and DR. Consequently, automated detection of eye diseases represents a significant application of retinal image analysis. Compared with classical diagnostic techniques, image classification by convolutional neural networks (CNN) exhibits potential for effective eye disease detection. METHODS This paper proposes the use of MATLAB - retrained AlexNet CNN for computerized eye diseases identification, particularly glaucoma and diabetic retinopathy, by employing retinal fundus images. The acquisition of the database was carried out through free access databases and access upon request. A transfer learning technique was employed to retrain the AlexNet CNN for non-disease (Non_D), glaucoma (Sus_G) and diabetic retinopathy (Sus_R) classification. Moreover, model benchmarking was conducted using ResNet50 and GoogLeNet architectures. A Grad-CAM analysis is also incorporated for each eye condition examined. RESULTS Metrics for validation accuracy, false positives, false negatives, precision, and recall were reported. Validation accuracies for the NetTransfer (I-V) and netAlexNet ranged from 89.7% to 94.3%, demonstrating varied effectiveness in identifying Non_D, Sus_G, and Sus_R categories, with netAlexNet achieving a 93.2% accuracy in the benchmarking of models against netResNet50 at 93.8% and netGoogLeNet at 90.4%. CONCLUSIONS This study demonstrates the efficacy of using a MATLAB-retrained AlexNet CNN for detecting glaucoma and diabetic retinopathy. It emphasizes the need for automated early detection tools, proposing CNNs as accessible solutions without replacing existing technologies.
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Affiliation(s)
- Isaac Arias-Serrano
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
| | - Paolo A. Velásquez-López
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
| | - Laura N. Avila-Briones
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
| | - Fanny C. Laurido-Mora
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
| | - Fernando Villalba-Meneses
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
- Department of Design and Manufacturing Engineering, University of Zaragoza, Zaragoza, Aragon, 50018, Spain
| | - Andrés Tirado-Espin
- School of Mathematical and Computational Sciences, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
| | - Jonathan Cruz-Varela
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
| | - Diego Almeida-Galárraga
- School of Biological Sciences and Engineering, Universidad Yachay Tech, Urcuquí, Imbabura, 100119, Ecuador
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17
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Yap BP, Kelvin LZ, Toh EQ, Low KY, Rani SK, Goh EJH, Hui VYC, Ng BK, Lim TH. Generalizability of Deep Neural Networks for Vertical Cup-to-Disc Ratio Estimation in Ultra-Widefield and Smartphone-Based Fundus Images. Transl Vis Sci Technol 2024; 13:6. [PMID: 38568608 PMCID: PMC10996969 DOI: 10.1167/tvst.13.4.6] [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: 10/12/2023] [Accepted: 02/19/2024] [Indexed: 04/05/2024] Open
Abstract
Purpose To develop and validate a deep learning system (DLS) for estimation of vertical cup-to-disc ratio (vCDR) in ultra-widefield (UWF) and smartphone-based fundus images. Methods A DLS consisting of two sequential convolutional neural networks (CNNs) to delineate optic disc (OD) and optic cup (OC) boundaries was developed using 800 standard fundus images from the public REFUGE data set. The CNNs were tested on 400 test images from the REFUGE data set and 296 UWF and 300 smartphone-based images from a teleophthalmology clinic. vCDRs derived from the delineated OD/OC boundaries were compared with optometrists' annotations using mean absolute error (MAE). Subgroup analysis was conducted to study the impact of peripapillary atrophy (PPA), and correlation study was performed to investigate potential correlations between sectoral CDR (sCDR) and retinal nerve fiber layer (RNFL) thickness. Results The system achieved MAEs of 0.040 (95% CI, 0.037-0.043) in the REFUGE test images, 0.068 (95% CI, 0.061-0.075) in the UWF images, and 0.084 (95% CI, 0.075-0.092) in the smartphone-based images. There was no statistical significance in differences between PPA and non-PPA images. Weak correlation (r = -0.4046, P < 0.05) between sCDR and RNFL thickness was found only in the superior sector. Conclusions We developed a deep learning system that estimates vCDR from standard, UWF, and smartphone-based images. We also described anatomic peripapillary adversarial lesion and its potential impact on OD/OC delineation. Translational Relevance Artificial intelligence can estimate vCDR from different types of fundus images and may be used as a general and interpretable screening tool to improve community reach for diagnosis and management of glaucoma.
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Affiliation(s)
- Boon Peng Yap
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Li Zhenghao Kelvin
- Department of Ophthalmology, Tan Tock Seng Hospital, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- National Healthcare Group Eye Institute, Singapore, Singapore
| | - En Qi Toh
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
| | - Kok Yao Low
- Department of Ophthalmology, Tan Tock Seng Hospital, Singapore, Singapore
- National Healthcare Group Eye Institute, Singapore, Singapore
| | - Sumaya Khan Rani
- Department of Ophthalmology, Tan Tock Seng Hospital, Singapore, Singapore
- National Healthcare Group Eye Institute, Singapore, Singapore
| | - Eunice Jin Hui Goh
- Department of Ophthalmology, Tan Tock Seng Hospital, Singapore, Singapore
- National Healthcare Group Eye Institute, Singapore, Singapore
| | - Vivien Yip Cherng Hui
- Department of Ophthalmology, Tan Tock Seng Hospital, Singapore, Singapore
- National Healthcare Group Eye Institute, Singapore, Singapore
| | - Beng Koon Ng
- School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore
| | - Tock Han Lim
- Department of Ophthalmology, Tan Tock Seng Hospital, Singapore, Singapore
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
- National Healthcare Group Eye Institute, Singapore, Singapore
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18
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Rao DP, Shroff S, Savoy FM, S S, Hsu CK, Negiloni K, Pradhan ZS, P V J, Sivaraman A, Rao HL. Evaluation of an offline, artificial intelligence system for referable glaucoma screening using a smartphone-based fundus camera: a prospective study. Eye (Lond) 2024; 38:1104-1111. [PMID: 38092938 PMCID: PMC11009383 DOI: 10.1038/s41433-023-02826-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2022] [Revised: 10/27/2023] [Accepted: 11/01/2023] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND/OBJECTIVES An affordable and scalable screening model is critical for undetected glaucoma. The study evaluated the performance of an offline, smartphone-based AI system for the detection of referable glaucoma against two benchmarks: specialist diagnosis following full glaucoma workup and consensus image grading. SUBJECTS/METHODS This prospective study (tertiary glaucoma centre, India) included 243 subjects with varying severity of glaucoma and control group without glaucoma. Disc-centred images were captured using a validated smartphone-based fundus camera analysed by the AI system and graded by specialists. Diagnostic ability of the AI in detecting referable Glaucoma (Confirmed glaucoma) and no referable Glaucoma (Suspects and No glaucoma) when compared to a final diagnosis (comprehensive glaucoma workup) and majority grading (image grading) by Glaucoma specialists (pre-defined criteria) were evaluated. RESULTS The AI system demonstrated a sensitivity and specificity of 93.7% (95% CI: 87.6-96.9%) and 85.6% (95% CI:78.6-90.6%), respectively, in the detection of referable glaucoma when compared against final diagnosis following full glaucoma workup. True negative rate in definite non-glaucoma cases was 94.7% (95% CI: 87.2-97.9%). Amongst the false negatives were 4 early and 3 moderate glaucoma. When the same set of images provided to the AI was also provided to the specialists for image grading, specialists detected 60% (67/111) of true glaucoma cases versus a detection rate of 94% (104/111) by the AI. CONCLUSION The AI tool showed robust performance when compared against a stringent benchmark. It had modest over-referral of normal subjects despite being challenged with fundus images alone. The next step involves a population-level assessment.
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Affiliation(s)
| | - Sujani Shroff
- Narayana Nethralaya Eye Hospital, Glaucoma Services, Bangalore, India
| | | | - Shruthi S
- Narayana Nethralaya Eye Hospital, Glaucoma Services, Bangalore, India
| | - Chao-Kai Hsu
- Medios Technologies Pte Ltd, Singapore, Singapore
| | | | | | - Jayasree P V
- Narayana Nethralaya Eye Hospital, Glaucoma Services, Bangalore, India
| | | | - Harsha L Rao
- Narayana Nethralaya Eye Hospital, Glaucoma Services, Bangalore, India
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19
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Perumalraja R, Felcia Logan's Deshna B, Swetha N. Statistical performance review on diagnosis of leukemia, glaucoma and diabetes mellitus using AI. Stat Med 2024; 43:1227-1237. [PMID: 38247116 DOI: 10.1002/sim.10004] [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: 12/13/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024]
Abstract
The growth of artificial intelligence (AI) in the healthcare industry tremendously increases the patient outcomes by reshaping the way we diagnose, treat and monitor patients. AI-based innovation in healthcare include exploration of drugs, personalized medicine, clinical diagnosis investigations, robotic-assisted surgery, verified prescriptions, pregnancy care for women, radiology, and reviewed patient information analytics. However, prediction of AI-based solutions are depends mainly on the implementation of statistical algorithms and input data set. In this article, statistical performance review on various algorithms, Accuracy, Precision, Recall and F1-Score used to predict the diagnosis of leukemia, glaucoma, and diabetes mellitus is presented. Review on statistical algorithms' performance, used for individual disease diagnosis gives a complete picture of various research efforts during the last two decades. At the end of statistical review on each disease diagnosis, we have discussed our inferences that will give future directions for the new researchers on selection of AI statistical algorithm as well as the input data set.
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Affiliation(s)
- Rengaraju Perumalraja
- Department of Information Technology, Velammal College of Engineering and Technology, Madurai, India
| | - B Felcia Logan's Deshna
- Department of Information Technology, Velammal College of Engineering and Technology, Madurai, India
| | - N Swetha
- Department of Information Technology, Velammal College of Engineering and Technology, Madurai, India
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20
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AlShawabkeh M, AlRyalat SA, Al Bdour M, Alni’mat A, Al-Akhras M. The utilization of artificial intelligence in glaucoma: diagnosis versus screening. FRONTIERS IN OPHTHALMOLOGY 2024; 4:1368081. [PMID: 38984126 PMCID: PMC11182276 DOI: 10.3389/fopht.2024.1368081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 02/20/2024] [Indexed: 07/11/2024]
Abstract
With advancements in the implementation of artificial intelligence (AI) in different ophthalmology disciplines, it continues to have a significant impact on glaucoma diagnosis and screening. This article explores the distinct roles of AI in specialized ophthalmology clinics and general practice, highlighting the critical balance between sensitivity and specificity in diagnostic and screening models. Screening models prioritize sensitivity to detect potential glaucoma cases efficiently, while diagnostic models emphasize specificity to confirm disease with high accuracy. AI applications, primarily using machine learning (ML) and deep learning (DL), have been successful in detecting glaucomatous optic neuropathy from colored fundus photographs and other retinal imaging modalities. Diagnostic models integrate data extracted from various forms of modalities (including tests that assess structural optic nerve damage as well as those evaluating functional damage) to provide a more nuanced, accurate and thorough approach to diagnosing glaucoma. As AI continues to evolve, the collaboration between technology and clinical expertise should focus more on improving specificity of glaucoma diagnostic models to assess ophthalmologists to revolutionize glaucoma diagnosis and improve patients care.
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Affiliation(s)
| | - Saif Aldeen AlRyalat
- Department of Ophthalmology, The University of Jordan, Amman, Jordan
- Department of Ophthalmology, Houston Methodist Hospital, Houston, TX, United States
| | - Muawyah Al Bdour
- Department of Ophthalmology, The University of Jordan, Amman, Jordan
| | - Ayat Alni’mat
- Department of Ophthalmology, Al Taif Eye Center, Amman, Jordan
| | - Mousa Al-Akhras
- Department of Computer Information Systems, School of Information Technology and Systems, The University of Jordan, Amman, Jordan
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21
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Bragança CP, Torres JM, Macedo LO, Soares CPDA. Advancements in Glaucoma Diagnosis: The Role of AI in Medical Imaging. Diagnostics (Basel) 2024; 14:530. [PMID: 38473002 DOI: 10.3390/diagnostics14050530] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 02/17/2024] [Accepted: 02/23/2024] [Indexed: 03/14/2024] Open
Abstract
The progress of artificial intelligence algorithms in digital image processing and automatic diagnosis studies of the eye disease glaucoma has been growing and presenting essential advances to guarantee better clinical care for the population. Given the context, this article describes the main types of glaucoma, traditional forms of diagnosis, and presents the global epidemiology of the disease. Furthermore, it explores how studies using artificial intelligence algorithms have been investigated as possible tools to aid in the early diagnosis of this pathology through population screening. Therefore, the related work section presents the main studies and methodologies used in the automatic classification of glaucoma from digital fundus images and artificial intelligence algorithms, as well as the main databases containing images labeled for glaucoma and publicly available for the training of machine learning algorithms.
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Affiliation(s)
- Clerimar Paulo Bragança
- ISUS Unit, Faculty of Science and Technology, University Fernando Pessoa, 4249-004 Porto, Portugal
- Department of Ophthalmology, Eye Hospital of Southern Minas Gerais State, Rua Joaquim Rosa 14, Itanhandu 37464-000, MG, Brazil
| | - José Manuel Torres
- ISUS Unit, Faculty of Science and Technology, University Fernando Pessoa, 4249-004 Porto, Portugal
- Artificial Intelligence and Computer Science Laboratory, LIACC, University of Porto, 4100-000 Porto, Portugal
| | - Luciano Oliveira Macedo
- Department of Ophthalmology, Eye Hospital of Southern Minas Gerais State, Rua Joaquim Rosa 14, Itanhandu 37464-000, MG, Brazil
| | - Christophe Pinto de Almeida Soares
- ISUS Unit, Faculty of Science and Technology, University Fernando Pessoa, 4249-004 Porto, Portugal
- Artificial Intelligence and Computer Science Laboratory, LIACC, University of Porto, 4100-000 Porto, Portugal
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22
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Lee DK, Choi YJ, Lee SJ, Kang HG, Park YR. Development of a deep learning model to distinguish the cause of optic disc atrophy using retinal fundus photography. Sci Rep 2024; 14:5079. [PMID: 38429319 PMCID: PMC10907364 DOI: 10.1038/s41598-024-55054-0] [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: 11/29/2023] [Accepted: 02/20/2024] [Indexed: 03/03/2024] Open
Abstract
The differential diagnosis for optic atrophy can be challenging and requires expensive, time-consuming ancillary testing to determine the cause. While Leber's hereditary optic neuropathy (LHON) and optic neuritis (ON) are both clinically significant causes for optic atrophy, both relatively rare in the general population, contributing to limitations in obtaining large imaging datasets. This study therefore aims to develop a deep learning (DL) model based on small datasets that could distinguish the cause of optic disc atrophy using only fundus photography. We retrospectively reviewed fundus photographs of 120 normal eyes, 30 eyes (15 patients) with genetically-confirmed LHON, and 30 eyes (26 patients) with ON. Images were split into a training dataset and a test dataset and used for model training with ResNet-18. To visualize the critical regions in retinal photographs that are highly associated with disease prediction, Gradient-Weighted Class Activation Map (Grad-CAM) was used to generate image-level attention heat maps and to enhance the interpretability of the DL system. In the 3-class classification of normal, LHON, and ON, the area under the receiver operating characteristic curve (AUROC) was 1.0 for normal, 0.988 for LHON, and 0.990 for ON, clearly differentiating each class from the others with an overall total accuracy of 0.93. Specifically, when distinguishing between normal and disease cases, the precision, recall, and F1 scores were perfect at 1.0. Furthermore, in the differentiation of LHON from other conditions, ON from others, and between LHON and ON, we consistently observed precision, recall, and F1 scores of 0.8. The model performance was maintained until only 10% of the pixel values of the image, identified as important by Grad-CAM, were preserved and the rest were masked, followed by retraining and evaluation.
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Affiliation(s)
- Dong Kyu Lee
- Department of Ophthalmology, Institute of Vision Research, Severance Eye Hospital, Yonsei University College of Medicine, Yonsei-ro 50-1, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Young Jo Choi
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yonsei-ro 50-1, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Seung Jae Lee
- Department of Ophthalmology, Institute of Vision Research, Severance Eye Hospital, Yonsei University College of Medicine, Yonsei-ro 50-1, Seodaemun-gu, Seoul, 03722, Republic of Korea
| | - Hyun Goo Kang
- Department of Ophthalmology, Institute of Vision Research, Severance Eye Hospital, Yonsei University College of Medicine, Yonsei-ro 50-1, Seodaemun-gu, Seoul, 03722, Republic of Korea.
| | - Yu Rang Park
- Department of Biomedical Systems Informatics, Yonsei University College of Medicine, Yonsei-ro 50-1, Seodaemun-gu, Seoul, 03722, Republic of Korea.
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23
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Zhu Y, Salowe R, Chow C, Li S, Bastani O, O'Brien JM. Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection. Bioengineering (Basel) 2024; 11:122. [PMID: 38391608 PMCID: PMC10886285 DOI: 10.3390/bioengineering11020122] [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: 12/04/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/24/2024] Open
Abstract
Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI's potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI's vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside.
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Affiliation(s)
- Yan Zhu
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rebecca Salowe
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Caven Chow
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shuo Li
- Department of Computer & Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Osbert Bastani
- Department of Computer & Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joan M O'Brien
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
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24
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Li B, Chen H, Yu W, Zhang M, Lu F, Ma J, Hao Y, Li X, Hu B, Shen L, Mao J, He X, Wang H, Ding D, Li X, Chen Y. The performance of a deep learning system in assisting junior ophthalmologists in diagnosing 13 major fundus diseases: a prospective multi-center clinical trial. NPJ Digit Med 2024; 7:8. [PMID: 38212607 PMCID: PMC10784504 DOI: 10.1038/s41746-023-00991-9] [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: 03/29/2023] [Accepted: 12/11/2023] [Indexed: 01/13/2024] Open
Abstract
Artificial intelligence (AI)-based diagnostic systems have been reported to improve fundus disease screening in previous studies. This multicenter prospective self-controlled clinical trial aims to evaluate the diagnostic performance of a deep learning system (DLS) in assisting junior ophthalmologists in detecting 13 major fundus diseases. A total of 1493 fundus images from 748 patients were prospectively collected from five tertiary hospitals in China. Nine junior ophthalmologists were trained and annotated the images with or without the suggestions proposed by the DLS. The diagnostic performance was evaluated among three groups: DLS-assisted junior ophthalmologist group (test group), junior ophthalmologist group (control group) and DLS group. The diagnostic consistency was 84.9% (95%CI, 83.0% ~ 86.9%), 72.9% (95%CI, 70.3% ~ 75.6%) and 85.5% (95%CI, 83.5% ~ 87.4%) in the test group, control group and DLS group, respectively. With the help of the proposed DLS, the diagnostic consistency of junior ophthalmologists improved by approximately 12% (95% CI, 9.1% ~ 14.9%) with statistical significance (P < 0.001). For the detection of 13 diseases, the test group achieved significant higher sensitivities (72.2% ~ 100.0%) and comparable specificities (90.8% ~ 98.7%) comparing with the control group (sensitivities, 50% ~ 100%; specificities 96.7 ~ 99.8%). The DLS group presented similar performance to the test group in the detection of any fundus abnormality (sensitivity, 95.7%; specificity, 87.2%) and each of the 13 diseases (sensitivity, 83.3% ~ 100.0%; specificity, 89.0 ~ 98.0%). The proposed DLS provided a novel approach for the automatic detection of 13 major fundus diseases with high diagnostic consistency and assisted to improve the performance of junior ophthalmologists, resulting especially in reducing the risk of missed diagnoses. ClinicalTrials.gov NCT04723160.
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Affiliation(s)
- Bing Li
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Huan Chen
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Weihong Yu
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China
| | - Ming Zhang
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, China
| | - Fang Lu
- Department of Ophthalmology, West China Hospital, Sichuan University, Chengdu, China
| | - Jingxue Ma
- Department of Ophthalmology, Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Yuhua Hao
- Department of Ophthalmology, Second Hospital of Hebei Medical University, Shijiazhuang, China
| | - Xiaorong Li
- Department of Retina, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Bojie Hu
- Department of Retina, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Lijun Shen
- Department of Retina Center, Affiliated Eye Hospital of Wenzhou Medical University, Hangzhou, Zhejiang Province, China
| | - Jianbo Mao
- Department of Retina Center, Affiliated Eye Hospital of Wenzhou Medical University, Hangzhou, Zhejiang Province, China
| | - Xixi He
- School of Information Science and Technology, North China University of Technology, Beijing, China
- Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data, Beijing, China
| | - Hao Wang
- Visionary Intelligence Ltd., Beijing, China
| | | | - Xirong Li
- MoE Key Lab of DEKE, Renmin University of China, Beijing, China
| | - Youxin Chen
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing, China.
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25
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Nam Y, Kim J, Kim K, Park KA, Kang M, Cho BH, Oh SY, Kee C, Han J, Lee GI, Kang MC, Lee D, Choi Y, Yun HJ, Park H, Kim J, Cho SJ, Chang DK. Deep learning-based optic disc classification is affected by optic-disc tilt. Sci Rep 2024; 14:498. [PMID: 38177229 PMCID: PMC10767025 DOI: 10.1038/s41598-023-50256-4] [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: 01/02/2023] [Accepted: 12/18/2023] [Indexed: 01/06/2024] Open
Abstract
We aimed to determine the effect of optic disc tilt on deep learning-based optic disc classification. A total of 2507 fundus photographs were acquired from 2236 eyes of 1809 subjects (mean age of 46 years; 53% men). Among all photographs, 1010 (40.3%) had tilted optic discs. Image annotation was performed to label pathologic changes of the optic disc (normal, glaucomatous optic disc changes, disc swelling, and disc pallor). Deep learning-based classification modeling was implemented to develop optic-disc appearance classification models with the photographs of all subjects and those with and without tilted optic discs. Regardless of deep learning algorithms, the classification models showed better overall performance when developed based on data from subjects with non-tilted discs (AUC, 0.988 ± 0.002, 0.991 ± 0.003, and 0.986 ± 0.003 for VGG16, VGG19, and DenseNet121, respectively) than when developed based on data with tilted discs (AUC, 0.924 ± 0.046, 0.928 ± 0.017, and 0.935 ± 0.008). In classification of each pathologic change, non-tilted disc models had better sensitivity and specificity than the tilted disc models. The optic disc appearance classification models developed based all-subject data demonstrated lower accuracy in patients with the appearance of tilted discs than in those with non-tilted discs. Our findings suggested the need to identify and adjust for the effect of optic disc tilt on the optic disc classification algorithm in future development.
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Affiliation(s)
- Youngwoo Nam
- Medical AI Research Center, Institute of Smart Healthcare, Samsung Medical Center, Seoul, Republic of Korea
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Joonhyoung Kim
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyunga Kim
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Biomedical Statistics Center, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Republic of Korea
- Department of Data Convergence & Future Medicine, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kyung-Ah Park
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
| | - Mira Kang
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea.
- Health Promotion Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea.
- Digital Innovation Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
| | - Baek Hwan Cho
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Department of Biomedical Informatics, CHA University School of Medicine, CHA University, Seongam, Republic of Korea
| | - Sei Yeul Oh
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Changwon Kee
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Jongchul Han
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
- Department of Medical Device Management and Research, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
| | - Ga-In Lee
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Min Chae Kang
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Dongyoung Lee
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Yeeun Choi
- Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Hee Jee Yun
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Hansol Park
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Jiho Kim
- Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Soo Jin Cho
- Health Promotion Center, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea
| | - Dong Kyung Chang
- Department of Digital Health, SAIHST, Sungkyunkwan University, Seoul, Republic of Korea
- Division of Gastroenterology, Department of Internal Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
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26
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Wang JD, Liu MR, Liu ML, Zhang R, Chen CX, Cao K. An auxiliary diagnostic tool for common fundus diseases based on fundus color photography and light-weight classification models. Graefes Arch Clin Exp Ophthalmol 2024; 262:223-229. [PMID: 37540261 DOI: 10.1007/s00417-023-06182-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 07/11/2023] [Accepted: 07/17/2023] [Indexed: 08/05/2023] Open
Abstract
OBJECTIVE To evaluate the performance of two lightweight neural network models in the diagnosis of common fundus diseases and make comparison to another two classical models. METHODS A total of 16,000 color fundus photography were collected, including 2000 each of glaucoma, diabetic retinopathy (DR), high myopia, central retinal vein occlusion (CRVO), age-related macular degeneration (AMD), optic neuropathy, and central serous chorioretinopathy (CSC), in addition to 2000 normal fundus. Fundus photography was obtained from patients or physical examiners who visited the Ophthalmology Department of Beijing Tongren Hospital, Capital Medical University. Each fundus photography has been diagnosed and labeled by two professional ophthalmologists. Two classical classification models (ResNet152 and DenseNet121), and two lightweight classification models (MobileNetV3 and ShufflenetV2), were trained. Area under the curve (AUC), sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were used to evaluate the performance of the four models. RESULTS Compared with the classical classification model, the total size and number of parameters of the two lightweight classification models were significantly reduced, and the classification speed was sharply improved. Compared with the DenseNet121 model, the ShufflenetV2 model took 50.7% less time to make a diagnosis on a fundus photography. The classical models performed better than lightweight classification models, and Densenet121 showed highest AUC in five out of the seven common fundus diseases. However, the performance of lightweight classification models is satisfying. The AUCs using MobileNetV3 model to diagnose AMD, diabetic retinopathy, glaucoma, CRVO, high myopia, optic atrophy, and CSC were 0.805, 0.892, 0.866, 0.812, 0.887, 0.868, and 0.803, respectively. For ShufflenetV2model, the AUCs for the above seven diseases were 0.856, 0.893, 0.855, 0.884, 0.891, 0.867, and 0.844, respectively. CONCLUSION The training of light-weight neural network models based on color fundus photography for the diagnosis of common fundus diseases is not only fast but also has a significant reduction in storage size and parameter number compared with the classical classification model, and can achieve satisfactory accuracy.
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Affiliation(s)
- Jin-Da Wang
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China
| | - Mei-Rui Liu
- School of Public Health, North China University of Science and Technology, Weifang, China
| | - Mei-Ling Liu
- Dahongmen Community Health Service Center, Beijing, China
| | - Ran Zhang
- Majiapu Community Health Service Center, Beijing, China
| | - Chang-Xi Chen
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, No.17. Hougou Alley, Dongcheng District, Beijing, China.
| | - Kai Cao
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, No.17. Hougou Alley, Dongcheng District, Beijing, China.
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27
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Chen Q, Zhou M, Cao Y, Zheng X, Mao H, Lei C, Lin W, Jiang J, Chen Y, Song D, Xu X, Ye C, Liang Y. Quality assessment of non-mydriatic fundus photographs for glaucoma screening in primary healthcare centres: a real-world study. BMJ Open Ophthalmol 2023; 8:e001493. [PMID: 38092419 PMCID: PMC10729214 DOI: 10.1136/bmjophth-2023-001493] [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: 09/13/2023] [Accepted: 11/22/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND This study assessed the quality distribution of non-mydriatic fundus photographs (NMFPs) in real-world glaucoma screening and analysed its influencing factors. METHODS This cross-sectional study was conducted in primary healthcare centres in the Yinzhou District, China, from 17 March to 3 December 2021. The quality distribution of bilateral NMFPs was assessed by the Digital Reading Department of the Eye Hospital of Wenzhou Medical University. Generalised estimating equations and logistic regression models identified factors affecting image quality. RESULTS A total of 17 232 photographs of 8616 subjects were assessed. Of these, 11.9% of images were reliable for the right eyes, while only 4.6% were reliable for the left eyes; 93.6% of images were readable in the right eyes, while 90.3% were readable in the left eyes. In adjusted models, older age was associated with decreased odds of image readability (adjusted OR (aOR)=1.07, 95% CI 1.06~1.08, p<0.001). A larger absolute value of spherical equivalent significantly decreased the odds of image readability (all p<0.001). Media opacity and worse visual acuity had a significantly lower likelihood of achieving readable NMFPs (aOR=1.52, 95% CI 1.31~1.75; aOR=1.70, 95% CI 1.42~2.02, respectively, all p<0.001). Astigmatism axes within 31°~60° and 121°~150° had lower odds of image readability (aOR=1.35, 95% CI 1.11~1.63, p<0.01) than astigmatism axes within 180°±30°. CONCLUSIONS The image readability of NMFPs in large-scale glaucoma screening for individuals 50 years and older is comparable with relevant studies, but image reliability is unsatisfactory. Addressing the associated factors may be vital when implementing ophthalmological telemedicine in underserviced areas. TRIAL REGISTRATION NUMBER ChiCTR2200059277.
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Affiliation(s)
- Qi Chen
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Department of Ophthalmology, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China
| | - Mengtian Zhou
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yang Cao
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Xuanli Zheng
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Huiyan Mao
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Changrong Lei
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Wanglong Lin
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Junhong Jiang
- Department of Ophthalmology, Shanghai General Hospital, National Clinical Research Center for Eye Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Yize Chen
- Department of Ophthalmology, First Hospital of Shanxi Medical University, Taiyuan, China
| | - Di Song
- Department of Ophthalmology, The First People's Hospital of Huzhou, The First Affiliated Hospital of Huzhou Teacher College, Huzhou, China
| | - Xiang Xu
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Cong Ye
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
| | - Yuanbo Liang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou, Zhejiang, China
- Glaucoma Research Institute, Wenzhou Medical University, Wenzhou, China
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Qian X, Xian S, Yifei S, Wei G, Liu H, Xiaoming X, Chu C, Yilong Y, Shuang Y, Kai M, Mei C, Yi Q. External validation of a deep learning detection system for glaucomatous optic neuropathy: a real-world multicentre study. Eye (Lond) 2023; 37:3813-3818. [PMID: 37322379 PMCID: PMC10698045 DOI: 10.1038/s41433-023-02622-9] [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: 11/23/2022] [Revised: 05/17/2023] [Accepted: 06/02/2023] [Indexed: 06/17/2023] Open
Abstract
OBJECTIVES To conduct an external validation of an automated artificial intelligence (AI) diagnostic system using fundus photographs from a real-life multicentre cohort. METHODS We designed external validation in multiple scenarios, consisting of 3049 images from Qilu Hospital of Shandong University in China (QHSDU, validation dataset 1), 7495 images from three other hospitals in China (validation dataset 2), and 516 images from high myopia (HM) population of QHSDU (validation dataset 3). The corresponding sensitivity, specificity and accuracy of this AI diagnostic system to identify glaucomatous optic neuropathy (GON) were calculated. RESULTS In validation datasets 1 and 2, the algorithm yielded accuracy of 93.18% and 91.40%, area under the receiver operating curves (AUC) of 95.17% and 96.64%, and significantly higher sensitivity of 91.75% and 91.41%, respectively, compared to manual graders. On the subsets complicated with retinal comorbidities, such as diabetic retinopathy or age-related macular degeneration, in validation datasets 1 and 2, the algorithm achieved accuracy of 87.54% and 93.81%, and AUC of 97.02% and 97.46%, respectively. In validation dataset 3, the algorithm achieved comparable accuracy of 81.98% and AUC of 87.49%, with a sensitivity of 83.61% and specificity of 81.76% on GON recognition specifically in the HM population. CONCLUSIONS With acceptable generalization capability across varying levels of image quality, different clinical centres, or certain retinal comorbidities, such as HM, the automatic AI diagnostic system had the potential to provide expert-level glaucoma detection.
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Affiliation(s)
- Xu Qian
- Department of Geriatric Medicine, Qilu Hospital of Shandong University, No. 107, Wenhuaxi Road, Jinan, 250012, China
- Key Laboratory of Cardiovascular Proteomics of Shandong Province, Jinan, China
- Jinan Clinical Research Center for Geriatric Medicine (202132001), Jinan, China
| | - Song Xian
- Department of Geriatric Medicine, Qilu Hospital of Shandong University, No. 107, Wenhuaxi Road, Jinan, 250012, China
- Key Laboratory of Cardiovascular Proteomics of Shandong Province, Jinan, China
- Jinan Clinical Research Center for Geriatric Medicine (202132001), Jinan, China
| | - Su Yifei
- Global Health Research Center, Duke Kunshan University, No. 8 Duke Avenue, Kunshan, Jiangsu Province, 215316, China
| | - Guo Wei
- Lunan Eye Hospital, Linyi, 276000, China
| | - Hanruo Liu
- Beijing Tongren Hospital, Capital Medical University, Beijing, 100730, China
- School of Information and Electronics, Beijing Institute of Technology, Beijing, 100730, China
| | - Xi Xiaoming
- School of Computer Science and Technology, Shandong Jianzhu University, Jinan, 250101, China
| | | | - Yin Yilong
- School of Software, Shandong University, Jinan, 250101, China
| | - Yu Shuang
- Tencent Healthcare, Shenzhen, 51800, China
| | - Ma Kai
- Tencent Healthcare, Shenzhen, 51800, China
| | - Cheng Mei
- Department of Geriatric Medicine, Qilu Hospital of Shandong University, No. 107, Wenhuaxi Road, Jinan, 250012, China
- Key Laboratory of Cardiovascular Proteomics of Shandong Province, Jinan, China
- Jinan Clinical Research Center for Geriatric Medicine (202132001), Jinan, China
| | - Qu Yi
- Department of Geriatric Medicine, Qilu Hospital of Shandong University, No. 107, Wenhuaxi Road, Jinan, 250012, China.
- Key Laboratory of Cardiovascular Proteomics of Shandong Province, Jinan, China.
- Jinan Clinical Research Center for Geriatric Medicine (202132001), Jinan, China.
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Ji Y, Ni X, Zheng K, Jiang Y, Ren C, Zhu H, Xiao M, Wang T. Combined effects of transcranial direct current stimulation and aerobic exercise on inhibitory control function in healthy young adults: An event-related potential study. Brain Cogn 2023; 173:106090. [PMID: 37816273 DOI: 10.1016/j.bandc.2023.106090] [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: 04/29/2023] [Revised: 09/12/2023] [Accepted: 10/02/2023] [Indexed: 10/12/2023]
Abstract
Transcranial direct current stimulation (tDCS) and aerobic exercise (AE) have been demonstrated to enhance inhibitory control function in healthy individuals separately. However, the potential benefits of combining these two interventions have yet to be fully explored. In this study, we aimed to use multiple event-related potential (ERP) components (P200, N200, and N450) to investigate the combined effects of tDCS and AE on the improvement of inhibitory control ability in healthy young adults. We evaluated the influence of this combined intervention on cognitive tasks involving inhibitory control function and basic information processing by performing the Stroop Word Color task. Our results showed that compared to the application of tDCS or AE alone, the combined intervention of tDCS and AE had a greater effect on improving inhibitory control function in healthy young adults. The amplitude of P200, N200, and N450 ERP components also changed more significantly during the Stroop Word Color task. We concluded that the mechanism of tDCS combined with AE in improving inhibitory control ability may involve synergistic effects on brain structures at different levels, such as regulating interactions at the reticular activating system level and activating corresponding brain regions at the medial frontal lobe and frontal lobe levels.
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Affiliation(s)
- Yingying Ji
- The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Xuemei Ni
- The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Kai Zheng
- The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Ying Jiang
- The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Caili Ren
- The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China
| | - Haohao Zhu
- The Affiliated Mental Health Center of Jiangnan University, Wuxi Central Rehabilitation Hospital, Wuxi, Jiangsu, China.
| | - Ming Xiao
- Jiangsu Province Key Laboratory of Neurodegeneration, Center for Global Health, Nanjing Medical University, Nanjing, Jiangsu, China.
| | - Tong Wang
- Rehabilitation Medicine Center, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu, China.
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Saha S, Vignarajan J, Frost S. A fast and fully automated system for glaucoma detection using color fundus photographs. Sci Rep 2023; 13:18408. [PMID: 37891238 PMCID: PMC10611813 DOI: 10.1038/s41598-023-44473-0] [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: 03/06/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023] Open
Abstract
This paper presents a low computationally intensive and memory efficient convolutional neural network (CNN)-based fully automated system for detection of glaucoma, a leading cause of irreversible blindness worldwide. Using color fundus photographs, the system detects glaucoma in two steps. In the first step, the optic disc region is determined relying upon You Only Look Once (YOLO) CNN architecture. In the second step classification of 'glaucomatous' and 'non-glaucomatous' is performed using MobileNet architecture. A simplified version of the original YOLO net, specific to the context, is also proposed. Extensive experiments are conducted using seven state-of-the-art CNNs with varying computational intensity, namely, MobileNetV2, MobileNetV3, Custom ResNet, InceptionV3, ResNet50, 18-Layer CNN and InceptionResNetV2. A total of 6671 fundus images collected from seven publicly available glaucoma datasets are used for the experiment. The system achieves an accuracy and F1 score of 97.4% and 97.3%, with sensitivity, specificity, and AUC of respectively 97.5%, 97.2%, 99.3%. These findings are comparable with the best reported methods in the literature. With comparable or better performance, the proposed system produces significantly faster decisions and drastically minimizes the resource requirement. For example, the proposed system requires 12 times less memory in comparison to ResNes50, and produces 2 times faster decisions. With significantly less memory efficient and faster processing, the proposed system has the capability to be directly embedded into resource limited devices such as portable fundus cameras.
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Affiliation(s)
- Sajib Saha
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Perth, Australia.
| | - Janardhan Vignarajan
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Perth, Australia
| | - Shaun Frost
- Australian e-Health Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Perth, Australia
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Davuluru SS, Jess AT, Kim JSB, Yoo K, Nguyen V, Xu BY. Identifying, Understanding, and Addressing Disparities in Glaucoma Care in the United States. Transl Vis Sci Technol 2023; 12:18. [PMID: 37889504 PMCID: PMC10617640 DOI: 10.1167/tvst.12.10.18] [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: 07/16/2023] [Accepted: 10/09/2023] [Indexed: 10/28/2023] Open
Abstract
Glaucoma is the leading cause of irreversible blindness worldwide, currently affecting around 80 million people. Glaucoma prevalence is rapidly rising in the United States due to an aging population. Despite recent advances in the diagnosis and treatment of glaucoma, significant disparities persist in disease detection, management, and outcomes among the diverse patient populations of the United States. Research on disparities is critical to identifying, understanding, and addressing societal and healthcare inequalities. Disparities research is especially important and impactful in the context of irreversible diseases such as glaucoma, where earlier detection and intervention are the primary approach to improving patient outcomes. In this article, we first review recent studies identifying disparities in glaucoma care that affect patient populations based on race, age, and gender. We then review studies elucidating and furthering our understanding of modifiable factors that contribute to these inequities, including socioeconomic status (particularly age and education), insurance product, and geographic region. Finally, we present work proposing potential strategies addressing disparities in glaucoma care, including teleophthalmology and artificial intelligence. We also discuss the presence of non-modifiable factors that contribute to differences in glaucoma burden and can confound the detection of glaucoma disparities. Translational Relevance By recognizing underlying causes and proposing potential solutions, healthcare providers, policymakers, and other stakeholders can work collaboratively to reduce the burden of glaucoma and improve visual health and clinical outcomes in vulnerable patient populations.
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Affiliation(s)
- Shaili S. Davuluru
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Alison T. Jess
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Kristy Yoo
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Van Nguyen
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Benjamin Y. Xu
- Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
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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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He S, Bulloch G, Zhang L, Xie Y, Wu W, He Y, Meng W, Shi D, He M. Cross-camera Performance of Deep Learning Algorithms to Diagnose Common Ophthalmic Diseases: A Comparative Study Highlighting Feasibility to Portable Fundus Camera Use. Curr Eye Res 2023; 48:857-863. [PMID: 37246918 DOI: 10.1080/02713683.2023.2215984] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Revised: 04/19/2023] [Accepted: 05/14/2023] [Indexed: 05/30/2023]
Abstract
PURPOSE To compare the inter-camera performance and consistency of various deep learning (DL) diagnostic algorithms applied to fundus images taken from desktop Topcon and portable Optain cameras. METHODS Participants over 18 years of age were enrolled between November 2021 and April 2022. Pair-wise fundus photographs from each patient were collected in a single visit; once by Topcon (used as the reference camera) and once by a portable Optain camera (the new target camera). These were analyzed by three previously validated DL models for the detection of diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucomatous optic neuropathy (GON). Ophthalmologists manually analyzed all fundus photos for the presence of DR and these were referred to as the ground truth. Sensitivity, specificity, the area under the curve (AUC) and agreement between cameras (estimated by Cohen's weighted kappa, K) were the primary outcomes of this study. RESULTS A total of 504 patients were recruited. After excluding 12 photographs with matching errors and 59 photographs with low quality, 906 pairs of Topcon-Optain fundus photos were available for algorithm assessment. Topcon and Optain cameras had excellent consistency (Κ=0.80) when applied to the referable DR algorithm, while AMD had moderate consistency (Κ=0.41) and GON had poor consistency (Κ=0.32). For the DR model, Topcon and Optain achieved a sensitivity of 97.70% and 97.67% and a specificity of 97.92% and 97.93%, respectively. There was no significant difference between the two camera models (McNemar's test: x2=0.08, p = .78). CONCLUSION Topcon and Optain cameras had excellent consistency for detecting referable DR, albeit performances for detecting AMD and GON models were unsatisfactory. This study highlights the methods of using pair-wise images to evaluate DL models between reference and new fundus cameras.
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Affiliation(s)
- Shuang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Gabriella Bulloch
- University of Melbourne, Melbourne, Victoria, Australia
- Centre for Eye Research Australia, Melbourne, Victoria, Australia
| | - Liangxin Zhang
- Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou, China
| | - Yiyu Xie
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
| | - Weiyu Wu
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Yahong He
- Department of Ophthalmology, Guangdong Academy of Medical Sciences, Guangdong Provincial People's Hospital, Guangzhou, China
| | - Wei Meng
- Eyetelligence Ltd, Melbourne, Victoria, Australia
| | - Danli Shi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, China
- Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
- University of Melbourne, Melbourne, Victoria, Australia
- Centre for Eye Research Australia, Melbourne, Victoria, Australia
- Eyetelligence Ltd, Melbourne, Victoria, Australia
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Mendez-Hernandez C, Gutierrez-Diaz E, Pazos M, Gimenez-Gomez R, Pinazo-Duran MD. Agreement between Five Experts and the Laguna ONhE Automatic Colourimetric Application Interpreting the Glaucomatous Aspect of the Optic Nerve. J Clin Med 2023; 12:5485. [PMID: 37685554 PMCID: PMC10488544 DOI: 10.3390/jcm12175485] [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: 07/24/2023] [Revised: 08/19/2023] [Accepted: 08/21/2023] [Indexed: 09/10/2023] Open
Abstract
BACKGROUND Optic nerve head (ONH) interpretation is a glaucoma screening method which may be influenced by criteria variability. Laguna ONhE software is a low-cost and non-invasive method of ONH analysis. METHODS We analysed the results of the Laguna ONhE application, interpreting 552 ONH images from the ACRIMA database, publicly available on the Internet, and compared them with the opinion of five experts. Diagnostic agreement was investigated using Cohen's kappa (κ) with 95% confidence. RESULTS The kappa concordance index obtained with Laguna ONhE and the majority of the experts' criterion (0.77) was significantly higher compared to that obtained with ACRIMA and the majority of the experts' criterion (0.61). In 44.7% of the cases there was absolute agreement among the 5 experts and the Laguna ONhE program. Removing borderline cases from the analysis yielded increased diagnostic agreement (0.81). The area under the receiver operating characteristic (AUROC) of the Laguna ONhE program (0.953, p < 0.001) was not significantly different than AUROC of the majority of the experts' criterion (0.925, p < 0.001), p = 0.052. Individually obtained expert's AUROCs were significantly lower (0.636 to 0.913; p < 0.01). CONCLUSIONS Laguna ONhE's agreement with the experts is high, particularly where the diagnosis may be more obvious by the appearance of the ONH.
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Affiliation(s)
- Carmen Mendez-Hernandez
- Ophthalmology Department, Hospital Clinico San Carlos, Institute of Health Research (IdISSC), 28232 Madrid, Spain
- Department of Immunology, Ophthalmology and ORL IIORC, Complutense University of Madrid, 28232 Madrid, Spain
| | | | - Marta Pazos
- Institut of Ophthalmology, Hospital Clinic Barcelona, Universitat de Barcelona, 08036 Barcelona, Spain;
| | - Rafael Gimenez-Gomez
- Ophthalmology Department, Hospital Universitario Reina Sofia, Instituto Maimonides de Investigación Médica, 14014 Cordoba, Spain;
| | - Maria Dolores Pinazo-Duran
- Ophthalmic Research Unit “Santiago Grisolia”, FISABIO and Department of Surgery, Medical School, University of Valencia, 46010 Valencia, Spain;
- Spanish Network of Inflammatory Diseases REI-RICORS RD21/0002/0032, Institute of Health Carlos III, 28029 Madrid, Spain
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Wen D, Yu Z, Yang Z, Zheng C, Ren X, Shao Y, Li X. Deep learning-based postoperative visual acuity prediction in idiopathic epiretinal membrane. BMC Ophthalmol 2023; 23:361. [PMID: 37599349 PMCID: PMC10440890 DOI: 10.1186/s12886-023-03079-w] [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: 01/30/2023] [Accepted: 07/12/2023] [Indexed: 08/22/2023] Open
Abstract
BACKGROUND To develop a deep learning (DL) model based on preoperative optical coherence tomography (OCT) training to automatically predict the 6-month postoperative visual outcomes in patients with idiopathic epiretinal membrane (iERM). METHODS In this retrospective cohort study, a total of 442 eyes (5304 images in total) were enrolled for the development of the DL and multimodal deep fusion network (MDFN) models. All eyes were randomized into a training dataset with 265 eyes (60.0%), a validation dataset with 89 eyes (20.1%), and an internal testing dataset with the remaining 88 eyes (19.9%). The input variables for prediction consisted of macular OCT images and diverse clinical data. Inception-Resnet-v2 network was utilized to estimate the 6-month postoperative best-corrected visual acuity (BCVA). Concurrently, a regression model was developed using the clinical data and OCT parameters in the training data set for predicting postoperative BCVA. The reliability of the models was subsequently evaluated using the testing dataset. RESULTS The prediction DL algorithm exhibited a mean absolute error (MAE) of 0.070 logMAR and root mean square error (RMSE) of 0.11 logMAR in the testing dataset. The DL model demonstrated a robust promising performance with R2 = 0.80, notably superior to R2 = 0.49 of the regression model. The percentages of BCVA prediction errors within ± 0.20 logMAR amounted to 94.32% in the testing dataset. CONCLUSIONS The OCT-based DL model demonstrated sensitivity and accuracy in predicting postoperative BCVA in iERM patients. This innovative DL model exhibits substantial potential for integration into surgical planning protocols.
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Affiliation(s)
- Dejia Wen
- 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, 251 Fukang Road, Tianjin, 300384, China
| | - Zihao Yu
- 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, 251 Fukang Road, Tianjin, 300384, China
| | - Zhengwei Yang
- 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, 251 Fukang Road, Tianjin, 300384, China
| | - Chuanzhen Zheng
- 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, 251 Fukang Road, Tianjin, 300384, China
| | - Xinjun Ren
- 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, 251 Fukang Road, Tianjin, 300384, China
| | - Yan Shao
- 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, 251 Fukang Road, Tianjin, 300384, 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, 251 Fukang Road, Tianjin, 300384, China.
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Yao Z, Luo R, Xing C, Li F, Zhu G, Wang Z, Zhang G. 3D-FVS: construction and application of three-dimensional fundus vascular structure model based on single image features. Eye (Lond) 2023; 37:2505-2510. [PMID: 36522528 PMCID: PMC10397231 DOI: 10.1038/s41433-022-02364-0] [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: 03/02/2022] [Revised: 10/31/2022] [Accepted: 12/06/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Fundus microvasculature may be visually observed by ophthalmoscope and has been widely used in clinical practice. Due to the limitations of available equipment and technology, most studies only utilized the two-dimensional planar features of the fundus microvasculature. METHODS This study proposed a novel method for establishing the three-dimensional fundus vascular structure model and generating hemodynamic characteristics based on a single image. Firstly, the fundus vascular are segmented through our proposed network framework. Then, the length and width of vascular segments and the relationship among the adjacent segments are collected to construct the three-dimensional vascular structure model. Finally, the hemodynamic model is generated based on the vascular structure model, and highly correlated hemodynamic features are selected to diagnose the ophthalmic diseases. RESULTS In fundus vascular segmentation, the proposed network framework obtained 98.63% and 97.52% on Area Under Curve (AUC) and accuracy respectively. In diagnosis, the high correlation features extracted based on the proposed method achieved 95% on accuracy. CONCLUSIONS This study demonstrated that hemodynamic features filtered by relevance were essential for diagnosing retinal diseases. Additionally, the method proposed also outperformed the existing models on the levels of retina vessel segmentation. In conclusion, the proposed method may represent a novel way to diagnose retinal related diseases, which can analysis two-dimensional fundus pictures by extracting heterogeneous three-dimensional features.
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Affiliation(s)
- Zhaomin Yao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China
| | - Renli Luo
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China
| | - Chencong Xing
- School of Computer Science and Software Engineering, East China Normal University, Shanghai, 200241, China
| | - Fei Li
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Gancheng Zhu
- College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin, 130012, China
| | - Zhiguo Wang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China.
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China.
| | - Guoxu Zhang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, Liaoning, 110167, China.
- Department of Nuclear Medicine, General Hospital of Northern Theater Command, Shenyang, Liaoning, 110016, China.
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Rajesh AE, Olvera-Barrios A, Warwick AN, Wu Y, Stuart KV, Biradar M, Ung CY, Khawaja AP, Luben R, Foster PJ, Lee CS, Tufail A, Lee AY, Egan C. Ethnicity is not biology: retinal pigment score to evaluate biological variability from ophthalmic imaging using machine learning. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.06.28.23291873. [PMID: 37461664 PMCID: PMC10350142 DOI: 10.1101/2023.06.28.23291873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 07/24/2023]
Abstract
Background Few metrics exist to describe phenotypic diversity within ophthalmic imaging datasets, with researchers often using ethnicity as an inappropriate marker for biological variability. Methods We derived a continuous, measured metric, the retinal pigment score (RPS), that quantifies the degree of pigmentation from a colour fundus photograph of the eye. RPS was validated using two large epidemiological studies with demographic and genetic data (UK Biobank and EPIC-Norfolk Study). Findings A genome-wide association study (GWAS) of RPS from UK Biobank identified 20 loci with known associations with skin, iris and hair pigmentation, of which 8 were replicated in the EPIC-Norfolk cohort. There was a strong association between RPS and ethnicity, however, there was substantial overlap between each ethnicity and the respective distributions of RPS scores. Interpretation RPS serves to decouple traditional demographic variables, such as ethnicity, from clinical imaging characteristics. RPS may serve as a useful metric to quantify the diversity of the training, validation, and testing datasets used in the development of AI algorithms to ensure adequate inclusion and explainability of the model performance, critical in evaluating all currently deployed AI models. The code to derive RPS is publicly available at: https://github.com/uw-biomedical-ml/retinal-pigmentation-score. Funding The authors did not receive support from any organisation for the submitted work.
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Affiliation(s)
- Anand E Rajesh
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- The Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Abraham Olvera-Barrios
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology, London, UK
| | - Alasdair N Warwick
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology, London, UK
| | - Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- The Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Kelsey V Stuart
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology, London, UK
| | - Mahantesh Biradar
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology, London, UK
- University of Cambridge, Cambridge, UK
| | | | - Anthony P Khawaja
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology, London, UK
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Robert Luben
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology, London, UK
| | - Paul J Foster
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology, London, UK
| | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- The Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Adnan Tufail
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology, London, UK
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- The Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Catherine Egan
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust & University College London Institute of Ophthalmology, London, UK
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Medeiros FA, Lee T, Jammal AA, Al-Aswad LA, Eydelman MB, Schuman JS. The Definition of Glaucomatous Optic Neuropathy in Artificial Intelligence Research and Clinical Applications. Ophthalmol Glaucoma 2023; 6:432-438. [PMID: 36731747 PMCID: PMC10387499 DOI: 10.1016/j.ogla.2023.01.007] [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: 12/13/2022] [Revised: 01/19/2023] [Accepted: 01/23/2023] [Indexed: 06/11/2023]
Abstract
OBJECTIVE Although artificial intelligence (AI) models may offer innovative and powerful ways to use the wealth of data generated by diagnostic tools, there are important challenges related to their development and validation. Most notable is the lack of a perfect reference standard for glaucomatous optic neuropathy (GON). Because AI models are trained to predict presence of glaucoma or its progression, they generally rely on a reference standard that is used to train the model and assess its validity. If an improper reference standard is used, the model may be trained to detect or predict something that has little or no clinical value. This article summarizes the issues and discussions related to the definition of GON in AI applications as presented by the Glaucoma Workgroup from the Collaborative Community for Ophthalmic Imaging (CCOI) US Food and Drug Administration Virtual Workshop, on September 3 and 4, 2020, and on January 28, 2022. DESIGN Review and conference proceedings. SUBJECTS No human or animal subjects or data therefrom were used in the production of this article. METHODS A summary of the Workshop was produced with input and approval from all participants. MAIN OUTCOME MEASURES Consensus position of the CCOI Workgroup on the challenges in defining GON and possible solutions. RESULTS The Workshop reviewed existing challenges that arise from the use of subjective definitions of GON and highlighted the need for a more objective approach to characterize GON that could facilitate replication and comparability of AI studies and allow for better clinical validation of proposed AI tools. Different tests and combination of parameters for defining a reference standard for GON have been proposed. Different reference standards may need to be considered depending on the scenario in which the AI models are going to be applied, such as community-based or opportunistic screening versus detection or monitoring of glaucoma in tertiary care. CONCLUSIONS The development and validation of new AI-based diagnostic tests should be based on rigorous methodology with clear determination of how the reference standards for glaucomatous damage are constructed and the settings where the tests are going to be applied. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Felipe A Medeiros
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina; Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina.
| | - Terry Lee
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina
| | - Alessandro A Jammal
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina
| | - Lama A Al-Aswad
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York; Department of Population Health, NYU Langone Health, NYU Grossman School of Medicine, New York, New York
| | | | - Joel S Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York; Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York; Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, New York; Center for Neural Science, NYU, New York, New York; Neuroscience Institute, NYU Langone Health, New York, New York
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Gu B, Sidhu S, Weinreb RN, Christopher M, Zangwill LM, Baxter SL. Review of Visualization Approaches in Deep Learning Models of Glaucoma. Asia Pac J Ophthalmol (Phila) 2023; 12:392-401. [PMID: 37523431 DOI: 10.1097/apo.0000000000000619] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2023] [Accepted: 05/11/2023] [Indexed: 08/02/2023] Open
Abstract
Glaucoma is a major cause of irreversible blindness worldwide. As glaucoma often presents without symptoms, early detection and intervention are important in delaying progression. Deep learning (DL) has emerged as a rapidly advancing tool to help achieve these objectives. In this narrative review, data types and visualization approaches for presenting model predictions, including models based on tabular data, functional data, and/or structural data, are summarized, and the importance of data source diversity for improving the utility and generalizability of DL models is explored. Examples of innovative approaches to understanding predictions of artificial intelligence (AI) models and alignment with clinicians are provided. In addition, methods to enhance the interpretability of clinical features from tabular data used to train AI models are investigated. Examples of published DL models that include interfaces to facilitate end-user engagement and minimize cognitive and time burdens are highlighted. The stages of integrating AI models into existing clinical workflows are reviewed, and challenges are discussed. Reviewing these approaches may help inform the generation of user-friendly interfaces that are successfully integrated into clinical information systems. This review details key principles regarding visualization approaches in DL models of glaucoma. The articles reviewed here focused on usability, explainability, and promotion of clinician trust to encourage wider adoption for clinical use. These studies demonstrate important progress in addressing visualization and explainability issues required for successful real-world implementation of DL models in glaucoma.
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Affiliation(s)
- Byoungyoung Gu
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, US
| | - Sophia Sidhu
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, US
| | - Robert N Weinreb
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
| | - Mark Christopher
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
| | - Linda M Zangwill
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
| | - Sally L Baxter
- Division of Ophthalmology Informatics and Data Science and Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, US
- Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA, US
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Zedan MJM, Zulkifley MA, Ibrahim AA, Moubark AM, Kamari NAM, Abdani SR. Automated Glaucoma Screening and Diagnosis Based on Retinal Fundus Images Using Deep Learning Approaches: A Comprehensive Review. Diagnostics (Basel) 2023; 13:2180. [PMID: 37443574 DOI: 10.3390/diagnostics13132180] [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: 05/20/2023] [Revised: 06/16/2023] [Accepted: 06/17/2023] [Indexed: 07/15/2023] Open
Abstract
Glaucoma is a chronic eye disease that may lead to permanent vision loss if it is not diagnosed and treated at an early stage. The disease originates from an irregular behavior in the drainage flow of the eye that eventually leads to an increase in intraocular pressure, which in the severe stage of the disease deteriorates the optic nerve head and leads to vision loss. Medical follow-ups to observe the retinal area are needed periodically by ophthalmologists, who require an extensive degree of skill and experience to interpret the results appropriately. To improve on this issue, algorithms based on deep learning techniques have been designed to screen and diagnose glaucoma based on retinal fundus image input and to analyze images of the optic nerve and retinal structures. Therefore, the objective of this paper is to provide a systematic analysis of 52 state-of-the-art relevant studies on the screening and diagnosis of glaucoma, which include a particular dataset used in the development of the algorithms, performance metrics, and modalities employed in each article. Furthermore, this review analyzes and evaluates the used methods and compares their strengths and weaknesses in an organized manner. It also explored a wide range of diagnostic procedures, such as image pre-processing, localization, classification, and segmentation. In conclusion, automated glaucoma diagnosis has shown considerable promise when deep learning algorithms are applied. Such algorithms could increase the accuracy and efficiency of glaucoma diagnosis in a better and faster manner.
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Affiliation(s)
- Mohammad J M Zedan
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
- Computer and Information Engineering Department, College of Electronics Engineering, Ninevah University, Mosul 41002, Iraq
| | - Mohd Asyraf Zulkifley
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Ahmad Asrul Ibrahim
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Asraf Mohamed Moubark
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Nor Azwan Mohamed Kamari
- Department of Electrical, Electronic and Systems Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia
| | - Siti Raihanah Abdani
- School of Computing Sciences, College of Computing, Informatics and Media, Universiti Teknologi MARA, Shah Alam 40450, Selangor, Malaysia
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Hemelings R, Elen B, Schuster AK, Blaschko MB, Barbosa-Breda J, Hujanen P, Junglas A, Nickels S, White A, Pfeiffer N, Mitchell P, De Boever P, Tuulonen A, Stalmans I. A generalizable deep learning regression model for automated glaucoma screening from fundus images. NPJ Digit Med 2023; 6:112. [PMID: 37311940 DOI: 10.1038/s41746-023-00857-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 06/01/2023] [Indexed: 06/15/2023] Open
Abstract
A plethora of classification models for the detection of glaucoma from fundus images have been proposed in recent years. Often trained with data from a single glaucoma clinic, they report impressive performance on internal test sets, but tend to struggle in generalizing to external sets. This performance drop can be attributed to data shifts in glaucoma prevalence, fundus camera, and the definition of glaucoma ground truth. In this study, we confirm that a previously described regression network for glaucoma referral (G-RISK) obtains excellent results in a variety of challenging settings. Thirteen different data sources of labeled fundus images were utilized. The data sources include two large population cohorts (Australian Blue Mountains Eye Study, BMES and German Gutenberg Health Study, GHS) and 11 publicly available datasets (AIROGS, ORIGA, REFUGE1, LAG, ODIR, REFUGE2, GAMMA, RIM-ONEr3, RIM-ONE DL, ACRIMA, PAPILA). To minimize data shifts in input data, a standardized image processing strategy was developed to obtain 30° disc-centered images from the original data. A total of 149,455 images were included for model testing. Area under the receiver operating characteristic curve (AUC) for BMES and GHS population cohorts were at 0.976 [95% CI: 0.967-0.986] and 0.984 [95% CI: 0.980-0.991] on participant level, respectively. At a fixed specificity of 95%, sensitivities were at 87.3% and 90.3%, respectively, surpassing the minimum criteria of 85% sensitivity recommended by Prevent Blindness America. AUC values on the eleven publicly available data sets ranged from 0.854 to 0.988. These results confirm the excellent generalizability of a glaucoma risk regression model trained with homogeneous data from a single tertiary referral center. Further validation using prospective cohort studies is warranted.
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Affiliation(s)
- Ruben Hemelings
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000, Leuven, Belgium.
- Flemish Institute for Technological Research (VITO), Boeretang 200, 2400, Mol, Belgium.
| | - Bart Elen
- Flemish Institute for Technological Research (VITO), Boeretang 200, 2400, Mol, Belgium
| | - Alexander K Schuster
- Department of Ophthalmology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | | | - João Barbosa-Breda
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
- Cardiovascular R&D Center, Faculty of Medicine of the University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319, Porto, Portugal
- Department of Ophthalmology, Centro Hospitalar e Universitário São João, Alameda Prof. Hernâni Monteiro, 4200-319, Porto, Portugal
| | - Pekko Hujanen
- Tays Eye Centre, Tampere University Hospital, Tampere, Finland
| | - Annika Junglas
- Department of Ophthalmology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Stefan Nickels
- Department of Ophthalmology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Andrew White
- Department of Ophthalmology, The University of Sydney, Sydney, NSW, Australia
| | - Norbert Pfeiffer
- Department of Ophthalmology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Paul Mitchell
- Department of Ophthalmology, The University of Sydney, Sydney, NSW, Australia
| | - Patrick De Boever
- Centre for Environmental Sciences, Hasselt University, Agoralaan building D, 3590, Diepenbeek, Belgium
- University of Antwerp, Department of Biology, 2610, Wilrijk, Belgium
| | - Anja Tuulonen
- Tays Eye Centre, Tampere University Hospital, Tampere, Finland
| | - Ingeborg Stalmans
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
- Ophthalmology Department, UZ Leuven, Herestraat 49, 3000, Leuven, Belgium
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Zhao PY, Bommakanti N, Yu G, Aaberg MT, Patel TP, Paulus YM. Deep learning for automated detection of neovascular leakage on ultra-widefield fluorescein angiography in diabetic retinopathy. Sci Rep 2023; 13:9165. [PMID: 37280345 DOI: 10.1038/s41598-023-36327-6] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2022] [Accepted: 06/01/2023] [Indexed: 06/08/2023] Open
Abstract
Diabetic retinopathy is a leading cause of blindness in working-age adults worldwide. Neovascular leakage on fluorescein angiography indicates progression to the proliferative stage of diabetic retinopathy, which is an important distinction that requires timely ophthalmic intervention with laser or intravitreal injection treatment to reduce the risk of severe, permanent vision loss. In this study, we developed a deep learning algorithm to detect neovascular leakage on ultra-widefield fluorescein angiography images obtained from patients with diabetic retinopathy. The algorithm, an ensemble of three convolutional neural networks, was able to accurately classify neovascular leakage and distinguish this disease marker from other angiographic disease features. With additional real-world validation and testing, our algorithm could facilitate identification of neovascular leakage in the clinical setting, allowing timely intervention to reduce the burden of blinding diabetic eye disease.
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Affiliation(s)
- Peter Y Zhao
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Nikhil Bommakanti
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Gina Yu
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Michael T Aaberg
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Tapan P Patel
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA
| | - Yannis M Paulus
- Department of Ophthalmology and Visual Sciences, W.K. Kellogg Eye Center, University of Michigan, 1000 Wall Street, Ann Arbor, MI, 48105, USA.
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Abramovich O, Pizem H, Van Eijgen J, Oren I, Melamed J, Stalmans I, Blumenthal EZ, Behar JA. FundusQ-Net: A regression quality assessment deep learning algorithm for fundus images quality grading. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 239:107522. [PMID: 37285697 DOI: 10.1016/j.cmpb.2023.107522] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Revised: 03/23/2023] [Accepted: 03/30/2023] [Indexed: 06/09/2023]
Abstract
OBJECTIVE Ophthalmological pathologies such as glaucoma, diabetic retinopathy and age-related macular degeneration are major causes of blindness and vision impairment. There is a need for novel decision support tools that can simplify and speed up the diagnosis of these pathologies. A key step in this process is to automatically estimate the quality of the fundus images to make sure these are interpretable by a human operator or a machine learning model. We present a novel fundus image quality scale and deep learning (DL) model that can estimate fundus image quality relative to this new scale. METHODS A total of 1245 images were graded for quality by two ophthalmologists within the range 1-10, with a resolution of 0.5. A DL regression model was trained for fundus image quality assessment. The architecture used was Inception-V3. The model was developed using a total of 89,947 images from 6 databases, of which 1245 were labeled by the specialists and the remaining 88,702 images were used for pre-training and semi-supervised learning. The final DL model was evaluated on an internal test set (n=209) as well as an external test set (n=194). RESULTS The final DL model, denoted FundusQ-Net, achieved a mean absolute error of 0.61 (0.54-0.68) on the internal test set. When evaluated as a binary classification model on the public DRIMDB database as an external test set the model obtained an accuracy of 99%. SIGNIFICANCE the proposed algorithm provides a new robust tool for automated quality grading of fundus images.
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Affiliation(s)
- Or Abramovich
- The Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel
| | - Hadas Pizem
- Rambam Medical Center: Rambam Health Care Campus, Israel
| | - Jan Van Eijgen
- Research Group of Ophthalmology, Department of Neurosciences, KU Leuven, Oude Markt 13, 3000 Leuven; Department of Ophthalmology, University Hospitals UZ Leuven, Herestraat 49, 3000 Leuven, Belgium
| | - Ilan Oren
- The Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel
| | - Joshua Melamed
- The Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel
| | - Ingeborg Stalmans
- Research Group of Ophthalmology, Department of Neurosciences, KU Leuven, Oude Markt 13, 3000 Leuven; Department of Ophthalmology, University Hospitals UZ Leuven, Herestraat 49, 3000 Leuven, Belgium
| | | | - Joachim A Behar
- The Faculty of Biomedical Engineering, Technion-IIT, Haifa, Israel.
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Dolar-Szczasny J, Barańska A, Rejdak R. Evaluating the Efficacy of Teleophthalmology in Delivering Ophthalmic Care to Underserved Populations: A Literature Review. J Clin Med 2023; 12:jcm12093161. [PMID: 37176602 PMCID: PMC10179149 DOI: 10.3390/jcm12093161] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 04/24/2023] [Accepted: 04/25/2023] [Indexed: 05/15/2023] Open
Abstract
Technological advancement has brought commendable changes in medicine, advancing diagnosis, treatment, and interventions. Telemedicine has been adopted by various subspecialties including ophthalmology. Over the years, teleophthalmology has been implemented in various countries, and continuous progress is being made in this area. In underserved populations, due to socioeconomic factors, there is little or no access to healthcare facilities, and people are at higher risk of eye diseases and vision impairment. Transportation is the major hurdle for these people in obtaining access to eye care in the main hospitals. There is a dire need for accessible eye care for such populations, and teleophthalmology is the ray of hope for providing eye care facilities to underserved people. Numerous studies have reported the advantages of teleophthalmology for rural populations such as being cost-effective, timesaving, reliable, efficient, and satisfactory for patients. Although it is being practiced in urban populations, for rural populations, its benefits amplify. However, there are certain obstacles as well, such as the cost of equipment, lack of steady electricity and internet supply in rural areas, and the attitude of people in certain regions toward acceptance of teleophthalmology. In this review, we have discussed in detail eye health in rural populations, teleophthalmology, and its effectiveness in rural populations of different countries.
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Affiliation(s)
- Joanna Dolar-Szczasny
- Chair and Department of General and Pediatric Ophthalmology, Medical University of Lublin, 20-079 Lublin, Poland
| | - Agnieszka Barańska
- Department of Medical Informatics and Statistics with E-Learning Laboratory, Medical University of Lublin, 20-090 Lublin, Poland
| | - Robert Rejdak
- Chair and Department of General and Pediatric Ophthalmology, Medical University of Lublin, 20-079 Lublin, Poland
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Cao S, Zhang R, Jiang A, Kuerban M, Wumaier A, Wu J, Xie K, Aizezi M, Tuersun A, Liang X, Chen R. Application effect of an artificial intelligence-based fundus screening system: evaluation in a clinical setting and population screening. Biomed Eng Online 2023; 22:38. [PMID: 37095516 PMCID: PMC10127070 DOI: 10.1186/s12938-023-01097-9] [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: 10/26/2022] [Accepted: 03/24/2023] [Indexed: 04/26/2023] Open
Abstract
BACKGROUND To investigate the application effect of artificial intelligence (AI)-based fundus screening system in real-world clinical environment. METHODS A total of 637 color fundus images were included in the analysis of the application of the AI-based fundus screening system in the clinical environment and 20,355 images were analyzed in the population screening. RESULTS The AI-based fundus screening system demonstrated superior diagnostic effectiveness for diabetic retinopathy (DR), retinal vein occlusion (RVO) and pathological myopia (PM) according to gold standard referral. The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of three fundus abnormalities were greater (all > 80%) than those for age-related macular degeneration (ARMD), referable glaucoma and other abnormalities. The percentages of different diagnostic conditions were similar in both the clinical environment and the population screening. CONCLUSIONS In a real-world setting, our AI-based fundus screening system could detect 7 conditions, with better performance for DR, RVO and PM. Testing in the clinical environment and through population screening demonstrated the clinical utility of our AI-based fundus screening system in the early detection of ocular fundus abnormalities and the prevention of blindness.
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Affiliation(s)
- Shujuan Cao
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Rongpei Zhang
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Aixin Jiang
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Mayila Kuerban
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Aizezi Wumaier
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Jianhua Wu
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Kaihua Xie
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Mireayi Aizezi
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Abudurexiti Tuersun
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China
| | - Xuanwei Liang
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China.
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China.
| | - Rongxin Chen
- State Key Laboratory of Ophthalmology, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, 510060, China.
- Ophthalmologic Center, The Affiliated Kashi Hospital of Sun Yat-sen University, The First People's Hospital of Kashi Prefecture, Kashi, 844000, China.
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Randhawa J, Chiang M, Porporato N, Pardeshi AA, Dredge J, Apolo Aroca G, Tun TA, Quah JH, Tan M, Higashita R, Aung T, Varma R, Xu BY. Generalisability and performance of an OCT-based deep learning classifier for community-based and hospital-based detection of gonioscopic angle closure. Br J Ophthalmol 2023; 107:511-517. [PMID: 34670749 PMCID: PMC9018872 DOI: 10.1136/bjophthalmol-2021-319470] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 10/02/2021] [Indexed: 11/04/2022]
Abstract
PURPOSE To assess the generalisability and performance of a deep learning classifier for automated detection of gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images. METHODS A convolutional neural network (CNN) model developed using data from the Chinese American Eye Study (CHES) was used to detect gonioscopic angle closure in AS-OCT images with reference gonioscopy grades provided by trained ophthalmologists. Independent test data were derived from the population-based CHES, a community-based clinic in Singapore, and a hospital-based clinic at the University of Southern California (USC). Classifier performance was evaluated with receiver operating characteristic curve and area under the receiver operating characteristic curve (AUC) metrics. Interexaminer agreement between the classifier and two human examiners at USC was calculated using Cohen's kappa coefficients. RESULTS The classifier was tested using 640 images (311 open and 329 closed) from 127 Chinese Americans, 10 165 images (9595 open and 570 closed) from 1318 predominantly Chinese Singaporeans and 300 images (234 open and 66 closed) from 40 multiethnic USC patients. The classifier achieved similar performance in the CHES (AUC=0.917), Singapore (AUC=0.894) and USC (AUC=0.922) cohorts. Standardising the distribution of gonioscopy grades across cohorts produced similar AUC metrics (range 0.890-0.932). The agreement between the CNN classifier and two human examiners (Ҡ=0.700 and 0.704) approximated interexaminer agreement (Ҡ=0.693) in the USC cohort. CONCLUSION An OCT-based deep learning classifier demonstrated consistent performance detecting gonioscopic angle closure across three independent patient populations. This automated method could aid ophthalmologists in the assessment of angle status in diverse patient populations.
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Affiliation(s)
- Jasmeen Randhawa
- Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Michael Chiang
- Roski Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Natalia Porporato
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | - Anmol A Pardeshi
- Roski Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Justin Dredge
- Roski Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Galo Apolo Aroca
- Roski Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Tin A Tun
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | | | - Marcus Tan
- Ophthalmology, National University of Singapore, Singapore
| | | | - Tin Aung
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
- Ophthalmology, National University of Singapore, Singapore
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, California, USA
| | - Benjamin Y Xu
- Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
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Jiang X, Xie M, Ma L, Dong L, Li D. International publication trends in the application of artificial intelligence in ophthalmology research: an updated bibliometric analysis. ANNALS OF TRANSLATIONAL MEDICINE 2023; 11:219. [PMID: 37007552 PMCID: PMC10061466 DOI: 10.21037/atm-22-3773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 12/18/2022] [Indexed: 03/17/2023]
Abstract
Background The literature on artificial intelligence (AI)-related topics has been expanding rapidly over the last two decades, showing that AI is a crucial force in advancing ophthalmology. This analysis aims to provide a dynamic and longitudinal bibliometric analysis of AI-related ophthalmic papers. Methods The Web of Science was searched to retrieve papers regarding the application of AI in ophthalmology published in the English language up to May 2022. The variables were analyzed using Microsoft Excel 2019 and GraphPad Prism 9. Data visualization was performed using VOSviewer and CiteSpace. Results In this study, a total of 1,686 publications were analyzed. Recently, AI-related ophthalmology research has increased exponentially. China was the most productive country in this research field, with 483 articles, but the United States of America (446 publications) contributed most to the sum of citations and the H-index. The League of European Research Universities, Ting DSW, and Daniel SW were the most prolific institution and researchers. This field is primarily concerned with diabetic retinopathy (DR), glaucoma, optical coherence tomography, and the classification and diagnosis of fundus pictures. Current hotspots in AI research include deep learning, diagnosing and predicting systemic disorders by fundus images, incidence and progression of ocular diseases, and outcome prediction. Conclusions This analysis thoroughly reviews AI-related research in ophthalmology to help academics better comprehend the growth and possible practice consequences of AI. The association between eye and systemic biomarkers, telemedicine, real-world studies, and the development and application of new AI algorithms, such as visual converters, will continue to be research hotspots over the next few years.
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Affiliation(s)
- Xue Jiang
- Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Capital Medical University, Beijing Tongren Hospital, Beijing, China
| | - Minyue Xie
- Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Capital Medical University, Beijing Tongren Hospital, Beijing, China
| | - Lan Ma
- Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Capital Medical University, Beijing Tongren Hospital, Beijing, China
| | - Li Dong
- Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Capital Medical University, Beijing Tongren Hospital, Beijing, China
| | - Dongmei Li
- Beijing Tongren Eye Center, Beijing Ophthalmology & Visual Sciences Key Lab, Capital Medical University, Beijing Tongren Hospital, Beijing, China
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Lemij HG, de Vente C, Sánchez CI, Vermeer KA. Characteristics of a large, labeled dataset for the training of artificial intelligence for glaucoma screening with fundus photographs. OPHTHALMOLOGY SCIENCE 2023; 3:100300. [PMID: 37113471 PMCID: PMC10127130 DOI: 10.1016/j.xops.2023.100300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Revised: 02/12/2023] [Accepted: 03/13/2023] [Indexed: 03/19/2023]
Abstract
Purpose Significant visual impairment due to glaucoma is largely caused by the disease being detected too late. Objective To build a labeled data set for training artificial intelligence (AI) algorithms for glaucoma screening by fundus photography, to assess the accuracy of the graders, and to characterize the features of all eyes with referable glaucoma (RG). Design Cross-sectional study. Subjects Color fundus photographs (CFPs) of 113 893 eyes of 60 357 individuals were obtained from EyePACS, California, United States, from a population screening program for diabetic retinopathy. Methods Carefully selected graders (ophthalmologists and optometrists) graded the images. To qualify, they had to pass the European Optic Disc Assessment Trial optic disc assessment with ≥ 85% accuracy and 92% specificity. Of 90 candidates, 30 passed. Each image of the EyePACS set was then scored by varying random pairs of graders as "RG," "no referable glaucoma (NRG)," or "ungradable (UG)." In case of disagreement, a glaucoma specialist made the final grading. Referable glaucoma was scored if visual field damage was expected. In case of RG, graders were instructed to mark up to 10 relevant glaucomatous features. Main Outcome Measures Qualitative features in eyes with RG. Results The performance of each grader was monitored; if the sensitivity and specificity dropped below 80% and 95%, respectively (the final grade served as reference), they exited the study and their gradings were redone by other graders. In all, 20 graders qualified; their mean sensitivity and specificity (standard deviation [SD]) were 85.6% (5.7) and 96.1% (2.8), respectively. The 2 graders agreed in 92.45% of the images (Gwet's AC2, expressing the inter-rater reliability, was 0.917). Of all gradings, the sensitivity and specificity (95% confidence interval) were 86.0 (85.2-86.7)% and 96.4 (96.3-96.5)%, respectively. Of all gradable eyes (n = 111 183; 97.62%) the prevalence of RG was 4.38%. The most common features of RG were the appearance of the neuroretinal rim (NRR) inferiorly and superiorly. Conclusions A large data set of CFPs was put together of sufficient quality to develop AI screening solutions for glaucoma. The most common features of RG were the appearance of the NRR inferiorly and superiorly. Disc hemorrhages were a rare feature of RG. Financial Disclosures Proprietary or commercial disclosure may be found after the references.
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A Neural Network for Automated Image Quality Assessment of Optic Disc Photographs. J Clin Med 2023; 12:jcm12031217. [PMID: 36769865 PMCID: PMC9917571 DOI: 10.3390/jcm12031217] [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: 12/20/2022] [Revised: 01/26/2023] [Accepted: 01/31/2023] [Indexed: 02/05/2023] Open
Abstract
This study describes the development of a convolutional neural network (CNN) for automated assessment of optic disc photograph quality. Using a code-free deep learning platform, a total of 2377 optic disc photographs were used to develop a deep CNN capable of determining optic disc photograph quality. Of these, 1002 were good-quality images, 609 were acceptable-quality, and 766 were poor-quality images. The dataset was split 80/10/10 into training, validation, and test sets and balanced for quality. A ternary classification model (good, acceptable, and poor quality) and a binary model (usable, unusable) were developed. In the ternary classification system, the model had an overall accuracy of 91% and an AUC of 0.98. The model had higher predictive accuracy for images of good (93%) and poor quality (96%) than for images of acceptable quality (91%). The binary model performed with an overall accuracy of 98% and an AUC of 0.99. When validated on 292 images not included in the original training/validation/test dataset, the model's accuracy was 85% on the three-class classification task and 97% on the binary classification task. The proposed system for automated image-quality assessment for optic disc photographs achieves high accuracy in both ternary and binary classification systems, and highlights the success achievable with a code-free platform. There is wide clinical and research potential for such a model, with potential applications ranging from integration into fundus camera software to provide immediate feedback to ophthalmic photographers, to prescreening large databases before their use in research.
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Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State. Int J Mol Sci 2023; 24:ijms24032814. [PMID: 36769127 PMCID: PMC9917798 DOI: 10.3390/ijms24032814] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/07/2023] [Accepted: 01/18/2023] [Indexed: 02/05/2023] Open
Abstract
Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease's development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers.
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