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Kim H, Moon S, Lee J, Kim E, Jin SW, Kim JL, Lee SU, Kim J, Yoo S, Lee J, Song G, Lee J. Fuzzy clustering of 24-2 visual field patterns can detect glaucoma progression. PLoS One 2024; 19:e0309011. [PMID: 39231172 PMCID: PMC11373827 DOI: 10.1371/journal.pone.0309011] [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: 05/02/2024] [Accepted: 08/03/2024] [Indexed: 09/06/2024] Open
Abstract
PURPOSE To represent 24-2 visual field (VF) losses of individual patients using a hybrid approach of archetypal analysis (AA) and fuzzy c-means (FCM) clustering. METHODS In this multicenter retrospective study, we classified characteristic patterns of 24-2 VF using AA and decomposed them with FCM clustering. We predicted the change in mean deviation (MD) through supervised machine learning from decomposition coefficient change. In addition, we compared the areas under the receiver operating characteristic curves (AUCs) of the decomposition coefficient slopes to detect VF progression using three criteria: MD slope, Visual Field Index slope, and pointwise linear regression analysis. RESULTS We identified 16 characteristic patterns (archetypes or ATs) of 24-2 VF from 132,938 VFs of 18,033 participants using AA. The hybrid approach using FCM revealed a lower mean squared error and greater correlation coefficient than the AA single approach for predicting MD change (all P ≤ 0.001). Three of 16 AUCs of the FCM decomposition coefficient slopes outperformed the AA decomposition coefficient slopes in detecting VF progression for all three criteria (AT5, superior altitudinal defect; AT10, double arcuate defect; AT13, total loss) (all P ≤ 0.028). CONCLUSION A hybrid approach combining AA and FCM to analyze 24-2 VF can visualize VF tests in characteristic patterns and enhance detection of VF progression with lossless decomposition.
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Affiliation(s)
- Hwayeong Kim
- Department of Ophthalmology, Pusan National University College of Medicine, Busan, Korea
| | - Sangwoo Moon
- Department of Ophthalmology, Pusan National University Yangsan Hospital, Pusan National University School of Medicine, Yangsan, Korea
| | - Joohwang Lee
- Department of Ophthalmology, Pusan National University College of Medicine, Busan, Korea
| | - EunAh Kim
- Department of Ophthalmology, Haeundae Paik Hospital, Inje University College of Medicine, Busan, South Korea
| | - Sang Wook Jin
- Department of Ophthalmology, Dong-A University College of Medicine, Busan, Korea
| | - Jung Lim Kim
- Department of Ophthalmology, Busan Paik Hospital, Inje University College of Medicine, Busan, Korea
| | - Seung Uk Lee
- Department of Ophthalmology, Kosin University College of Medicine, Busan, Korea
| | - Jinmi Kim
- Department of Biostatistics, Clinical Trial Center, Biomedical Research Institute, Pusan National University Hospital, Busan, Korea
| | - Seungtae Yoo
- Division of Artificial Intelligence, Department of Information Convergence Engineering, Pusan National University, Busan, Korea
| | - Jiwon Lee
- Division of Artificial Intelligence, Department of Information Convergence Engineering, Pusan National University, Busan, Korea
| | - Giltae Song
- Division of Artificial Intelligence, Department of Information Convergence Engineering, Pusan National University, Busan, Korea
- Center for Artificial Intelligence Research, Pusan National University, Busan, Korea
- School of Computer Science and Engineering, Pusan National University, Busan, Korea
| | - Jiwoong Lee
- Department of Ophthalmology, Pusan National University College of Medicine, Busan, Korea
- Biomedical Research Institute, Pusan National University Hospital, Busan, Korea
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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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Târcoveanu F, Leon F, Lisa C, Curteanu S, Feraru A, Ali K, Anton N. The use of artificial neural networks in studying the progression of glaucoma. Sci Rep 2024; 14:19597. [PMID: 39179625 PMCID: PMC11344130 DOI: 10.1038/s41598-024-70748-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2024] [Accepted: 08/20/2024] [Indexed: 08/26/2024] Open
Abstract
In ophthalmology, artificial intelligence methods show great promise due to their potential to enhance clinical observations with predictive capabilities and support physicians in diagnosing and treating patients. This paper focuses on modelling glaucoma evolution because it requires early diagnosis, individualized treatment, and lifelong monitoring. Glaucoma is a chronic, progressive, irreversible, multifactorial optic neuropathy that primarily affects elderly individuals. It is important to emphasize that the processed data are taken from medical records, unlike other studies in the literature that rely on image acquisition and processing. Although more challenging to handle, this approach has the advantage of including a wide range of parameters in large numbers, which can highlight their potential influence. Artificial neural networks are used to study glaucoma progression, designed through successive trials for near-optimal configurations using the NeuroSolutions and PyTorch frameworks. Furthermore, different problems are formulated to demonstrate the influence of various structural and functional parameters on the study of glaucoma progression. Optimal neural networks were obtained using a program written in Python using the PyTorch deep learning framework. For various tasks, very small errors in training and validation, under 5%, were obtained. It has been demonstrated that very good results can be achieved, making them credible and useful for medical practice.
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Affiliation(s)
- Filip Târcoveanu
- Ophthalmology Department, Faculty of Medicine, University of Medicine and Pharmacy "Gr. T. Popa" Iasi, University Street No 16, 700115, Iasi, Romania
| | - Florin Leon
- Faculty of Automatic Control and Computer Engineering, "Gheorghe Asachi" Technical University of Iasi, 27 Mangeron Street, 700050, Iasi, Romania
| | - Cătălin Lisa
- Department of Chemical Engineering, Faculty of Chemical Engineering and Environmental Protection "Cristofor Simionescu", "Gheorghe Asachi" Technical University of Iasi, 73 Mangeron Street, 700050, Iasi, Romania
| | - Silvia Curteanu
- Department of Chemical Engineering, Faculty of Chemical Engineering and Environmental Protection "Cristofor Simionescu", "Gheorghe Asachi" Technical University of Iasi, 73 Mangeron Street, 700050, Iasi, Romania.
| | - Andreea Feraru
- Faculty of Economic Science, "Vasile Alecsandri" University of Bacau, Calea Marasesti 156, 600115, Bacau, Romania
| | - Kashif Ali
- Countess of Chester Hospital, Liverpool Rd, Chester, CH21UL, UK
| | - Nicoleta Anton
- Ophthalmology Department, Faculty of Medicine, University of Medicine and Pharmacy "Gr. T. Popa" Iasi, University Street No 16, 700115, Iasi, Romania.
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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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5
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Ha A, Sun S, Kim YK, Jeoung JW, Kim HC, Park KH. Deep-learning-based prediction of glaucoma conversion in normotensive glaucoma suspects. Br J Ophthalmol 2024; 108:927-932. [PMID: 37918891 DOI: 10.1136/bjo-2022-323167] [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: 12/30/2022] [Accepted: 09/03/2023] [Indexed: 11/04/2023]
Abstract
BACKGROUND/AIMS To assess the performance of deep-learning (DL) models for prediction of conversion to normal-tension glaucoma (NTG) in normotensive glaucoma suspect (GS) patients. METHODS Datasets of 12 458 GS eyes were reviewed. Two hundred and ten eyes (105 eyes showing NTG conversion and 105 without conversion), followed up for a minimum of 7 years during which intraocular pressure (IOP) was lower than 21 mm Hg, were included. The features of two fundus images (optic disc photography and red-free retinal nerve fibre layer (RNFL) photography) were extracted by convolutional auto encoder. The extracted features as well as 15 clinical features including age, sex, IOP, spherical equivalent, central corneal thickness, axial length, average circumpapillary RNFL thickness, systolic/diastolic blood pressure and body mass index were used to predict NTG conversion. Prediction was performed using three machine-learning classifiers (ie, XGBoost, Random Forest, Gradient Boosting) with different feature combinations. RESULTS All three algorithms achieved high diagnostic accuracy for NTG conversion prediction. The AUCs ranged from 0.987 (95% CI 0.978 to 1.000; Random Forest trained with both fundus images and clinical features) and 0.994 (95% CI 0.984 to 1.000; XGBoost trained with both fundus images and clinical features). XGBoost showed the best prediction performance for time to NTG conversion (mean squared error, 2.24). The top three important clinical features for time-to-conversion prediction were baseline IOP, diastolic blood pressure and average circumpapillary RNFL thickness. CONCLUSION DL models, trained with both fundus images and clinical data, showed the potential to predict whether and when normotensive GS patients will show conversion to NTG.
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Affiliation(s)
- Ahnul Ha
- Department of Ophthalmology, Jeju National University, Jeju, Korea (the Republic of)
| | - Sukkyu Sun
- Department of AI Software Convergence, Dongguk University, Seoul, Korea (the Republic of)
| | - Young Kook Kim
- Department of Ophthalmology, Seoul National University Hospital, Seoul, South Korea
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
| | - Jin Wook Jeoung
- Department of Ophthalmology, Seoul National University Hospital, Seoul, South Korea
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
| | - Hee Chan Kim
- Department of Biomedical Engineering, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
| | - Ki Ho Park
- Department of Ophthalmology, Seoul National University Hospital, Seoul, South Korea
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul, Korea (the Republic of)
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Wang N, Yang S, Gao Q, Jin X. Immersive teaching using virtual reality technology to improve ophthalmic surgical skills for medical postgraduate students. Postgrad Med 2024; 136:487-495. [PMID: 38819302 DOI: 10.1080/00325481.2024.2363171] [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: 04/08/2024] [Accepted: 05/28/2024] [Indexed: 06/01/2024]
Abstract
Medical education is primarily based on practical schooling and the accumulation of experience and skills, which is important for the growth and development of young ophthalmic surgeons. However, present learning and refresher methods are constrained by several factors. Nevertheless, virtual reality (VR) technology has considerably contributed to medical training worldwide, providing convenient and practical auxiliary value for the selection of students' sub-majors. Moreover, it offers previously inaccessible surgical step training, scenario simulations, and immersive evaluation exams. This paper outlines the current applications of VR immersive teaching methods for ophthalmic surgery interns.
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Affiliation(s)
- Ning Wang
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
| | - Shuo Yang
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
| | - Qi Gao
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
| | - Xiuming Jin
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, China
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Swaminathan U, Daigavane S. Unveiling the Potential: A Comprehensive Review of Artificial Intelligence Applications in Ophthalmology and Future Prospects. Cureus 2024; 16:e61826. [PMID: 38975538 PMCID: PMC11227442 DOI: 10.7759/cureus.61826] [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: 05/25/2024] [Accepted: 06/06/2024] [Indexed: 07/09/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative force in healthcare, particularly in the field of ophthalmology. This comprehensive review examines the current applications of AI in ophthalmology, highlighting its significant contributions to diagnostic accuracy, treatment efficacy, and patient care. AI technologies, such as deep learning algorithms, have demonstrated exceptional performance in the early detection and diagnosis of various eye conditions, including diabetic retinopathy (DR), age-related macular degeneration (AMD), and glaucoma. Additionally, AI has enhanced the analysis of ophthalmic imaging techniques like optical coherence tomography (OCT) and fundus photography, facilitating more precise disease monitoring and management. The review also explores AI's role in surgical assistance, predictive analytics, and personalized treatment plans, showcasing its potential to revolutionize clinical practice and improve patient outcomes. Despite these advancements, challenges such as data privacy, regulatory hurdles, and ethical considerations remain. The review underscores the need for continued research and collaboration among clinicians, researchers, technology developers, and policymakers to address these challenges and fully harness the potential of AI in improving eye health worldwide. By integrating AI with teleophthalmology and developing AI-driven wearable devices, the future of ophthalmic care promises enhanced accessibility, efficiency, and efficacy, ultimately reducing the global burden of visual impairment and blindness.
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Affiliation(s)
- Uma Swaminathan
- Ophthalmology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
| | - Sachin Daigavane
- Ophthalmology, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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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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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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10
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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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Bekollari M, Dettoraki M, Stavrou V, Glotsos D, Liaparinos P. Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device. Diagnostics (Basel) 2024; 14:349. [PMID: 38396388 PMCID: PMC10888400 DOI: 10.3390/diagnostics14040349] [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: 01/10/2024] [Revised: 01/29/2024] [Accepted: 02/01/2024] [Indexed: 02/25/2024] Open
Abstract
Glaucoma is a chronic, progressive eye disease affecting the optic nerve, which may cause visual damage and blindness. In this study, we present a machine-learning investigation to classify patients with glaucoma (case group) with respect to normal participants (control group). We examined 172 eyes at the Ophthalmology Clinic of the "Elpis" General Hospital of Athens between October 2022 and September 2023. In addition, we investigated the glaucoma classification in terms of the following: (a) eye selection and (b) gender. Our methodology was based on the features extracted via two diagnostic optical systems: (i) conventional optical coherence tomography (OCT) and (ii) a modern RETeval portable device. The machine-learning approach comprised three different classifiers: the Bayesian, the Probabilistic Neural Network (PNN), and Support Vectors Machines (SVMs). For all cases examined, classification accuracy was found to be significantly higher when using the RETeval device with respect to the OCT system, as follows: 14.7% for all participants, 13.4% and 29.3% for eye selection (right and left, respectively), and 25.6% and 22.6% for gender (male and female, respectively). The most efficient classifier was found to be the SVM compared to the PNN and Bayesian classifiers. In summary, all aforementioned comparisons demonstrate that the RETeval device has the advantage over the OCT system for the classification of glaucoma patients by using the machine-learning approach.
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Affiliation(s)
- Marsida Bekollari
- Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Athens, Greece; (M.B.); (D.G.)
| | - Maria Dettoraki
- Department of Ophthalmology, “Elpis” General Hospital, 11522 Athens, Greece
| | - Valentina Stavrou
- Department of Ophthalmology, “Elpis” General Hospital, 11522 Athens, Greece
| | - Dimitris Glotsos
- Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Athens, Greece; (M.B.); (D.G.)
| | - Panagiotis Liaparinos
- Department of Biomedical Engineering, University of West Attica, Ag. Spyridonos, 12243 Athens, Greece; (M.B.); (D.G.)
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12
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Kaushik M, Tiwari P, Dada T, Dada R. Beyond the optic nerve: Genetics, diagnosis, and promising therapies for glaucoma. Gene 2024; 894:147983. [PMID: 37952746 DOI: 10.1016/j.gene.2023.147983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 11/07/2023] [Accepted: 11/09/2023] [Indexed: 11/14/2023]
Abstract
Glaucoma stands as a leading global cause of blindness, affecting millions. It entails optic nerve damage and vision loss, categorized into open-angle and closed-angle glaucoma with subtypes like POAG, ACG, XFG, PCG, PDG, and developmental glaucoma. The pathophysiological and genetic factors behind glaucoma remain partially understood, with past studies linking intraocular pressure (IOP) levels to retinal ganglion cell death. Open-angle glaucoma involves elevated resistance to aqueous outflow via the trabecular meshwork, while angle-closure glaucoma typically sees drainage pathways obstructed by the iris. Genes have been identified for POAG, ACG, XFG, PCG, PDG, and developmental glaucoma, allowing for early-onset detection and the emergence of gene therapy as an effective treatment. Nevertheless, diagnostic and treatment options have their constraints, necessitating large-scale, well-designed studies to deepen our grasp of genetics' role in glaucoma's pathogenesis. This review delves into glaucoma's risk factors, pathophysiology, genetics, diagnosis, and available treatment options, including gene therapy. Additionally, it suggests alternative therapies like yoga and meditation as adjunct treatments for glaucoma prevention. Overall, this review advances our comprehension of the pathophysiology and genetic associations of glaucoma while highlighting the potential of gene therapy as a treatment avenue. Further research is imperative to fully elucidate the genetic mechanisms underpinning glaucoma and to devise effective treatments.
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Affiliation(s)
- Meenakshi Kaushik
- Lab for Molecular Reproduction and Genetics, Department of Anatomy, All India Institute of Medical Sciences (AIIMS), New Delhi 110029, India
| | - Prabhakar Tiwari
- Lab for Molecular Reproduction and Genetics, Department of Anatomy, All India Institute of Medical Sciences (AIIMS), New Delhi 110029, India.
| | - Tanuj Dada
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, AIIMS, New Delhi, India
| | - Rima Dada
- Lab for Molecular Reproduction and Genetics, Department of Anatomy, All India Institute of Medical Sciences (AIIMS), New Delhi 110029, India.
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13
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Chuter B, Huynh J, Bowd C, Walker E, Rezapour J, Brye N, Belghith A, Fazio MA, Girkin CA, De Moraes G, Liebmann JM, Weinreb RN, Zangwill LM, Christopher M. Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection. Transl Vis Sci Technol 2024; 13:23. [PMID: 38285462 PMCID: PMC10829806 DOI: 10.1167/tvst.13.1.23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Accepted: 12/26/2023] [Indexed: 01/30/2024] Open
Abstract
Purpose To develop and evaluate a deep learning (DL) model to assess fundus photograph quality, and quantitatively measure its impact on automated POAG detection in independent study populations. Methods Image quality ground truth was determined by manual review of 2815 fundus photographs of healthy and POAG eyes from the Diagnostic Innovations in Glaucoma Study and African Descent and Glaucoma Evaluation Study (DIGS/ADAGES), as well as 11,350 from the Ocular Hypertension Treatment Study (OHTS). Human experts assessed a photograph as high quality if of sufficient quality to determine POAG status and poor quality if not. A DL quality model was trained on photographs from DIGS/ADAGES and tested on OHTS. The effect of DL quality assessment on DL POAG detection was measured using area under the receiver operating characteristic (AUROC). Results The DL quality model yielded an AUROC of 0.97 for differentiating between high- and low-quality photographs; qualitative human review affirmed high model performance. Diagnostic accuracy of the DL POAG model was significantly greater (P < 0.001) in good (AUROC, 0.87; 95% CI, 0.80-0.92) compared with poor quality photographs (AUROC, 0.77; 95% CI, 0.67-0.88). Conclusions The DL quality model was able to accurately assess fundus photograph quality. Using automated quality assessment to filter out low-quality photographs increased the accuracy of a DL POAG detection model. Translational Relevance Incorporating DL quality assessment into automated review of fundus photographs can help to decrease the burden of manual review and improve accuracy for automated DL POAG detection.
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Affiliation(s)
- Benton Chuter
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Justin Huynh
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Christopher Bowd
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Evan Walker
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Jasmin Rezapour
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
- Department of Ophthalmology, University Medical Center Mainz, Germany
| | - Nicole Brye
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Akram Belghith
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Massimo A. Fazio
- School of Medicine, Callahan Eye Hospital, University of Alabama-Birmingham, Birmingham, Alabama, United States
| | - Christopher A. Girkin
- School of Medicine, Callahan Eye Hospital, University of Alabama-Birmingham, Birmingham, Alabama, United States
| | - Gustavo De Moraes
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Medical Center, New York, New York, United States
| | - Jeffrey M. Liebmann
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Medical Center, New York, New York, United States
| | - Robert N. Weinreb
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Linda M. Zangwill
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
| | - Mark Christopher
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California, United States
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14
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Jayaram H, Kolko M, Friedman DS, Gazzard G. Glaucoma: now and beyond. Lancet 2023; 402:1788-1801. [PMID: 37742700 DOI: 10.1016/s0140-6736(23)01289-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Revised: 06/11/2023] [Accepted: 06/19/2023] [Indexed: 09/26/2023]
Abstract
The glaucomas are a group of conditions leading to irreversible sight loss and characterised by progressive loss of retinal ganglion cells. Although not always elevated, intraocular pressure is the only modifiable risk factor demonstrated by large clinical trials. It remains the leading cause of irreversible blindness, but timely treatment to lower intraocular pressure is effective at slowing the rate of vision loss from glaucoma. Methods for lowering intraocular pressure include laser treatments, topical medications, and surgery. Although modern surgical innovations aim to be less invasive, many have been introduced with little supporting evidence from randomised controlled trials. Many cases remain undiagnosed until the advanced stages of disease due to the limitations of screening and poor access to opportunistic case finding. Future research aims to generate evidence for intraocular pressure-independent neuroprotective treatments, personalised treatment through genetic risk profiling, and exploration of potential advanced cellular and gene therapies.
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Affiliation(s)
- Hari Jayaram
- Glaucoma Service, Moorfields Eye Hospital NHS Foundation Trust, London, UK; UCL Institute of Ophthalmology, London, UK; National Institute for Health and Care Research Moorfields Biomedical Research Centre, London, UK
| | - Miriam Kolko
- Copenhagen University Hospital, Rigshospitalet, Glostrup, Denmark; University of Copenhagen, Department of Drug Design and Pharmacology, Copenhagen, Denmark
| | - David S Friedman
- Massachusetts Eye and Ear Hospital, Glaucoma Center of Excellence, Boston, MA, USA; Harvard University, Boston, MA, USA
| | - Gus Gazzard
- Glaucoma Service, Moorfields Eye Hospital NHS Foundation Trust, London, UK; UCL Institute of Ophthalmology, London, UK; National Institute for Health and Care Research Moorfields Biomedical Research Centre, London, UK.
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15
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Schmetterer L, Scholl H, Garhöfer G, Janeschitz-Kriegl L, Corvi F, Sadda SR, Medeiros FA. Endpoints for clinical trials in ophthalmology. Prog Retin Eye Res 2023; 97:101160. [PMID: 36599784 DOI: 10.1016/j.preteyeres.2022.101160] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 12/22/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023]
Abstract
With the identification of novel targets, the number of interventional clinical trials in ophthalmology has increased. Visual acuity has for a long time been considered the gold standard endpoint for clinical trials, but in the recent years it became evident that other endpoints are required for many indications including geographic atrophy and inherited retinal disease. In glaucoma the currently available drugs were approved based on their IOP lowering capacity. Some recent findings do, however, indicate that at the same level of IOP reduction, not all drugs have the same effect on visual field progression. For neuroprotection trials in glaucoma, novel surrogate endpoints are required, which may either include functional or structural parameters or a combination of both. A number of potential surrogate endpoints for ophthalmology clinical trials have been identified, but their validation is complicated and requires solid scientific evidence. In this article we summarize candidates for clinical endpoints in ophthalmology with a focus on retinal disease and glaucoma. Functional and structural biomarkers, as well as quality of life measures are discussed, and their potential to serve as endpoints in pivotal trials is critically evaluated.
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Affiliation(s)
- Leopold Schmetterer
- Singapore Eye Research Institute, Singapore; SERI-NTU Advanced Ocular Engineering (STANCE), Singapore; Academic Clinical Program, Duke-NUS Medical School, Singapore; School of Chemistry, Chemical Engineering and Biotechnology, Nanyang Technological University, Singapore; Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria; Center for Medical Physics and Biomedical Engineering, Medical University Vienna, Vienna, Austria; Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland.
| | - Hendrik Scholl
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland; Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Gerhard Garhöfer
- Department of Clinical Pharmacology, Medical University Vienna, Vienna, Austria
| | - Lucas Janeschitz-Kriegl
- Institute of Molecular and Clinical Ophthalmology, Basel, Switzerland; Department of Ophthalmology, University of Basel, Basel, Switzerland
| | - Federico Corvi
- Eye Clinic, Department of Biomedical and Clinical Sciences "Luigi Sacco", University of Milan, Italy
| | - SriniVas R Sadda
- Doheny Eye Institute, Los Angeles, CA, USA; Department of Ophthalmology, David Geffen School of Medicine at University of California, Los Angeles, CA, USA
| | - Felipe A Medeiros
- Vision, Imaging and Performance Laboratory, Department of Ophthalmology, Duke Eye Center, Duke University, Durham, NC, USA
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16
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Cellini F, Caamaño D, Carrasco B, Juberías JR, Ossa C, Bringas R, de la Fuente F, Franco P, Coronado D, Pastor JC. Deep Learning Application to Detect Glaucoma with a Mixed Training Approach: Public Database and Expert-Labeled Glaucoma Population. Ophthalmic Res 2023; 66:1278-1285. [PMID: 37778337 DOI: 10.1159/000534251] [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: 03/22/2023] [Accepted: 09/18/2023] [Indexed: 10/03/2023]
Abstract
INTRODUCTION Artificial intelligence has real potential for early identification of ocular diseases such as glaucoma. An important challenge is the requirement for large databases properly selected, which are not easily obtained. We used a relatively original strategy: a glaucoma recognition algorithm trained with fundus images from public databases and then tested and retrained with a carefully selected patient database. METHODS The study's supervised deep learning method was an adapted version of the ResNet-50 architecture previously trained from 10,658 optic head images (glaucomatous or non-glaucomatous) from seven public databases. A total of 1,158 new images labeled by experts from 616 patients were added. The images were categorized after clinical examination including visual fields in 304 (26%) control images or those with ocular hypertension and 347 (30%) images with early, 290 (25%) with moderate, and 217 (19%) with advanced glaucoma. The initial algorithm was tested using 30% of the selected glaucoma database and then re-trained with 70% of this database and tested again. RESULTS The results in the initial sample showed an area under the curve (AUC) of 76% for all images, and 66% for early, 82% for moderate, and 84% for advanced glaucoma. After retraining the algorithm, the respective AUC results were 82%, 72%, 89%, and 91%. CONCLUSION Using combined data from public databases and data selected and labeled by experts facilitated improvement of the system's precision and identified interesting possibilities for obtaining tools for automatic screening of glaucomatous eyes more affordably.
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Affiliation(s)
- Florencia Cellini
- Instituto de Oftalmobiología Aplicada (IOBA), University of Valladolid, Valladolid, Spain
| | - Deborah Caamaño
- Instituto de Oftalmobiología Aplicada (IOBA), University of Valladolid, Valladolid, Spain
| | - Belen Carrasco
- Ophthalmology Department, Hospital Clinico Universitario (HCUV), Valladolid, Spain
| | - José R Juberías
- Instituto de Oftalmobiología Aplicada (IOBA), University of Valladolid, Valladolid, Spain
- Ophthalmology Department, Hospital Clinico Universitario (HCUV), Valladolid, Spain
| | - Carolina Ossa
- Instituto de Oftalmobiología Aplicada (IOBA), University of Valladolid, Valladolid, Spain
| | - Ramón Bringas
- Ophthalmology Department, Hospital Universitario Río Hortega (HURH), Valladolid, Spain
| | | | | | | | - Jose Carlos Pastor
- Instituto de Oftalmobiología Aplicada (IOBA), University of Valladolid, Valladolid, Spain
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17
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Shi M, Lokhande A, Fazli MS, Sharma V, Tian Y, Luo Y, Pasquale LR, Elze T, Boland MV, Zebardast N, Friedman DS, Shen LQ, Wang M. Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma. IEEE J Biomed Health Inform 2023; 27:4329-4340. [PMID: 37347633 PMCID: PMC10560582 DOI: 10.1109/jbhi.2023.3288830] [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] [Indexed: 06/24/2023]
Abstract
Ophthalmic images, along with their derivatives like retinal nerve fiber layer (RNFL) thickness maps, play a crucial role in detecting and monitoring eye diseases such as glaucoma. For computer-aided diagnosis of eye diseases, the key technique is to automatically extract meaningful features from ophthalmic images that can reveal the biomarkers (e.g., RNFL thinning patterns) associated with functional vision loss. However, representation learning from ophthalmic images that links structural retinal damage with human vision loss is non-trivial mostly due to large anatomical variations between patients. This challenge is further amplified by the presence of image artifacts, commonly resulting from image acquisition and automated segmentation issues. In this paper, we present an artifact-tolerant unsupervised learning framework called EyeLearn for learning ophthalmic image representations in glaucoma cases. EyeLearn includes an artifact correction module to learn representations that optimally predict artifact-free images. In addition, EyeLearn adopts a clustering-guided contrastive learning strategy to explicitly capture the affinities within and between images. During training, images are dynamically organized into clusters to form contrastive samples, which encourage learning similar or dissimilar representations for images in the same or different clusters, respectively. To evaluate EyeLearn, we use the learned representations for visual field prediction and glaucoma detection with a real-world dataset of glaucoma patient ophthalmic images. Extensive experiments and comparisons with state-of-the-art methods confirm the effectiveness of EyeLearn in learning optimal feature representations from ophthalmic images.
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18
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Pucchio A, Krance S, Pur DR, Bassi A, Miranda R, Felfeli T. The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review. Eur J Ophthalmol 2023; 33:1816-1833. [PMID: 36426575 PMCID: PMC10469503 DOI: 10.1177/11206721221140948] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2022] [Accepted: 11/01/2022] [Indexed: 08/31/2023]
Abstract
PURPOSE This review focuses on utility of artificial intelligence (AI) in analysis of biofluid markers in glaucoma. We detail the accuracy and validity of AI in the exploration of biomarkers to provide insight into glaucoma pathogenesis. METHODS A comprehensive search was conducted across five electronic databases including Embase, Medline, Cochrane Central Register of Controlled Trials, Cochrane Database of Systematic Reviews, and Web of Science. Studies pertaining to biofluid marker analysis using AI or bioinformatics in glaucoma were included. Identified studies were critically appraised and assessed for risk of bias using the Joanna Briggs Institute Critical Appraisal tools. RESULTS A total of 10,258 studies were screened and 39 studies met the inclusion criteria, including 23 cross-sectional studies (59%), nine prospective cohort studies (23%), six retrospective cohort studies (15%), and one case-control study (3%). Primary open angle glaucoma (POAG) was the most commonly studied subtype (55% of included studies). Twenty-four studies examined disease characteristics, 10 explored treatment decisions, and 5 provided diagnostic clarification. While studies examined at entire metabolomic or proteomic profiles to determine changes in POAG, there was heterogeneity in the data with over 175 unique, differentially expressed biomarkers reported. Discriminant analysis and artificial neural network predictive models displayed strong differentiating ability between glaucoma patients and controls, although these tools were untested in a clinical context. CONCLUSION The use of AI models could inform glaucoma diagnosis with high sensitivity and specificity. While insight into differentially expressed biomarkers is valuable in pathogenic exploration, no clear pathogenic mechanism in glaucoma has emerged.
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Affiliation(s)
- Aidan Pucchio
- School of Medicine, Queen's University, Kingston, Ontario, Canada
| | - Saffire Krance
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Daiana R Pur
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Arshpreet Bassi
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Rafael Miranda
- Toronto Health Economics and Technology Assessment Collaborative, University of Toronto, Toronto, Ontario, Canada
- The Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
| | - Tina Felfeli
- Toronto Health Economics and Technology Assessment Collaborative, University of Toronto, Toronto, Ontario, Canada
- Department of Ophthalmology and Visual Sciences, University of Toronto, Toronto, Ontario, Canada
- The Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Ontario, Canada
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19
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Wang JZ, Lu NH, Du WC, Liu KY, Hsu SY, Wang CY, Chen YJ, Chang LC, Twan WH, Chen TB, Huang YH. Classification of Color Fundus Photographs Using Fusion Extracted Features and Customized CNN Models. Healthcare (Basel) 2023; 11:2228. [PMID: 37570467 PMCID: PMC10418900 DOI: 10.3390/healthcare11152228] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Revised: 08/04/2023] [Accepted: 08/05/2023] [Indexed: 08/13/2023] Open
Abstract
This study focuses on overcoming challenges in classifying eye diseases using color fundus photographs by leveraging deep learning techniques, aiming to enhance early detection and diagnosis accuracy. We utilized a dataset of 6392 color fundus photographs across eight disease categories, which was later augmented to 17,766 images. Five well-known convolutional neural networks (CNNs)-efficientnetb0, mobilenetv2, shufflenet, resnet50, and resnet101-and a custom-built CNN were integrated and trained on this dataset. Image sizes were standardized, and model performance was evaluated via accuracy, Kappa coefficient, and precision metrics. Shufflenet and efficientnetb0demonstrated strong performances, while our custom 17-layer CNN outperformed all with an accuracy of 0.930 and a Kappa coefficient of 0.920. Furthermore, we found that the fusion of image features with classical machine learning classifiers increased the performance, with Logistic Regression showcasing the best results. Our study highlights the potential of AI and deep learning models in accurately classifying eye diseases and demonstrates the efficacy of custom-built models and the fusion of deep learning and classical methods. Future work should focus on validating these methods across larger datasets and assessing their real-world applicability.
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Affiliation(s)
- Jing-Zhe Wang
- Department of Information Engineering, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 84001, Taiwan
| | - Nan-Han Lu
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
- Department of Radiology, E-DA Cancer Hospital, I-Shou University, No. 21, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
| | - Wei-Chang Du
- Department of Information Engineering, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 84001, Taiwan
| | - Kuo-Ying Liu
- Department of Radiology, E-DA Cancer Hospital, I-Shou University, No. 21, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
| | - Shih-Yen Hsu
- Department of Information Engineering, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 84001, Taiwan
| | - Chi-Yuan Wang
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
| | - Yun-Ju Chen
- School of Medicine for International Students, I-Shu University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 84001, Taiwan
| | - Li-Ching Chang
- School of Medicine for International Students, I-Shu University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 84001, Taiwan
| | - Wen-Hung Twan
- Department of Life Sciences, National Taitung University, No. 369, Sec. 2, University Road, Taitung City 95048, Taiwan
| | - Tai-Been Chen
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
- Institute of Statistics, National Yang Ming Chiao Tung University, No. 1001, University Road, Hsinchu 30010, Taiwan
| | - Yung-Hui Huang
- Department of Medical Imaging and Radiological Science, I-Shou University, No. 8, Yida Road, Jiao-Su Village, Yan-Chao District, Kaohsiung City 82445, Taiwan
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20
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Surya J, Garima, Pandy N, Hyungtaek Rim T, Lee G, Priya MNS, Subramanian B, Raman R. Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma. Indian J Ophthalmol 2023; 71:3039-3045. [PMID: 37530278 PMCID: PMC10538813 DOI: 10.4103/ijo.ijo_11_23] [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: 01/02/2023] [Revised: 05/22/2023] [Accepted: 06/02/2023] [Indexed: 08/03/2023] Open
Abstract
Purpose To analyze the efficacy of a deep learning (DL)-based artificial intelligence (AI)-based algorithm in detecting the presence of diabetic retinopathy (DR) and glaucoma suspect as compared to the diagnosis by specialists secondarily to explore whether the use of this algorithm can reduce the cross-referral in three clinical settings: a diabetologist clinic, retina clinic, and glaucoma clinic. Methods This is a prospective observational study. Patients between 35 and 65 years of age were recruited from glaucoma and retina clinics at a tertiary eye care hospital and a physician's clinic. Non-mydriatic fundus photography was performed according to the disease-specific protocols. These images were graded by the AI system and specialist graders and comparatively analyzed. Results Out of 1085 patients, 362 were seen at glaucoma clinics, 341 were seen at retina clinics, and 382 were seen at physician clinics. The kappa agreement between AI and the glaucoma grader was 85% [95% confidence interval (CI): 77.55-92.45%], and retina grading had 91.90% (95% CI: 87.78-96.02%). The retina grader from the glaucoma clinic had 85% agreement, and the glaucoma grader from the retina clinic had 73% agreement. The sensitivity and specificity of AI glaucoma grading were 79.37% (95% CI: 67.30-88.53%) and 99.45 (95% CI: 98.03-99.93), respectively; DR grading had 83.33% (95 CI: 51.59-97.91) and 98.86 (95% CI: 97.35-99.63). The cross-referral accuracy of DR and glaucoma was 89.57% and 95.43%, respectively. Conclusion DL-based AI systems showed high sensitivity and specificity in both patients with DR and glaucoma; also, there was a good agreement between the specialist graders and the AI system.
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Affiliation(s)
- Janani Surya
- Department of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Garima
- Department of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Neha Pandy
- Department of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Tyler Hyungtaek Rim
- Department of Ophthalmology, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Science and Technology, Medi Whale, Seoul, South Korea
| | - Geunyoung Lee
- Department of Science and Technology, Medi Whale, Seoul, South Korea
| | - MN Swathi Priya
- Department of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Brughanya Subramanian
- Department of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
| | - Rajiv Raman
- Department of Ophthalmology, Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Chennai, Tamil Nadu, India
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Haja SA, Mahadevappa V. Advancing glaucoma detection with convolutional neural networks: a paradigm shift in ophthalmology. Rom J Ophthalmol 2023; 67:222-237. [PMID: 37876506 PMCID: PMC10591431 DOI: 10.22336/rjo.2023.39] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/24/2023] [Indexed: 10/26/2023] Open
Abstract
A leading cause of irreversible vision loss, glaucoma needs early detection for effective management. Intraocular Pressure (IOP) is a significant risk factor for glaucoma. Convolutional Neural Networks (CNN) demonstrate exceptional capabilities in analyzing retinal fundus images, a non-invasive and cost-effective imaging technique widely used in glaucoma diagnosis. By learning from large datasets of annotated images, CNN can identify subtle changes in the optic nerve head and retinal structures indicative of glaucoma. This enables early and precise glaucoma diagnosis, empowering clinicians to implement timely interventions. CNNs excel in analyzing complex medical images, detecting subtle changes indicative of glaucoma with high precision. Another valuable diagnostic tool for glaucoma evaluation, Optical Coherence Tomography (OCT), provides high-resolution cross-sectional images of the retina. CNN can effectively analyze OCT scans and extract meaningful features, facilitating the identification of structural abnormalities associated with glaucoma. Visual field testing, performed using devices like the Humphrey Field Analyzer, is crucial for assessing functional vision loss in glaucoma. The integration of CNN with retinal fundus images, OCT scans, visual field testing, and IOP measurements represents a transformative approach to glaucoma detection. These advanced technologies have the potential to revolutionize ophthalmology by enabling early detection, personalized management, and improved patient outcomes. CNNs facilitate remote expert opinions and enhance treatment monitoring. Overcoming challenges such as data scarcity and interpretability can optimize CNN utilization in glaucoma diagnosis. Measuring retinal nerve fiber layer thickness as a diagnostic marker proves valuable. CNN implementation reduces healthcare costs and improves access to quality eye care. Future research should focus on optimizing architectures and incorporating novel biomarkers. CNN integration in glaucoma detection revolutionizes ophthalmology, improving patient outcomes and access to care. This review paves the way for innovative CNN-based glaucoma detection methods. Abbreviations: CNN = Convolutional Neural Networks, AI = Artificial Intelligence, IOP = Intraocular Pressure, OCT = Optical Coherence Tomography, CLSO = Confocal Scanning Laser Ophthalmoscopy, AUC-ROC = Area Under the Receiver Operating Characteristic Curve, RNFL = Retinal Nerve Fiber Layer, RNN = Recurrent Neural Networks, VF = Visual Field, AP = Average Precision, MD = Mean Defect, sLV = square-root of Loss Variance, NN = Neural Network, WHO = World Health Organization.
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Affiliation(s)
- Shafeeq Ahmed Haja
- Department of Ophthalmology, Bangalore Medical College and Research Institute, India
| | - Vidyadevi Mahadevappa
- Department of Ophthalmology, Bangalore Medical College and Research Institute, India
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22
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AlRyalat SA, Singh P, Kalpathy-Cramer J, Kahook MY. Artificial Intelligence and Glaucoma: Going Back to Basics. Clin Ophthalmol 2023; 17:1525-1530. [PMID: 37284059 PMCID: PMC10239633 DOI: 10.2147/opth.s410905] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/24/2023] [Indexed: 06/08/2023] Open
Abstract
There has been a recent surge in the number of publications centered on the use of artificial intelligence (AI) to diagnose various systemic diseases. The Food and Drug Administration has approved several algorithms for use in clinical practice. In ophthalmology, most advances in AI relate to diabetic retinopathy, which is a disease process with agreed upon diagnostic and classification criteria. However, this is not the case for glaucoma, which is a relatively complex disease without agreed-upon diagnostic criteria. Moreover, currently available public datasets that focus on glaucoma have inconstant label quality, further complicating attempts at training AI algorithms efficiently. In this perspective paper, we discuss specific details related to developing AI models for glaucoma and suggest potential steps to overcome current limitations.
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Affiliation(s)
| | - Praveer Singh
- Department of Ophthalmology, University of Colorado School of Medicine, Sue Anschutz-Rodgers Eye Center, Aurora, CO, USA
| | - Jayashree Kalpathy-Cramer
- Department of Ophthalmology, University of Colorado School of Medicine, Sue Anschutz-Rodgers Eye Center, Aurora, CO, USA
| | - Malik Y Kahook
- Department of Ophthalmology, University of Colorado School of Medicine, Sue Anschutz-Rodgers Eye Center, Aurora, CO, USA
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23
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Sharma R, Sharma A. Population stratification strategies in artificial intelligence-based glaucoma monitoring, "corneal anthropology" to bridge gap between genetics and clinics? Indian J Ophthalmol 2023; 71:2304-2306. [PMID: 37202987 PMCID: PMC10391406 DOI: 10.4103/ijo.ijo_3061_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/20/2023] Open
Affiliation(s)
- Rajan Sharma
- Research Associate, Dr. Ashok Sharma's Cornea Centre, Chandigarh, India
| | - Ashok Sharma
- Senior Cornea Consultant and Medical Director, Dr. Ashok Sharma's Cornea Centre, Chandigarh, India
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24
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Arnould L, Meriaudeau F, Guenancia C, Germanese C, Delcourt C, Kawasaki R, Cheung CY, Creuzot-Garcher C, Grzybowski A. Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review. Ophthalmol Ther 2023; 12:657-674. [PMID: 36562928 PMCID: PMC10011267 DOI: 10.1007/s40123-022-00641-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
The healthcare burden of cardiovascular diseases remains a major issue worldwide. Understanding the underlying mechanisms and improving identification of people with a higher risk profile of systemic vascular disease through noninvasive examinations is crucial. In ophthalmology, retinal vascular network imaging is simple and noninvasive and can provide in vivo information of the microstructure and vascular health. For more than 10 years, different research teams have been working on developing software to enable automatic analysis of the retinal vascular network from different imaging techniques (retinal fundus photographs, OCT angiography, adaptive optics, etc.) and to provide a description of the geometric characteristics of its arterial and venous components. Thus, the structure of retinal vessels could be considered a witness of the systemic vascular status. A new approach called "oculomics" using retinal image datasets and artificial intelligence algorithms recently increased the interest in retinal microvascular biomarkers. Despite the large volume of associated research, the role of retinal biomarkers in the screening, monitoring, or prediction of systemic vascular disease remains uncertain. A PubMed search was conducted until August 2022 and yielded relevant peer-reviewed articles based on a set of inclusion criteria. This literature review is intended to summarize the state of the art in oculomics and cardiovascular disease research.
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Affiliation(s)
- Louis Arnould
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France. .,University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR U1219, 33000, Bordeaux, France.
| | - Fabrice Meriaudeau
- Laboratory ImViA, IFTIM, Université Bourgogne Franche-Comté, 21078, Dijon, France
| | - Charles Guenancia
- Pathophysiology and Epidemiology of Cerebro-Cardiovascular Diseases, (EA 7460), Faculty of Health Sciences, Université de Bourgogne Franche-Comté, Dijon, France.,Cardiology Department, Dijon University Hospital, Dijon, France
| | - Clément Germanese
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France
| | - Cécile Delcourt
- University of Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR U1219, 33000, Bordeaux, France
| | - Ryo Kawasaki
- Artificial Intelligence Center for Medical Research and Application, Osaka University Hospital, Osaka, Japan
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Catherine Creuzot-Garcher
- Ophthalmology Department, Dijon University Hospital, 14 Rue Paul Gaffarel, 21079, Dijon CEDEX, France.,Centre des Sciences du Goût et de l'Alimentation, AgroSup Dijon, CNRS, INRAE, Université Bourgogne Franche-Comté, Dijon, France
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland.,Institute for Research in Ophthalmology, Poznan, Poland
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25
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Nazir S, Dickson DM, Akram MU. Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks. Comput Biol Med 2023; 156:106668. [PMID: 36863192 DOI: 10.1016/j.compbiomed.2023.106668] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 01/12/2023] [Accepted: 02/10/2023] [Indexed: 02/21/2023]
Abstract
Artificial Intelligence (AI) techniques of deep learning have revolutionized the disease diagnosis with their outstanding image classification performance. In spite of the outstanding results, the widespread adoption of these techniques in clinical practice is still taking place at a moderate pace. One of the major hindrance is that a trained Deep Neural Networks (DNN) model provides a prediction, but questions about why and how that prediction was made remain unanswered. This linkage is of utmost importance for the regulated healthcare domain to increase the trust in the automated diagnosis system by the practitioners, patients and other stakeholders. The application of deep learning for medical imaging has to be interpreted with caution due to the health and safety concerns similar to blame attribution in the case of an accident involving autonomous cars. The consequences of both a false positive and false negative cases are far reaching for patients' welfare and cannot be ignored. This is exacerbated by the fact that the state-of-the-art deep learning algorithms comprise of complex interconnected structures, millions of parameters, and a 'black box' nature, offering little understanding of their inner working unlike the traditional machine learning algorithms. Explainable AI (XAI) techniques help to understand model predictions which help develop trust in the system, accelerate the disease diagnosis, and meet adherence to regulatory requirements. This survey provides a comprehensive review of the promising field of XAI for biomedical imaging diagnostics. We also provide a categorization of the XAI techniques, discuss the open challenges, and provide future directions for XAI which would be of interest to clinicians, regulators and model developers.
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Affiliation(s)
- Sajid Nazir
- Department of Computing, Glasgow Caledonian University, Glasgow, UK.
| | - Diane M Dickson
- Department of Podiatry and Radiography, Research Centre for Health, Glasgow Caledonian University, Glasgow, UK
| | - Muhammad Usman Akram
- Computer and Software Engineering Department, National University of Sciences and Technology, Islamabad, Pakistan
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26
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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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27
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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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28
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Lee GA, Kong GYX, Liu CH. Visual fields in glaucoma: Where are we now? Clin Exp Ophthalmol 2023; 51:162-169. [PMID: 36751125 DOI: 10.1111/ceo.14210] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 01/25/2023] [Accepted: 02/03/2023] [Indexed: 02/09/2023]
Abstract
Visual fields are an integral part of glaucoma diagnosis and management. COVID has heightened the awareness of the potential for viral spread with the practice of visual fields modified. Mask artefacts can occur due to fogging of the inferior rim of the trail lens. Fortunately, the risk of airborne transmission when field testing is low. The 24-2c may be useful to detect early disease and the 10-2 more sensitive to detect advanced loss. The SITA faster test algorithm is able to reduce testing time thereby improving clinic efficiency, however, may show milder results for moderate or severe glaucoma. The technician has an important role of supervising the visual field performance to achieve reliable output. Home monitoring can provide earlier detection of progression and thus improve monitoring of glaucoma as well as reduce the burden of in-clinic assessments. Artificial Intelligence has been found to have high sensitivity and specificity compared to expert observers in detecting field abnormalities and progression as well as integrating structure with function. Although these advances will improve efficiency and guide accuracy, there will remain a need for clinicians to interpret the results and instigate management.
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Affiliation(s)
- Graham A Lee
- City Eye Centre, Brisbane, Queensland, Australia.,University of Queensland, Herston, Queensland, Australia.,Department of Ophthalmology, Mater Hospital, Brisbane, Queensland, Australia
| | - George Y X Kong
- Glaucoma Investigation and Research Unit, Royal Victorian Eye and Ear Hospital VIC, East Melbourne, Victoria, Australia.,Centre for Eye Research Australia, Royal Victorian Eye, and Ear Hospital, East Melbourne, Victoria, Australia.,Ophthalmology, Department of Surgery, The University of Melbourne, Parkville, Victoria, Australia
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29
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A Deep Learning System Using Optical Coherence Tomography Angiography to Detect Glaucoma and Anterior Ischemic Optic Neuropathy. J Clin Med 2023; 12:jcm12020507. [PMID: 36675435 PMCID: PMC9865592 DOI: 10.3390/jcm12020507] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/30/2022] [Accepted: 01/04/2023] [Indexed: 01/11/2023] Open
Abstract
INTRODUCTION Glaucoma and non-arteritic anterior ischemic optic neuropathy (NAION) are optic neuropathies that can both lead to irreversible blindness. Several studies have compared optical coherence tomography angiography (OCTA) findings in glaucoma and NAION in the presence of similar functional and structural damages with contradictory results. The goal of this study was to use a deep learning system to differentiate OCTA in glaucoma and NAION. MATERIAL AND METHODS Sixty eyes with glaucoma (including primary open angle glaucoma, angle-closure glaucoma, normal tension glaucoma, pigmentary glaucoma, pseudoexfoliative glaucoma and juvenile glaucoma), thirty eyes with atrophic NAION and forty control eyes (NC) were included. All patients underwent OCTA imaging and automatic segmentation was used to analyze the macular superficial capillary plexus (SCP) and the radial peripapillary capillary (RPC) plexus. We used the classic convolutional neural network (CNN) architecture of ResNet50. Attribution maps were obtained using the "Integrated Gradients" method. RESULTS The best performances were obtained with the SCP + RPC model achieving a mean area under the receiver operating characteristics curve (ROC AUC) of 0.94 (95% CI 0.92-0.96) for glaucoma, 0.90 (95% CI 0.86-0.94) for NAION and 0.96 (95% CI 0.96-0.97) for NC. CONCLUSION This study shows that deep learning architecture can classify NAION, glaucoma and normal OCTA images with a good diagnostic performance and may outperform the specialist assessment.
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30
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Chen D, Ran Ran A, Fang Tan T, Ramachandran R, Li F, Cheung CY, Yousefi S, Tham CCY, Ting DSW, Zhang X, Al-Aswad LA. Applications of Artificial Intelligence and Deep Learning in Glaucoma. Asia Pac J Ophthalmol (Phila) 2023; 12:80-93. [PMID: 36706335 DOI: 10.1097/apo.0000000000000596] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/06/2022] [Indexed: 01/28/2023] Open
Abstract
Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.
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Affiliation(s)
- Dinah Chen
- Department of Ophthalmology, NYU Langone Health, New York City, NY
- Genentech Inc, South San Francisco, CA
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment And Research Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Ting Fang Tan
- Singapore Eye Research Institute, Singapore
- Singapore National Eye Center, Singapore
| | | | - Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment And Research Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Siamak Yousefi
- Department of Ophthalmology, The University of Tennessee Health Science Center, Memphis, TN
| | - Clement C Y Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment And Research Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore
- Singapore National Eye Center, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
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31
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Coan LJ, Williams BM, Krishna Adithya V, Upadhyaya S, Alkafri A, Czanner S, Venkatesh R, Willoughby CE, Kavitha S, Czanner G. Automatic detection of glaucoma via fundus imaging and artificial intelligence: A review. Surv Ophthalmol 2023; 68:17-41. [PMID: 35985360 DOI: 10.1016/j.survophthal.2022.08.005] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 08/04/2022] [Accepted: 08/08/2022] [Indexed: 02/01/2023]
Abstract
Glaucoma is a leading cause of irreversible vision impairment globally, and cases are continuously rising worldwide. Early detection is crucial, allowing timely intervention that can prevent further visual field loss. To detect glaucoma an examination of the optic nerve head via fundus imaging can be performed, at the center of which is the assessment of the optic cup and disc boundaries. Fundus imaging is noninvasive and low-cost; however, image examination relies on subjective, time-consuming, and costly expert assessments. A timely question to ask is: "Can artificial intelligence mimic glaucoma assessments made by experts?" Specifically, can artificial intelligence automatically find the boundaries of the optic cup and disc (providing a so-called segmented fundus image) and then use the segmented image to identify glaucoma with high accuracy? We conducted a comprehensive review on artificial intelligence-enabled glaucoma detection frameworks that produce and use segmented fundus images and summarized the advantages and disadvantages of such frameworks. We identified 36 relevant papers from 2011 to 2021 and 2 main approaches: 1) logical rule-based frameworks, based on a set of rules; and 2) machine learning/statistical modeling-based frameworks. We critically evaluated the state-of-art of the 2 approaches, identified gaps in the literature and pointed at areas for future research.
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Affiliation(s)
- Lauren J Coan
- School of Computer Science and Mathematics, Liverpool John Moores University, UK.
| | - Bryan M Williams
- School of Computing and Communications, Lancaster University, UK
| | | | - Swati Upadhyaya
- Department of Glaucoma, Aravind Eye Hospital, Pondicherry, India
| | - Ala Alkafri
- School of Computing, Engineering & Digital Technologies, Teesside University, UK
| | - Silvester Czanner
- School of Computer Science and Mathematics, Liverpool John Moores University, UK; Faculty of Informatics and Information Technologies, Slovak University of Technology, Slovakia
| | - Rengaraj Venkatesh
- Department of Glaucoma and Chief Medical Officer, Aravind Eye Hospital, Pondicherry, India
| | | | | | - Gabriela Czanner
- School of Computer Science and Mathematics, Liverpool John Moores University, UK; Faculty of Informatics and Information Technologies, Slovak University of Technology, Slovakia
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32
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Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions. Diagnostics (Basel) 2022; 13:diagnostics13010100. [PMID: 36611392 PMCID: PMC9818832 DOI: 10.3390/diagnostics13010100] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/12/2022] [Accepted: 12/26/2022] [Indexed: 12/31/2022] Open
Abstract
BACKGROUND Having several applications in medicine, and in ophthalmology in particular, artificial intelligence (AI) tools have been used to detect visual function deficits, thus playing a key role in diagnosing eye diseases and in predicting the evolution of these common and disabling diseases. AI tools, i.e., artificial neural networks (ANNs), are progressively involved in detecting and customized control of ophthalmic diseases. The studies that refer to the efficiency of AI in medicine and especially in ophthalmology were analyzed in this review. MATERIALS AND METHODS We conducted a comprehensive review in order to collect all accounts published between 2015 and 2022 that refer to these applications of AI in medicine and especially in ophthalmology. Neural networks have a major role in establishing the demand to initiate preliminary anti-glaucoma therapy to stop the advance of the disease. RESULTS Different surveys in the literature review show the remarkable benefit of these AI tools in ophthalmology in evaluating the visual field, optic nerve, and retinal nerve fiber layer, thus ensuring a higher precision in detecting advances in glaucoma and retinal shifts in diabetes. We thus identified 1762 applications of artificial intelligence in ophthalmology: review articles and research articles (301 pub med, 144 scopus, 445 web of science, 872 science direct). Of these, we analyzed 70 articles and review papers (diabetic retinopathy (N = 24), glaucoma (N = 24), DMLV (N = 15), other pathologies (N = 7)) after applying the inclusion and exclusion criteria. CONCLUSION In medicine, AI tools are used in surgery, radiology, gynecology, oncology, etc., in making a diagnosis, predicting the evolution of a disease, and assessing the prognosis in patients with oncological pathologies. In ophthalmology, AI potentially increases the patient's access to screening/clinical diagnosis and decreases healthcare costs, mainly when there is a high risk of disease or communities face financial shortages. AI/DL (deep learning) algorithms using both OCT and FO images will change image analysis techniques and methodologies. Optimizing these (combined) technologies will accelerate progress in this area.
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33
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Ahuja AS, Wagner IV, Dorairaj S, Checo L, Hulzen RT. Artificial intelligence in ophthalmology: A multidisciplinary approach. Integr Med Res 2022; 11:100888. [PMID: 36212633 PMCID: PMC9539781 DOI: 10.1016/j.imr.2022.100888] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 09/05/2022] [Accepted: 09/06/2022] [Indexed: 11/13/2022] Open
Affiliation(s)
- Abhimanyu S. Ahuja
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, FL, United States
| | - Isabella V. Wagner
- College of Medicine, Department of Ophthalmology, Mayo Clinic Florida, Jacksonville, FL, United States
| | - Syril Dorairaj
- College of Medicine, Department of Ophthalmology, Mayo Clinic Florida, Jacksonville, FL, United States
| | - Leticia Checo
- College of Medicine, Department of Ophthalmology, Mayo Clinic Florida, Jacksonville, FL, United States
| | - Richard Ten Hulzen
- College of Medicine, Department of Ophthalmology, Mayo Clinic Florida, Jacksonville, FL, United States
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Omodaka K, Horie J, Tokairin H, Kato C, Ouchi J, Ninomiya T, Parmanand S, Tsuda S, Nakazawa T. Deep Learning-Based Noise Reduction Improves Optical Coherence Tomography Angiography Imaging of Radial Peripapillary Capillaries in Advanced Glaucoma. Curr Eye Res 2022; 47:1600-1608. [PMID: 36102611 DOI: 10.1080/02713683.2022.2124275] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
PURPOSE We applied deep learning-based noise reduction (NR) to optical coherence tomography-angiography (OCTA) images of the radial peripapillary capillaries (RPCs) in eyes with glaucoma and investigated the usefulness of this method as an objective analysis of glaucoma. METHODS This cross-sectional study included 118 eyes of 94 open-angle glaucoma patients (male/female = 38/56, age: 56.1 ± 10.3 years). We used OCTA (OCT-HS100, Canon) and built-in software (RX software, v. 4.5) to perform NR and calculate RPC vessel area density (VAD) and skeleton vessel length density (VLD). We also examined NR's effect on reproducibility. Finally, we assessed the vascular structure (PRCs)/function relationship at different glaucoma stages with Spearman's correlation. RESULTS Regardless of NR, RPC parameters had excellent coefficients of variation (1.7-4.1%) in glaucoma patients and controls, and mean deviation (MD) was significantly correlated with VAD (NR: r = 0.835, p < 0.001; non-NR: r = 0.871, p < 0.001) and VLD (NR: r = 0.829, p < 0.001; non-NR: r = 0.837, p < 0.001). For mild, moderate, and advanced glaucoma, the correlation coefficients between MD and VLD were 0.366 (p = 0.028) 0.081 (p = 0.689), and 0.427 (p = 0.017) with NR and 0.405 (p = 0.014), 0.184 (p = 0.360), and 0.339 (p = 0.062) without NR, respectively. CONCLUSION Denoised RPC images might have the potential for a closer structural/functional relationship, in which the floor effect of retinal nerve fiber layer thickness affects measurements. Deep learning-based NR promises to improve glaucoma assessment.
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Affiliation(s)
- Kazuko Omodaka
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan.,Department of Ophthalmic Imaging and Information Analytics, Tohoku University Graduate School of Medicine, Sendai, Japan
| | | | - Hikari Tokairin
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Chiho Kato
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Junko Ouchi
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Takahiro Ninomiya
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Sharma Parmanand
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Satoru Tsuda
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan
| | - Toru Nakazawa
- Department of Ophthalmology, Tohoku University Graduate School of Medicine, Sendai, Japan.,Department of Ophthalmic Imaging and Information Analytics, Tohoku University Graduate School of Medicine, Sendai, Japan
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35
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Bhattacharya S. Artificial intelligence, human intelligence, and the future of public health. AIMS Public Health 2022; 9:644-650. [PMID: 36636147 PMCID: PMC9807415 DOI: 10.3934/publichealth.2022045] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2022] [Revised: 08/12/2022] [Accepted: 09/07/2022] [Indexed: 01/16/2023] Open
Abstract
In this paper, I have described the healthcare problem (maldistribution of doctors) in India. Later, I have introduced the concept of artificial intelligence (AI) and I have described AI technology with various examples, how it is rapidly changing the healthcare scenario across the world. I have also described the various advantages of artificial intelligence technology. At the end of the paper, I have raised some serious concerns regarding complete replacement of human based healthcare technology with artificial intelligence technology. I concluded that there is not the slightest question that AI will influence the future. People must be innovative, insightful, and context-aware for AI to work. This is because humans will continue to contribute value that cannot be reproduced by robots.
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Online circular contrast perimetry via a web-application: optimising parameters and establishing a normative database. Eye (Lond) 2022; 37:1184-1190. [PMID: 35577922 PMCID: PMC9109437 DOI: 10.1038/s41433-022-02085-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 04/25/2022] [Accepted: 04/29/2022] [Indexed: 02/03/2023] Open
Abstract
PURPOSE To establish a normative database and optimise parameters for personal-computer based perimetry via a web-application using circular contrast targets. METHODS Online 24-degree 52-loci perimetry was delivered through a web-application using circular flickering contrast targets. Embedding contrast differentials within targets allows calculation of relative decibel (rdB) per 256-bit greyscale level differential. Target light-band maximum brightness colour was fixed, while the dark-band varied to achieve the desired rdB level. A staircase system was used with two reversals ranging from 0 to 36 rdB levels. Blind spot localisation at the start of the test was used to optimise viewing distance and subsequently count fixation losses. Gaze was maintained on a spinning golden star which moves mid-test to maximise sampling area. Patients performed the test to each eye separately using a computer in a darkened room at 40-45 cm. RESULTS 158 eyes of 101 patients completed the Online Circular Contrast Perimetry (OCCP) test. Mean age was 62.9 ± 14.3 years old. Mean sensitivity reduced with age, at 1.0 relative decibel per decade. Mean sensitivity per locus correlated with standard automated perimetry (SAP) in a physiological hill of vision, with an average difference of 4.02 decibels (95% confidence interval (CI) = 3.77-4.27, p < 0.001) and good agreement between tests. CONCLUSIONS Online circular contract perimetry provides accurate perimetric testing with comparable results to standard automated perimetry.
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Ahuja AS, Bommakanti S, Wagner I, Dorairaj S, Ten Hulzen RD, Checo L. Current and Future Implications of Using Artificial Intelligence in Glaucoma Care. J Curr Ophthalmol 2022; 34:129-132. [PMID: 36147268 PMCID: PMC9486995 DOI: 10.4103/joco.joco_39_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/18/2022] [Accepted: 06/28/2022] [Indexed: 12/05/2022] Open
Affiliation(s)
- Abhimanyu S. Ahuja
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, Florida, USA
| | - Sarvika Bommakanti
- Charles E. Schmidt College of Medicine, Florida Atlantic University, Boca Raton, Florida, USA
| | - Isabella Wagner
- Department of Ophthalmology, Mayo Clinic Florida, Jacksonville, Florida, USA
| | - Syril Dorairaj
- Department of Ophthalmology, Mayo Clinic Florida, Jacksonville, Florida, USA
| | | | - Leticia Checo
- Research Collaborator Ophthalmology, Mayo Clinic Florida, Jacksonville, Florida, USA
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38
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Ittoop SM, Jaccard N, Lanouette G, Kahook MY. The Role of Artificial Intelligence in the Diagnosis and Management of Glaucoma. J Glaucoma 2022; 31:137-146. [PMID: 34930873 DOI: 10.1097/ijg.0000000000001972] [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: 05/20/2021] [Accepted: 12/10/2021] [Indexed: 11/26/2022]
Abstract
Glaucomatous optic neuropathy is the leading cause of irreversible blindness worldwide. Diagnosis and monitoring of disease involves integrating information from the clinical examination with subjective data from visual field testing and objective biometric data that includes pachymetry, corneal hysteresis, and optic nerve and retinal imaging. This intricate process is further complicated by the lack of clear definitions for the presence and progression of glaucomatous optic neuropathy, which makes it vulnerable to clinician interpretation error. Artificial intelligence (AI) and AI-enabled workflows have been proposed as a plausible solution. Applications derived from this field of computer science can improve the quality and robustness of insights obtained from clinical data that can enhance the clinician's approach to patient care. This review clarifies key terms and concepts used in AI literature, discusses the current advances of AI in glaucoma, elucidates the clinical advantages and challenges to implementing this technology, and highlights potential future applications.
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Affiliation(s)
- Sabita M Ittoop
- The George Washington University Medical Faculty Associates, Washington, DC
| | | | | | - Malik Y Kahook
- Sue Anschutz-Rodgers Eye Center, The University of Colorado School of Medicine, Aurora, CO
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Alawad M, Aljouie A, Alamri S, Alghamdi M, Alabdulkader B, Alkanhal N, Almazroa A. Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation – A Review. Clin Ophthalmol 2022; 16:747-764. [PMID: 35300031 PMCID: PMC8923700 DOI: 10.2147/opth.s348479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 02/11/2022] [Indexed: 12/12/2022] Open
Abstract
Background Globally, glaucoma is the second leading cause of blindness. Detecting glaucoma in the early stages is essential to avoid disease complications, which lead to blindness. Thus, computer-aided diagnosis systems are powerful tools to overcome the shortage of glaucoma screening programs. Methods A systematic search of public databases, including PubMed, Google Scholar, and other sources, was performed to identify relevant studies to overview the publicly available fundus image datasets used to train, validate, and test machine learning and deep learning methods. Additionally, existing machine learning and deep learning methods for optic cup and disc segmentation were surveyed and critically reviewed. Results Eight fundus images datasets were publicly available with 15,445 images labeled with glaucoma or non-glaucoma, and manually annotated optic disc and cup boundaries were found. Five metrics were identified for evaluating the developed models. Finally, three main deep learning architectural designs were commonly used for optic disc and optic cup segmentation. Conclusion We provided future research directions to formulate robust optic cup and disc segmentation systems. Deep learning can be utilized in clinical settings for this task. However, many challenges need to be addressed before using this strategy in clinical trials. Finally, two deep learning architectural designs have been widely adopted, such as U-net and its variants.
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Affiliation(s)
- Mohammed Alawad
- Department of Biostatistics and Bioinformatics, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Abdulrhman Aljouie
- Department of Biostatistics and Bioinformatics, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Suhailah Alamri
- Department of Imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for health Sciences, Riyadh, Saudi Arabia
- Research Labs, National Center for Artificial Intelligence, Riyadh, Saudi Arabia
| | - Mansour Alghamdi
- Department of Optometry and Vision Sciences College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Balsam Alabdulkader
- Department of Optometry and Vision Sciences College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Norah Alkanhal
- Department of Imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for health Sciences, Riyadh, Saudi Arabia
| | - Ahmed Almazroa
- Department of Imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for health Sciences, Riyadh, Saudi Arabia
- Correspondence: Ahmed Almazroa; Abdulrhman Aljouie, Email ;
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40
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Tan Z, Zhu Z, He Z, He M. Artificial Intelligence in Ophthalmology. Artif Intell Med 2022. [DOI: 10.1007/978-981-19-1223-8_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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41
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Zhang YY, Zhao H, Lin JY, Wu SN, Liu XW, Zhang HD, Shao Y, Yang WF. Artificial Intelligence to Detect Meibomian Gland Dysfunction From in-vivo Laser Confocal Microscopy. Front Med (Lausanne) 2021; 8:774344. [PMID: 34901091 PMCID: PMC8655877 DOI: 10.3389/fmed.2021.774344] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Accepted: 11/04/2021] [Indexed: 02/05/2023] Open
Abstract
Background: In recent years, deep learning has been widely used in a variety of ophthalmic diseases. As a common ophthalmic disease, meibomian gland dysfunction (MGD) has a unique phenotype in in-vivo laser confocal microscope imaging (VLCMI). The purpose of our study was to investigate a deep learning algorithm to differentiate and classify obstructive MGD (OMGD), atrophic MGD (AMGD) and normal groups. Methods: In this study, a multi-layer deep convolution neural network (CNN) was trained using VLCMI from OMGD, AMGD and healthy subjects as verified by medical experts. The automatic differential diagnosis of OMGD, AMGD and healthy people was tested by comparing its image-based identification of each group with the medical expert diagnosis. The CNN was trained and validated with 4,985 and 1,663 VLCMI images, respectively. By using established enhancement techniques, 1,663 untrained VLCMI images were tested. Results: In this study, we included 2,766 healthy control VLCMIs, 2,744 from OMGD and 2,801 from AMGD. Of the three models, differential diagnostic accuracy of the DenseNet169 CNN was highest at over 97%. The sensitivity and specificity of the DenseNet169 model for OMGD were 88.8 and 95.4%, respectively; and for AMGD 89.4 and 98.4%, respectively. Conclusion: This study described a deep learning algorithm to automatically check and classify VLCMI images of MGD. By optimizing the algorithm, the classifier model displayed excellent accuracy. With further development, this model may become an effective tool for the differential diagnosis of MGD.
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Affiliation(s)
- Ye-Ye Zhang
- Department of Electronic Engineering, School of Science, Hainan University, Haikou, China.,Department of Electronic Engineering, College of Engineering, Shantou University, Shantou, China
| | - Hui Zhao
- Department of Ophthalmology, Shanghai First People's Hospital, Shanghai Jiao Tong University, National Clinical Research Center for Eye Diseases, Shanghai, China
| | - Jin-Yan Lin
- Research Center for Advanced Optics and Photoelectronics, Department of Physics, College of Science, Shantou University, Shantou, China
| | - Shi-Nan Wu
- Jiangxi Centre of National Ophthalmology Clinical Research Center, Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Xi-Wang Liu
- Research Center for Advanced Optics and Photoelectronics, Department of Physics, College of Science, Shantou University, Shantou, China.,Department of Mathematics, College of Science, Shantou University, Shantou, China
| | - Hong-Dan Zhang
- Research Center for Advanced Optics and Photoelectronics, Department of Physics, College of Science, Shantou University, Shantou, China.,Department of Mathematics, College of Science, Shantou University, Shantou, China
| | - Yi Shao
- Jiangxi Centre of National Ophthalmology Clinical Research Center, Department of Ophthalmology, The First Affiliated Hospital of Nanchang University, Nanchang, China
| | - Wei-Feng Yang
- Department of Electronic Engineering, College of Engineering, Shantou University, Shantou, China.,Research Center for Advanced Optics and Photoelectronics, Department of Physics, College of Science, Shantou University, Shantou, China.,Department of Mathematics, College of Science, Shantou University, Shantou, China
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42
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Wu Y, Szymanska M, Hu Y, Fazal MI, Jiang N, Yetisen AK, Cordeiro MF. Measures of disease activity in glaucoma. Biosens Bioelectron 2021; 196:113700. [PMID: 34653715 DOI: 10.1016/j.bios.2021.113700] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 10/01/2021] [Accepted: 10/08/2021] [Indexed: 12/13/2022]
Abstract
Glaucoma is the leading cause of irreversible blindness globally which significantly affects the quality of life and has a substantial economic impact. Effective detective methods are necessary to identify glaucoma as early as possible. Regular eye examinations are important for detecting the disease early and preventing deterioration of vision and quality of life. Current methods of measuring disease activity are powerful in describing the functional and structural changes in glaucomatous eyes. However, there is still a need for a novel tool to detect glaucoma earlier and more accurately. Tear fluid biomarker analysis and new imaging technology provide novel surrogate endpoints of glaucoma. Artificial intelligence is a post-diagnostic tool that can analyse ophthalmic test results. A detail review of currently used clinical tests in glaucoma include intraocular pressure test, visual field test and optical coherence tomography are presented. The advanced technologies for glaucoma measurement which can identify specific disease characteristics, as well as the mechanism, performance and future perspectives of these devices are highlighted. Applications of AI in diagnosis and prediction in glaucoma are mentioned. With the development in imaging tools, sensor technologies and artificial intelligence, diagnostic evaluation of glaucoma must assess more variables to facilitate earlier diagnosis and management in the future.
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Affiliation(s)
- Yue Wu
- Department of Surgery and Cancer, Imperial College London, South Kensington, London, United Kingdom; Department of Chemical Engineering, Imperial College London, South Kensington, London, United Kingdom
| | - Maja Szymanska
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, United Kingdom
| | - Yubing Hu
- Department of Chemical Engineering, Imperial College London, South Kensington, London, United Kingdom.
| | - M Ihsan Fazal
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, United Kingdom
| | - Nan Jiang
- West China School of Basic Medical Sciences & Forensic Medicine, Sichuan University, Chengdu, 610041, China
| | - Ali K Yetisen
- Department of Chemical Engineering, Imperial College London, South Kensington, London, United Kingdom
| | - M Francesca Cordeiro
- The Imperial College Ophthalmic Research Group (ICORG), Imperial College London, London, United Kingdom; The Western Eye Hospital, Imperial College Healthcare NHS Trust (ICHNT), London, United Kingdom; Glaucoma and Retinal Neurodegeneration Group, Department of Visual Neuroscience, UCL Institute of Ophthalmology, London, United Kingdom.
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43
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Nikolaidou A, Tsaousis KT. Teleophthalmology and Artificial Intelligence As Game Changers in Ophthalmic Care After the COVID-19 Pandemic. Cureus 2021; 13:e16392. [PMID: 34408945 PMCID: PMC8363234 DOI: 10.7759/cureus.16392] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/13/2021] [Indexed: 12/17/2022] Open
Abstract
The current COVID-19 pandemic has boosted a sudden demand for telemedicine due to quarantine and travel restrictions. The exponential increase in the use of telemedicine is expected to affect ophthalmology drastically. The aim of this review is to discuss the utility, effectiveness and challenges of teleophthalmological new tools for eyecare delivery as well as its implementation and possible facilitation with artificial intelligence. We used the terms: “teleophthalmology,” “telemedicine and COVID-19,” “retinal diseases and telemedicine,” “virtual ophthalmology,” “cost effectiveness of teleophthalmology,” “pediatric teleophthalmology,” “Artificial intelligence and ophthalmology,” “Glaucoma and teleophthalmology” and “teleophthalmology limitations” in the database of PubMed and selected the articles being published in the course of 2015-2020. After the initial search, 321 articles returned as relevant. A meticulous screening followed and eventually 103 published manuscripts were included and used as our references. Emerging in the market, teleophthalmology is showing great potential for the future of ophthalmological care, benefiting both patients and ophthalmologists in times of pandemics. The spectrum of eye diseases that could benefit from teleophthalmology is wide, including mostly retinal diseases such as diabetic retinopathy, retinopathy of prematurity, age-related macular degeneration but also glaucoma and anterior segment conditions. Simultaneously, artificial intelligence provides ways of implementing teleophthalmology easier and with better outcomes, contributing as significant changing factors for ophthalmology practice after the COVID-19 pandemic.
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Affiliation(s)
- Anna Nikolaidou
- Ophthalmology, Aristotle University of Thessaloniki, Thessaloniki, GRC
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Dagi TF, Barker FG, Glass J. Machine Learning and Artificial Intelligence in Neurosurgery: Status, Prospects, and Challenges. Neurosurgery 2021; 89:133-142. [PMID: 34015816 DOI: 10.1093/neuros/nyab170] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Affiliation(s)
- T Forcht Dagi
- Queen's University Belfast and The William J. Clinton Leadership Institute, Belfast, UK
- Mayo Clinic College of Medicine and Science, Rochester, Minnesota, USA
| | - Fred G Barker
- Department of Neurosurgery, Harvard Medical School, Boston, Massachusetts, USA
- The Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Jacob Glass
- Center for Epigenetics Research, Memorial Sloan Kettering Cancer Center, New York, New York, USA
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45
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Lee EB, Wang SY, Chang RT. Interpreting Deep Learning Studies in Glaucoma: Unresolved Challenges. Asia Pac J Ophthalmol (Phila) 2021; 10:261-267. [PMID: 34383718 DOI: 10.1097/apo.0000000000000395] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
ABSTRACT Deep learning algorithms as tools for automated image classification have recently experienced rapid growth in imaging-dependent medical specialties, including ophthalmology. However, only a few algorithms tailored to specific health conditions have been able to achieve regulatory approval for autonomous diagnosis. There is now an international effort to establish optimized thresholds for algorithm performance benchmarking in a rapidly evolving artificial intelligence field. This review examines the largest deep learning studies in glaucoma, with special focus on identifying recurrent challenges and limitations within these studies which preclude widespread clinical deployment. We focus on the 3 most common input modalities when diagnosing glaucoma, namely, fundus photographs, spectral domain optical coherence tomography scans, and standard automated perimetry data. We then analyze 3 major challenges present in all studies: defining the algorithm output of glaucoma, determining reliable ground truth datasets, and compiling representative training datasets.
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Affiliation(s)
- Eric Boya Lee
- Byers Eye Institute, Department of Ophthalmology, Stanford University, CA
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46
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Saifee M, Wu J, Liu Y, Ma P, Patlidanon J, Yu Y, Ying GS, Han Y. Development and Validation of Automated Visual Field Report Extraction Platform Using Computer Vision Tools. Front Med (Lausanne) 2021; 8:625487. [PMID: 33996848 PMCID: PMC8116600 DOI: 10.3389/fmed.2021.625487] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2020] [Accepted: 03/31/2021] [Indexed: 02/01/2023] Open
Abstract
Purpose: To introduce and validate hvf_extraction_script, an open-source software script for the automated extraction and structuring of metadata, value plot data, and percentile plot data from Humphrey visual field (HVF) report images. Methods: Validation was performed on 90 HVF reports over three different report layouts, including a total of 1,530 metadata fields, 15,536 value plot data points, and 10,210 percentile data points, between the computer script and four human extractors, compared against DICOM reference data. Computer extraction and human extraction were compared on extraction time as well as accuracy of extraction for metadata, value plot data, and percentile plot data. Results: Computer extraction required 4.9-8.9 s per report, compared to the 6.5-19 min required by human extractors, representing a more than 40-fold difference in extraction speed. Computer metadata extraction error rate varied from an aggregate 1.2-3.5%, compared to 0.2-9.2% for human metadata extraction across all layouts. Computer value data point extraction had an aggregate error rate of 0.9% for version 1, <0.01% in version 2, and 0.15% in version 3, compared to 0.8-9.2% aggregate error rate for human extraction. Computer percentile data point extraction similarly had very low error rates, with no errors occurring in version 1 and 2, and 0.06% error rate in version 3, compared to 0.06-12.2% error rate for human extraction. Conclusions: This study introduces and validates hvf_extraction_script, an open-source tool for fast, accurate, automated data extraction of HVF reports to facilitate analysis of large-volume HVF datasets, and demonstrates the value of image processing tools in facilitating faster and cheaper large-volume data extraction in research settings.
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Affiliation(s)
- Murtaza Saifee
- Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States
| | - Jian Wu
- Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States.,Beijing Ophthalmology and Visual Science Key Lab, Beijing Tongren Eye Center, Beijing Tongren Hospital, Beijing Institute of Ophthalmology, Capital Medical University, Beijing, China
| | - Yingna Liu
- Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States
| | - Ping Ma
- Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States.,Department of Ophthalmology, Shandong Provincial Hospital, Shandong First Medical University, Jinan, China
| | - Jutima Patlidanon
- Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States.,Department of Ophthalmology, Bhumibol Adulyadej Hospital, Bangkok, Thailand
| | - Yinxi Yu
- Center for Preventive Ophthalmology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Gui-Shuang Ying
- Center for Preventive Ophthalmology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Ying Han
- Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States.,Ophthalmology Section, Surgical Service, San Francisco Veterans Affairs Medical Center, San Francisco, CA, United States
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47
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Foo LL, Ang M, Wong CW, Ohno-Matsui K, Saw SM, Wong TY, Ting DS. Is artificial intelligence a solution to the myopia pandemic? Br J Ophthalmol 2021; 105:741-744. [PMID: 33712483 DOI: 10.1136/bjophthalmol-2021-319129] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Affiliation(s)
- Li Lian Foo
- Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS, Singapore.,Singapore Eye Research Institute, Singapore
| | - Marcus Ang
- Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS, Singapore.,Singapore Eye Research Institute, Singapore
| | - Chee Wai Wong
- Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS, Singapore.,Singapore Eye Research Institute, Singapore
| | - Kyoko Ohno-Matsui
- Ophthalmology and Visual Science, Tokyo Medical and Dental University, Bunkyo-ku, Japan
| | | | - Tien Yin Wong
- Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Department, Duke-NUS, Singapore.,Singapore Eye Research Institute, Singapore
| | - Daniel S Ting
- Singapore National Eye Centre, Singapore .,Ophthalmology and Visual Sciences Department, Duke-NUS, Singapore.,Singapore Eye Research Institute, Singapore
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