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Riina N, Harris A, Siesky BA, Ritzer L, Pasquale LR, Tsai JC, Keller J, Wirostko B, Arciero J, Fry B, Eckert G, Verticchio Vercellin A, Antman G, Sidoti PA, Guidoboni G. Using Multi-Layer Perceptron Driven Diagnosis to Compare Biomarkers for Primary Open Angle Glaucoma. Invest Ophthalmol Vis Sci 2024; 65:16. [PMID: 39250119 PMCID: PMC11385878 DOI: 10.1167/iovs.65.11.16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2024] Open
Abstract
Purpose To use neural network machine learning (ML) models to identify the most relevant ocular biomarkers for the diagnosis of primary open-angle glaucoma (POAG). Methods Neural network models, also known as multi-layer perceptrons (MLPs), were trained on a prospectively collected observational dataset comprised of 93 glaucoma patients confirmed by a glaucoma specialist and 113 control subjects. The base model used only intraocular pressure, blood pressure, heart rate, and visual field (VF) parameters to diagnose glaucoma. The following models were given the base parameters in addition to one of the following biomarkers: structural features (optic nerve parameters, retinal nerve fiber layer [RNFL], ganglion cell complex [GCC] and macular thickness), choroidal thickness, and RNFL and GCC thickness only, by optical coherence tomography (OCT); and vascular features by OCT angiography (OCTA). Results MLPs of three different structures were evaluated with tenfold cross validation. The testing area under the receiver operating characteristic curve (AUC) of the models were compared with independent samples t-tests. The vascular and structural models both had significantly higher accuracies than the base model, with the hemodynamic AUC (0.819) insignificantly outperforming the structural set AUC (0.816). The GCC + RNFL model and the model containing all structural and vascular features were also significantly more accurate than the base model. Conclusions Neural network models indicate that OCTA optic nerve head vascular biomarkers are equally useful for ML diagnosis of POAG when compared to OCT structural biomarker features alone.
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Affiliation(s)
- Nicholas Riina
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Alon Harris
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Brent A Siesky
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Lukas Ritzer
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - Louis R Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
| | - James C Tsai
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
- New York Eye and Ear Infirmary of Mount Sinai, New York, New York, United States
| | - James Keller
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, Missouri, United States
| | - Barbara Wirostko
- University of Utah Health John A Moran Eye Center, Salt Lake City, Utah, United States
| | - Julia Arciero
- Department of Mathematical Sciences, IUPUI School of Science, Indianapolis, Indiana, United States
| | - Brendan Fry
- Department of Mathematics and Statistics, Metropolitan State University of Denver, Denver, Colorado, United States
| | - George Eckert
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, Indiana, United States
| | | | - Gal Antman
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
- Department of Ophthalmology, Rabin Medical Center, Petah Tikva, Central, Israel
- Faculty of Medicine, Tel Aviv University, Israel
| | - Paul A Sidoti
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York, New York, United States
- New York Eye and Ear Infirmary of Mount Sinai, New York, New York, United States
| | - Giovanna Guidoboni
- Maine College of Engineering and Computing, The University of Maine, Orono, Maine, United States
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Olyntho MAC, Jorge CAC, Castanha EB, Gonçalves AN, Silva BL, Nogueira BV, Lima GM, Gracitelli CPB, Tatham AJ. Artificial Intelligence in Anterior Chamber Evaluation: A Systematic Review and Meta-Analysis. J Glaucoma 2024; 33:658-664. [PMID: 38747721 DOI: 10.1097/ijg.0000000000002428] [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: 08/22/2023] [Accepted: 05/02/2024] [Indexed: 08/30/2024]
Abstract
PRCIS In this meta-analysis of 6 studies and 5269 patients, deep learning algorithms applied to AS-OCT demonstrated excellent diagnostic performance for closed angle compared with gonioscopy, with a pooled sensitivity and specificity of 94% and 93.6%, respectively. PURPOSE This study aimed to review the literature and compare the accuracy of deep learning algorithms (DLA) applied to anterior segment optical coherence tomography images (AS-OCT) against gonioscopy in detecting angle closure in patients with glaucoma. METHODS We performed a systematic review and meta-analysis evaluating DLA in AS-OCT images for the diagnosis of angle closure compared with gonioscopic evaluation. PubMed, Scopus, Embase, Lilacs, Scielo, and Cochrane Central Register of Controlled Trials were searched. The bivariate model was used to calculate pooled sensitivity and specificity. RESULTS The initial search identified 214 studies, of which 6 were included for final analysis. The total study population included 5269 patients. The combined sensitivity of the DLA compared with gonioscopy was 94.0% (95% CI: 83.8%-97.9%), whereas the pooled specificity was 93.6% (95% CI: 85.7%-97.3%). Sensitivity analyses removing each individual study showed a pooled sensitivity in the range of 90.1%-95.1%. Similarly, specificity results ranged from 90.3% to 94.5% with the removal of each individual study and recalculation of pooled specificity. CONCLUSION DLA applied to AS-OCT has excellent sensitivity and specificity in the identification of angle closure. This technology may be a valuable resource in the screening of populations without access to experienced ophthalmologists who perform gonioscopy.
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Affiliation(s)
| | - Carlos A C Jorge
- Department of Medicine, Federal University of Mato Grosso, Cuiabá-MT
| | | | - Andreia N Gonçalves
- Department of Technological Science, Virtual University of the State of São Paulo
| | | | | | - Geovana M Lima
- Department of Medicine, University of Gurupi, Gurupi-TO, Brazil
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Carlà MM, Gambini G, Baldascino A, Boselli F, Giannuzzi F, Margollicci F, Rizzo S. Large language models as assistance for glaucoma surgical cases: a ChatGPT vs. Google Gemini comparison. Graefes Arch Clin Exp Ophthalmol 2024; 262:2945-2959. [PMID: 38573349 PMCID: PMC11377518 DOI: 10.1007/s00417-024-06470-5] [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: 01/22/2024] [Revised: 03/11/2024] [Accepted: 03/20/2024] [Indexed: 04/05/2024] Open
Abstract
PURPOSE The aim of this study was to define the capability of ChatGPT-4 and Google Gemini in analyzing detailed glaucoma case descriptions and suggesting an accurate surgical plan. METHODS Retrospective analysis of 60 medical records of surgical glaucoma was divided into "ordinary" (n = 40) and "challenging" (n = 20) scenarios. Case descriptions were entered into ChatGPT and Bard's interfaces with the question "What kind of surgery would you perform?" and repeated three times to analyze the answers' consistency. After collecting the answers, we assessed the level of agreement with the unified opinion of three glaucoma surgeons. Moreover, we graded the quality of the responses with scores from 1 (poor quality) to 5 (excellent quality), according to the Global Quality Score (GQS) and compared the results. RESULTS ChatGPT surgical choice was consistent with those of glaucoma specialists in 35/60 cases (58%), compared to 19/60 (32%) of Gemini (p = 0.0001). Gemini was not able to complete the task in 16 cases (27%). Trabeculectomy was the most frequent choice for both chatbots (53% and 50% for ChatGPT and Gemini, respectively). In "challenging" cases, ChatGPT agreed with specialists in 9/20 choices (45%), outperforming Google Gemini performances (4/20, 20%). Overall, GQS scores were 3.5 ± 1.2 and 2.1 ± 1.5 for ChatGPT and Gemini (p = 0.002). This difference was even more marked if focusing only on "challenging" cases (1.5 ± 1.4 vs. 3.0 ± 1.5, p = 0.001). CONCLUSION ChatGPT-4 showed a good analysis performance for glaucoma surgical cases, either ordinary or challenging. On the other side, Google Gemini showed strong limitations in this setting, presenting high rates of unprecise or missed answers.
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Affiliation(s)
- Matteo Mario Carlà
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy.
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy.
| | - Gloria Gambini
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy
| | - Antonio Baldascino
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy
| | - Francesco Boselli
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy
| | - Federico Giannuzzi
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy
| | - Fabio Margollicci
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy
| | - Stanislao Rizzo
- Ophthalmology Department, Fondazione Policlinico Universitario A. Gemelli, IRCCS, 00168, Rome, Italy
- Ophthalmology Department, Catholic University "Sacro Cuore,", Largo A. Gemelli, 8, Rome, Italy
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Li F, Wang D, Yang Z, Zhang Y, Jiang J, Liu X, Kong K, Zhou F, Tham CC, Medeiros F, Han Y, Grzybowski A, Zangwill LM, Lam DSC, Zhang X. The AI revolution in glaucoma: Bridging challenges with opportunities. Prog Retin Eye Res 2024; 103:101291. [PMID: 39186968 DOI: 10.1016/j.preteyeres.2024.101291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/19/2024] [Accepted: 08/19/2024] [Indexed: 08/28/2024]
Abstract
Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the "black box" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.
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Affiliation(s)
- Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Deming Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Zefeng Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Yinhang Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Jiaxuan Jiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Xiaoyi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Kangjie Kong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Fengqi Zhou
- Ophthalmology, Mayo Clinic Health System, Eau Claire, WI, USA.
| | - Clement C Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Felipe Medeiros
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA.
| | - Ying Han
- University of California, San Francisco, Department of Ophthalmology, San Francisco, CA, USA; The Francis I. Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, CA, USA.
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
| | - Linda M Zangwill
- Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, CA, USA.
| | - Dennis S C Lam
- The International Eye Research Institute of the Chinese University of Hong Kong (Shenzhen), Shenzhen, China; The C-MER Dennis Lam & Partners Eye Center, C-MER International Eye Care Group, Hong Kong, China.
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
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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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Deng J, Qin Y. Current Status, Hotspots, and Prospects of Artificial Intelligence in Ophthalmology: A Bibliometric Analysis (2003-2023). Ophthalmic Epidemiol 2024:1-14. [PMID: 39146462 DOI: 10.1080/09286586.2024.2373956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2024] [Revised: 06/01/2024] [Accepted: 06/18/2024] [Indexed: 08/17/2024]
Abstract
PURPOSE Artificial intelligence (AI) has gained significant attention in ophthalmology. This paper reviews, classifies, and summarizes the research literature in this field and aims to provide readers with a detailed understanding of the current status and future directions, laying a solid foundation for further research and decision-making. METHODS Literature was retrieved from the Web of Science database. Bibliometric analysis was performed using VOSviewer, CiteSpace, and the R package Bibliometrix. RESULTS The study included 3,377 publications from 4,035 institutions in 98 countries. China and the United States had the most publications. Sun Yat-sen University is a leading institution. Translational Vision Science & Technology"published the most articles, while "Ophthalmology" had the most co-citations. Among 13,145 researchers, Ting DSW had the most publications and citations. Keywords included "Deep learning," "Diabetic retinopathy," "Machine learning," and others. CONCLUSION The study highlights the promising prospects of AI in ophthalmology. Automated eye disease screening, particularly its core technology of retinal image segmentation and recognition, has become a research hotspot. AI is also expanding to complex areas like surgical assistance, predictive models. Multimodal AI, Generative Adversarial Networks, and ChatGPT have driven further technological innovation. However, implementing AI in ophthalmology also faces many challenges, including technical, regulatory, and ethical issues, and others. As these challenges are overcome, we anticipate more innovative applications, paving the way for more effective and safer eye disease treatments.
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Affiliation(s)
- Jie Deng
- First Clinical College of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China
- Graduate School, Hunan University of Chinese Medicine, Changsha, Hunan, China
| | - YuHui Qin
- First Clinical College of Traditional Chinese Medicine, Hunan University of Chinese Medicine, Changsha, Hunan, China
- Graduate School, Hunan University of Chinese Medicine, Changsha, Hunan, China
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Christopher M, Hallaj S, Jiravarnsirikul A, Baxter SL, Zangwill LM. Novel Technologies in Artificial Intelligence and Telemedicine for Glaucoma Screening. J Glaucoma 2024; 33:S26-S32. [PMID: 38506792 DOI: 10.1097/ijg.0000000000002367] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Accepted: 01/22/2024] [Indexed: 03/21/2024]
Abstract
PURPOSE To provide an overview of novel technologies in telemedicine and artificial intelligence (AI) approaches for cost-effective glaucoma screening. METHODS/RESULTS A narrative review was performed by summarizing research results, recent developments in glaucoma detection and care, and considerations related to telemedicine and AI in glaucoma screening. Telemedicine and AI approaches provide the opportunity for novel glaucoma screening programs in primary care, optometry, portable, and home-based settings. These approaches offer several advantages for glaucoma screening, including increasing access to care, lowering costs, identifying patients in need of urgent treatment, and enabling timely diagnosis and early intervention. However, challenges remain in implementing these systems, including integration into existing clinical workflows, ensuring equity for patients, and meeting ethical and regulatory requirements. Leveraging recent work towards standardized data acquisition as well as tools and techniques developed for automated diabetic retinopathy screening programs may provide a model for a cost-effective approach to glaucoma screening. CONCLUSION Leveraging novel technologies and advances in telemedicine and AI-based approaches to glaucoma detection show promise for improving our ability to detect moderate and advanced glaucoma in primary care settings and target higher individuals at high risk for having the disease.
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Affiliation(s)
- Mark Christopher
- Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center
- Viterbi Family Department of Ophthalmology, Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute
| | - Shahin Hallaj
- Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center
- Viterbi Family Department of Ophthalmology, Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute
| | - Anuwat Jiravarnsirikul
- Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center
- Department of Medicine, Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Sally L Baxter
- Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center
- Viterbi Family Department of Ophthalmology, Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute
- Department of Medicine, Division of Biomedical Informatics, University of California San Diego, La Jolla, CA
| | - Linda M Zangwill
- Viterbi Family Department of Ophthalmology, Hamilton Glaucoma Center
- Viterbi Family Department of Ophthalmology, Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute
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Jan C, He M, Vingrys A, Zhu Z, Stafford RS. Diagnosing glaucoma in primary eye care and the role of Artificial Intelligence applications for reducing the prevalence of undetected glaucoma in Australia. Eye (Lond) 2024; 38:2003-2013. [PMID: 38514852 PMCID: PMC11269618 DOI: 10.1038/s41433-024-03026-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 02/05/2024] [Accepted: 03/08/2024] [Indexed: 03/23/2024] Open
Abstract
Glaucoma is the commonest cause of irreversible blindness worldwide, with over 70% of people affected remaining undiagnosed. Early detection is crucial for halting progressive visual impairment in glaucoma patients, as there is no cure available. This narrative review aims to: identify reasons for the significant under-diagnosis of glaucoma globally, particularly in Australia, elucidate the role of primary healthcare in glaucoma diagnosis using Australian healthcare as an example, and discuss how recent advances in artificial intelligence (AI) can be implemented to improve diagnostic outcomes. Glaucoma is a prevalent disease in ageing populations and can have improved visual outcomes through appropriate treatment, making it essential for general medical practice. In countries such as Australia, New Zealand, Canada, USA, and the UK, optometrists serve as the gatekeepers for primary eye care, and glaucoma detection often falls on their shoulders. However, there is significant variation in the capacity for glaucoma diagnosis among eye professionals. Automation with Artificial Intelligence (AI) analysis of optic nerve photos can help optometrists identify high-risk changes and mitigate the challenges of image interpretation rapidly and consistently. Despite its potential, there are significant barriers and challenges to address before AI can be deployed in primary healthcare settings, including external validation, high quality real-world implementation, protection of privacy and cybersecurity, and medico-legal implications. Overall, the incorporation of AI technology in primary healthcare has the potential to reduce the global prevalence of undiagnosed glaucoma cases by improving diagnostic accuracy and efficiency.
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Affiliation(s)
- Catherine Jan
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia.
- Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia.
- Lost Child's Vision Project, Sydney, NSW, Australia.
| | - Mingguang He
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia
- Centre for Eye and Vision Research, The Hong Kong Polytechnic University, Kowloon, TU428, Hong Kong SAR
| | - Algis Vingrys
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia
- Department of Optometry and Vision Sciences, The University of Melbourne, Melbourne, VIC, Australia
| | - Zhuoting Zhu
- Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia
- Ophthalmology, Department of Surgery, Faculty of Medicine, Dentistry & Health Sciences, University of Melbourne, Melbourne, VIC, Australia
| | - Randall S Stafford
- Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, CA, USA
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Founti P, Stuart K, Nolan WP, Khawaja AP, Foster PJ. Screening Strategies and Methodologies. J Glaucoma 2024; 33:S15-S20. [PMID: 39149948 DOI: 10.1097/ijg.0000000000002426] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Accepted: 05/02/2024] [Indexed: 08/17/2024]
Abstract
PRCIS While glaucoma is a leading cause of irreversible vision loss, it presents technical challenges in the design and implementation of screening. New technologies such as PRS and AI offer potential improvements in our ability to identify people at high risk of sight loss from glaucoma and may improve the viability of screening for this important disease. PURPOSE To review the current evidence and concepts around screening for glaucoma. METHODS/RESULTS A group of glaucoma-focused clinician scientists drew on knowledge and experience around glaucoma, its etiology, and the options for screening. Glaucoma is a chronic progressive optic neuropathy affecting around 76 million individuals worldwide and is the leading cause of irreversible blindness globally. Early stages of the disease are asymptomatic meaning a substantial proportion of cases remain undiagnosed. Early detection and timely intervention reduce the risk of glaucoma-related visual morbidity. However, imperfect tests and a relatively low prevalence currently limit the viability of population-based screening approaches. The diagnostic yield of opportunistic screening strategies, relying on the identification of disease during unrelated health care encounters, such as cataract clinics and diabetic retinopathy screening programs, focusing on older people and/or those with a family history, are hindered by a large number of false-positive and false-negative results. Polygenic risk scores (PRS) offer personalized risk assessment for adult-onset glaucoma. In addition, artificial intelligence (AI) algorithms have shown impressive performance, comparable to expert humans, in discriminating between potentially glaucomatous and non-glaucomatous eyes. These emerging technologies may offer a meaningful improvement in diagnostic yield in glaucoma screening. CONCLUSIONS While glaucoma is a leading cause of irreversible vision loss, it presents technical challenges in the design and implementation of screening. New technologies such as PRS and AI offer potential improvements in our ability to identify people at high risk of sight loss from glaucoma and may improve the viability of screening for this important disease.
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Affiliation(s)
| | - Kelsey Stuart
- Ocular Informatics Group, Population and Data Sciences Research Theme, University College London Institute of Ophthalmology
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology
| | - Winifred P Nolan
- Glaucoma Service, Moorfields Eye Hospital NHS Foundation Trust
- International Centre for Eye Health, London School of Hygiene and Tropical Medicine, London, UK
| | - Anthony P Khawaja
- Glaucoma Service, Moorfields Eye Hospital NHS Foundation Trust
- Ocular Informatics Group, Population and Data Sciences Research Theme, University College London Institute of Ophthalmology
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology
| | - Paul J Foster
- Glaucoma Service, Moorfields Eye Hospital NHS Foundation Trust
- Ocular Informatics Group, Population and Data Sciences Research Theme, University College London Institute of Ophthalmology
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology
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Beniz LAF, Campos VP, Medeiros FA. Optical Coherence Tomography Versus Optic Disc Photo Assessment in Glaucoma Screening. J Glaucoma 2024; 33:S21-S25. [PMID: 38546240 DOI: 10.1097/ijg.0000000000002392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 03/17/2024] [Indexed: 04/18/2024]
Abstract
PRCIS Optical coherence tomography (OCT) and optic disc photography present valuable but distinct capabilities for glaucoma screening. OBJECTIVE This review article examines the strengths and limitations of OCT and optic disc photography in glaucoma screening. METHODS A comprehensive literature review was conducted, focusing on the accuracy, feasibility, cost-effectiveness, and technological advancements in OCT and optic disc photography for glaucoma screening. RESULTS OCT is highly accurate and reproducible but faces limitations due to its cost and less portable nature, making widespread screening challenging. In contrast, optic disc photos are more accessible and cost-effective but are hindered by subjective interpretation and inconsistent grading reliability. A critical challenge in glaucoma screening is achieving a high PPV, particularly given the low prevalence of the disease, which can lead to a significant number of false positives. The advent of artificial intelligence (AI) and deep learning models shows potential in improving the diagnostic accuracy of optic disc photos by automating the detection of glaucomatous optic neuropathy and reducing subjectivity. However, the effectiveness of these AI models hinges on the quality of training data. Using subjective gradings as training data, will carry the limitations of human assessment into the AI system, leading to potential inaccuracies. Conversely, training AI models using objective data from OCT, such as retinal nerve fiber layer thickness, may offer a promising direction. CONCLUSION Both OCT and optic disc photography present valuable but distinct capabilities for glaucoma screening. An approach integrating AI technology might be key in optimizing these methods for effective, large-scale screening programs.
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Affiliation(s)
- Luiz Arthur F Beniz
- Department of Ophthalmology, Bascom Palmer Eye Institute, Miller School of Medicine, University of Miami, Miami, FL
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11
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Xu Y, Liu H, Sun R, Wang H, Huo Y, Wang N, Hu M. Deep learning for predicting circular retinal nerve fiber layer thickness from fundus photographs and diagnosing glaucoma. Heliyon 2024; 10:e33813. [PMID: 39040392 PMCID: PMC11261845 DOI: 10.1016/j.heliyon.2024.e33813] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2024] [Revised: 06/27/2024] [Accepted: 06/27/2024] [Indexed: 07/24/2024] Open
Abstract
Purpose This study aimed to propose a new deep learning (DL) approach to automatically predict the retinal nerve fiber layer thickness (RNFLT) around optic disc regions in fundus photography trained by optical coherence tomography (OCT) and diagnose glaucoma based on the predicted comprehensive information about RNFLT. Methods A total of 1403 pairs of fundus photographs and OCT RNFLT scans from 1403 eyes of 1196 participants were included. A residual deep neural network was trained to predict the RNFLT for each local image in a fundus photograph, and then a RNFLT report was generated based on the local images. Two indicators were designed based on the generated report. The support vector machines (SVM) algorithm was used to diagnose glaucoma based on the two indicators. Results A strong correlation was found between the predicted and actual RNFLT values on local images. On three testing datasets, we found the Pearson r to be 0.893, 0.850, and 0.831, respectively, and the mean absolute error of the prediction to be 14.345, 17.780, and 19.250 μm, respectively. The area under the receiver operating characteristic curves for discriminating glaucomatous from healthy eyes was 0.860 (95 % confidence interval, 0.799-0.921). Conclusions We established a novel local image-based DL approach to provide comprehensive quantitative information on RNFLT in fundus photographs, which was used to diagnose glaucoma. In addition, training a deep neural network based on local images to predict objective detail information in fundus photographs provided a new paradigm for the diagnosis of ophthalmic diseases.
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Affiliation(s)
- Yongli Xu
- College of Statistics and Data Science, Beijing University of Technology, Beijing, China
- Department of Mathematics, Beijing University of Chemical Technology, Beijing, China
| | - Hanruo Liu
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, China
- School of Information and Electronics, Beijing Institute of Technology, Beijing, China
| | - Run Sun
- Department of Mathematics, Beijing University of Chemical Technology, Beijing, China
| | - Huaizhou Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, China
| | - Yanjiao Huo
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, China
| | - Ningli Wang
- Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology & Visual Science Key Lab, Beijing, China
- Beijing Advanced Innovation Center for Big Data-Based Precision Medicine, Beihang University & Capital Medical University, Beijing Tongren Hospital, Beijing, China
| | - Man Hu
- Department of Ophthalmology, Beijing Children's Hospital, Capital Medical University, National Center for Children's Health, Beijing, China
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12
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Mohammadzadeh V, Wu S, Besharati S, Davis T, Vepa A, Morales E, Edalati K, Rafiee M, Martinyan A, Zhang D, Scalzo F, Caprioli J, Nouri-Mahdavi K. Prediction of Visual Field Progression with Baseline and Longitudinal Structural Measurements Using Deep Learning. Am J Ophthalmol 2024; 262:141-152. [PMID: 38354971 PMCID: PMC11226195 DOI: 10.1016/j.ajo.2024.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 02/03/2024] [Accepted: 02/05/2024] [Indexed: 02/16/2024]
Abstract
PURPOSE Identifying glaucoma patients at high risk of progression based on widely available structural data is an unmet task in clinical practice. We test the hypothesis that baseline or serial structural measures can predict visual field (VF) progression with deep learning (DL). DESIGN Development of a DL algorithm to predict VF progression. METHODS 3,079 eyes (1,765 patients) with various types of glaucoma and ≥5 VFs, and ≥3 years of follow-up from a tertiary academic center were included. Serial VF mean deviation (MD) rates of change were estimated with linear-regression. VF progression was defined as negative MD slope with p<0.05. A Siamese Neural Network with ResNet-152 backbone pre-trained on ImageNet was designed to predict VF progression using serial optic-disc photographs (ODP), and baseline retinal nerve fiber layer (RNFL) thickness. We tested the model on a separate dataset (427 eyes) with RNFL data from different OCT. The Main Outcome Measure was Area under ROC curve (AUC). RESULTS Baseline average (SD) MD was 3.4 (4.9)dB. VF progression was detected in 900 eyes (29%). AUC (95% CI) for model incorporating baseline ODP and RNFL thickness was 0.813 (0.757-0.869). After adding the second and third ODPs, AUC increased to 0.860 and 0.894, respectively (p<0.027). This model also had highest AUC (0.911) for predicting fast progression (MD rate <1.0 dB/year). Model's performance was similar when applied to second dataset using RNFL data from another OCT device (AUC=0.893; 0.837-0.948). CONCLUSIONS DL model predicted VF progression with clinically relevant accuracy using baseline RNFL thickness and serial ODPs and can be implemented as a clinical tool after further validation.
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Affiliation(s)
- Vahid Mohammadzadeh
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Sean Wu
- Department of Computer Science, Pepperdine University (S.W., F.S.), Malibu, California, USA
| | - Sajad Besharati
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Tyler Davis
- Department of Computer Science, University of California Los Angeles (T.D., A.V., F.S.), Los Angeles, California, USA
| | - Arvind Vepa
- Department of Computer Science, University of California Los Angeles (T.D., A.V., F.S.), Los Angeles, California, USA
| | - Esteban Morales
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Kiumars Edalati
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Mahshad Rafiee
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Arthur Martinyan
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - David Zhang
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Fabien Scalzo
- Department of Computer Science, Pepperdine University (S.W., F.S.), Malibu, California, USA; Department of Computer Science, University of California Los Angeles (T.D., A.V., F.S.), Los Angeles, California, USA
| | - Joseph Caprioli
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA
| | - Kouros Nouri-Mahdavi
- From the Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles (V.M., S.B., E.M., K.E., M.R., A.M., D.Z., J.C., K.N.-M.), Los Angeles, California, USA.
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Lozano AC, Serrano A, Salazar D, Rincón JV, Pardo Bayona M. Telemedicine for Screening and Follow-Up of Glaucoma: A Descriptive Study. Telemed J E Health 2024; 30:1901-1908. [PMID: 38662524 DOI: 10.1089/tmj.2023.0676] [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] [Indexed: 07/20/2024] Open
Abstract
Introduction: Glaucoma is a leading cause of irreversible blindness. It is a prevalent disease worldwide, affecting ∼70 million people and expected to reach up to 112 million by 2040. Purpose: The aim of this study is to describe the implementation and initial experience of a telemedicine program to monitor glaucoma and glaucoma suspect patients in a large, integrated health care system during the COVID-19 pandemic. Methods: A retrospective chart review of established glaucoma or glaucoma suspect patients who participated in a telemedicine evaluation at the ophthalmic center of a large, Colombian health care system between June 2020 and April 2023 was conducted. Clinical and sociodemographic variables were analyzed. Generated clinical orders for additional testing, surgical procedures, follow-ups, and referrals, as well as changes in medical treatment, were evaluated. Results: A total of 11,034 telemedicine consults were included. The mean ± standard deviation age of this group was 63 ± 17.2 years and 67% were female. Of the patients who attended teleconsults, 49% were glaucoma suspects and 38.5% were followed with a diagnosis of open-angle glaucoma. After the consult, 25% of patients were referred to a glaucoma specialist, 40% had additional testing ordered, and 8% had a surgical procedure ordered, mainly laser iridotomy (409 cases). Almost a third of patients returned for subsequent telemedicine visits after the initial encounter. Despite some technical difficulties, 99.8% of patients attended and completed their scheduled telemedicine appointments. Conclusions: A telemedicine program aimed to monitor established glaucoma patients can be successfully implemented. Established patients within an integrated health care system have high adherence to the virtual model. Further research by health care institutions and government agencies will be key to expand coverage to additional populations. Clinical Trial Registration Number: CEIFUS 1026-24.
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Affiliation(s)
- Andrea Caycedo Lozano
- Oftalmosanitas-Clínica Colsanitas (Colsanitas Clinic), Bogotá, Colombia
- Member of Vision Colombia Research Group, Bogota, Colombia
| | - Alejandro Serrano
- Clínica de Oftalmología San Diego (San Diego Ophthalmology Clinic), Medellín, Colombia
| | - Diana Salazar
- Ophthalmic Private Practice, Falls Church, Virginia, USA
| | - Juliana Vanessa Rincón
- Member of Vision Colombia Research Group, Bogota, Colombia
- Research Unit, Fundación Universitaria Sanitas (Unisanitas University), Bogotá, Colombia
| | - Mónica Pardo Bayona
- Member of Vision Colombia Research Group, Bogota, Colombia
- Research Unit, Fundación Universitaria Sanitas (Unisanitas University), Bogotá, Colombia
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Johnson NA, Gupta P, Lee T, Hadziahmetovic M, Rosdahl JA. The Use of Optical Coherence Tomography for Early Glaucoma Screening in a Population of Patients with Diabetes. Ophthalmic Epidemiol 2024; 31:145-151. [PMID: 37198948 DOI: 10.1080/09286586.2023.2214929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 05/14/2023] [Indexed: 05/19/2023]
Abstract
PURPOSE The utility of screening for early diagnosis of glaucoma remains a widely debated topic in the care of ophthalmic patients. There are currently no population-based guidelines regarding screening for glaucoma. The purpose of this study is to determine the utility of optical coherence tomography (OCT) for early glaucoma screening in a population of diabetic patients. The results of this study may inform future screening practices. METHODS The current study is a post hoc analysis of OCT data collected from diabetic patients screened for eye disease over 6 months. Glaucoma suspects (GS) were identified based on abnormal retinal nerve fiber layer (RNFL) thickness on OCT. Fundus photographs of GS were graded by two independent raters for vertical cup-to-disc ratio (CDR) and other signs of glaucomatous changes. RESULTS Of the 807 subjects screened, 50 patients (6.2%) were identified as GS. The mean RNFL thickness for GS was significantly lower than the mean RNFL in the total screening population (p < .001). Median CDR for GS was 0.44. Twenty-eight eyes of 17 GS were marked as having optic disc notching or rim thinning by at least one grader. Cohen's kappa statistic for inter-rater reliability was 0.85. Racial differences showed that mean CDR was significantly higher in non-whites (p < .001). Older age was associated with thinner RNFL (r = -0.29, p = .004). CONCLUSIONS Results of this study suggest that in a sample of diabetic patients, a small but clinically significant minority may be flagged as GS based on OCT. Nearly one-third of GS eyes were found to have glaucomatous changes on fundus photography by at least one grader. These results suggest screening with OCT may be useful in detecting early glaucomatous changes in high-risk populations, particularly older, non-white patients with diabetes.
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Affiliation(s)
- Nicholas A Johnson
- Duke University School of Medicine, Department of Ophthalmology, Durham, North Carolina, USA
| | - Priya Gupta
- Duke University School of Medicine, Department of Ophthalmology, Durham, North Carolina, USA
| | - Terry Lee
- Duke University School of Medicine, Department of Ophthalmology, Durham, North Carolina, USA
| | - Majda Hadziahmetovic
- Duke University School of Medicine, Department of Ophthalmology, Durham, North Carolina, USA
| | - Jullia A Rosdahl
- Duke University School of Medicine, Department of Ophthalmology, Durham, North Carolina, USA
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15
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Perumalraja R, Felcia Logan's Deshna B, Swetha N. Statistical performance review on diagnosis of leukemia, glaucoma and diabetes mellitus using AI. Stat Med 2024; 43:1227-1237. [PMID: 38247116 DOI: 10.1002/sim.10004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024]
Abstract
The growth of artificial intelligence (AI) in the healthcare industry tremendously increases the patient outcomes by reshaping the way we diagnose, treat and monitor patients. AI-based innovation in healthcare include exploration of drugs, personalized medicine, clinical diagnosis investigations, robotic-assisted surgery, verified prescriptions, pregnancy care for women, radiology, and reviewed patient information analytics. However, prediction of AI-based solutions are depends mainly on the implementation of statistical algorithms and input data set. In this article, statistical performance review on various algorithms, Accuracy, Precision, Recall and F1-Score used to predict the diagnosis of leukemia, glaucoma, and diabetes mellitus is presented. Review on statistical algorithms' performance, used for individual disease diagnosis gives a complete picture of various research efforts during the last two decades. At the end of statistical review on each disease diagnosis, we have discussed our inferences that will give future directions for the new researchers on selection of AI statistical algorithm as well as the input data set.
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Affiliation(s)
- Rengaraju Perumalraja
- Department of Information Technology, Velammal College of Engineering and Technology, Madurai, India
| | - B Felcia Logan's Deshna
- Department of Information Technology, Velammal College of Engineering and Technology, Madurai, India
| | - N Swetha
- Department of Information Technology, Velammal College of Engineering and Technology, Madurai, India
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16
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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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17
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Gupta V, Birla S, Varshney T, Somarajan BI, Gupta S, Gupta M, Panigrahi A, Singh A, Gupta D. In vivo identification of angle dysgenesis and its relation to genetic markers associated with glaucoma using artificial intelligence. Indian J Ophthalmol 2024; 72:339-346. [PMID: 38146977 PMCID: PMC11001234 DOI: 10.4103/ijo.ijo_1456_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: 06/04/2023] [Revised: 08/18/2023] [Accepted: 10/06/2023] [Indexed: 12/27/2023] Open
Abstract
PURPOSE To predict the presence of angle dysgenesis on anterior-segment optical coherence tomography (ADoA) by using deep learning (DL) and to correlate ADoA with mutations in known glaucoma genes. PARTICIPANTS In total, 800 high-definition anterior-segment optical coherence tomography (AS-OCT) images were included, of which 340 images were used to build the machine learning (ML) model. Images used to build the ML model included 170 scans of primary congenital glaucoma (16 patients), juvenile-onset open-angle glaucoma (62 patients), and adult-onset primary open-angle glaucoma eyes (37 patients); the rest were controls (n = 85). The genetic validation dataset consisted of another 393 images of patients with known mutations that were compared with 320 images of healthy controls. METHODS ADoA was defined as the absence of Schlemm's canal, the presence of hyperreflectivity over the region of the trabecular meshwork, or a hyperreflective membrane. DL was used to classify a given AS-OCT image as either having angle dysgenesis or not. ADoA was then specifically looked for on AS-OCT images of patients with mutations in the known genes for glaucoma. RESULTS The final prediction, which was a consensus-based outcome from the three optimized DL models, had an accuracy of >95%, a specificity of >97%, and a sensitivity of >96% in detecting ADoA in the internal test dataset. Among the patients with known gene mutations, ( MYOC, CYP1B1, FOXC1, and LTBP2 ) ADoA was observed among all the patients in the majority of the images, compared to only 5% of the healthy controls. CONCLUSION ADoA can be objectively identified using models built with DL.
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Affiliation(s)
- Viney Gupta
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Shweta Birla
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Jawaharlal Nehru University, New Delhi, India
| | - Toshit Varshney
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Bindu I Somarajan
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Shikha Gupta
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Mrinalini Gupta
- Department of Biomedical Engineering Technical University of Munich, Munich, Germany
| | - Arnav Panigrahi
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Abhishek Singh
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Dinesh Gupta
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Jawaharlal Nehru University, New Delhi, India
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Christopher M, Gonzalez R, Huynh J, Walker E, Radha Saseendrakumar B, Bowd C, Belghith A, Goldbaum MH, Fazio MA, Girkin CA, De Moraes CG, Liebmann JM, Weinreb RN, Baxter SL, Zangwill LM. Proactive Decision Support for Glaucoma Treatment: Predicting Surgical Interventions with Clinically Available Data. Bioengineering (Basel) 2024; 11:140. [PMID: 38391627 PMCID: PMC10886033 DOI: 10.3390/bioengineering11020140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/06/2024] [Accepted: 01/27/2024] [Indexed: 02/24/2024] Open
Abstract
A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting glaucoma surgical interventions. The cohort included 369 patients who underwent glaucoma surgery and 592 patients who did not undergo surgery. The data types used for prediction included patient demographics, history of systemic conditions, medication history, ophthalmic measurements, 24-2 VF results, and thickness measurements from OCT imaging. The ML models were trained to predict surgical interventions and evaluated on independent data collected at a separate study site. The models were evaluated based on their ability to predict surgeries at varying lengths of time prior to surgical intervention. The highest performing predictions achieved an AUC of 0.93, 0.92, and 0.93 in predicting surgical intervention at 1 year, 2 years, and 3 years, respectively. The models were also able to achieve high sensitivity (0.89, 0.77, 0.86 at 1, 2, and 3 years, respectively) and specificity (0.85, 0.90, and 0.91 at 1, 2, and 3 years, respectively) at an 0.80 level of precision. The multi-modal models trained on a combination of data types predicted surgical interventions with high accuracy up to three years prior to surgery and could provide an important tool to predict the need for glaucoma intervention.
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Affiliation(s)
- Mark Christopher
- Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA
| | - Ruben Gonzalez
- Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA
| | - Justin Huynh
- Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA
| | - Evan Walker
- Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA
| | - Bharanidharan Radha Saseendrakumar
- Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA
| | - Christopher Bowd
- Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA
| | - Akram Belghith
- Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA
| | - Michael H Goldbaum
- Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA
| | - Massimo A Fazio
- Department of Ophthalmology and Vision Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Christopher A Girkin
- Department of Ophthalmology and Vision Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35233, USA
| | - Carlos Gustavo De Moraes
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, NY 10032, USA
| | - Jeffrey M Liebmann
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, NY 10032, USA
| | - Robert N Weinreb
- Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA
| | - Sally L Baxter
- Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA
| | - Linda M Zangwill
- Hamilton Glaucoma Center and Division of Ophthalmology Informatics and Data Science, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, CA 92037, USA
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19
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Zhu Y, Salowe R, Chow C, Li S, Bastani O, O'Brien JM. Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection. Bioengineering (Basel) 2024; 11:122. [PMID: 38391608 PMCID: PMC10886285 DOI: 10.3390/bioengineering11020122] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2023] [Revised: 01/23/2024] [Accepted: 01/24/2024] [Indexed: 02/24/2024] Open
Abstract
Glaucoma, the leading cause of irreversible blindness worldwide, comprises a group of progressive optic neuropathies requiring early detection and lifelong treatment to preserve vision. Artificial intelligence (AI) technologies are now demonstrating transformative potential across the spectrum of clinical glaucoma care. This review summarizes current capabilities, future outlooks, and practical translation considerations. For enhanced screening, algorithms analyzing retinal photographs and machine learning models synthesizing risk factors can identify high-risk patients needing diagnostic workup and close follow-up. To augment definitive diagnosis, deep learning techniques detect characteristic glaucomatous patterns by interpreting results from optical coherence tomography, visual field testing, fundus photography, and other ocular imaging. AI-powered platforms also enable continuous monitoring, with algorithms that analyze longitudinal data alerting physicians about rapid disease progression. By integrating predictive analytics with patient-specific parameters, AI can also guide precision medicine for individualized glaucoma treatment selections. Advances in robotic surgery and computer-based guidance demonstrate AI's potential to improve surgical outcomes and surgical training. Beyond the clinic, AI chatbots and reminder systems could provide patient education and counseling to promote medication adherence. However, thoughtful approaches to clinical integration, usability, diversity, and ethical implications remain critical to successfully implementing these emerging technologies. This review highlights AI's vast capabilities to transform glaucoma care while summarizing key achievements, future prospects, and practical considerations to progress from bench to bedside.
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Affiliation(s)
- Yan Zhu
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rebecca Salowe
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Caven Chow
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shuo Li
- Department of Computer & Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Osbert Bastani
- Department of Computer & Information Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joan M O'Brien
- Department of Ophthalmology, Scheie Eye Institute, University of Pennsylvania, Philadelphia, PA 19104, USA
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Delsoz M, Raja H, Madadi Y, Tang AA, Wirostko BM, Kahook MY, Yousefi S. The Use of ChatGPT to Assist in Diagnosing Glaucoma Based on Clinical Case Reports. Ophthalmol Ther 2023; 12:3121-3132. [PMID: 37707707 PMCID: PMC10640454 DOI: 10.1007/s40123-023-00805-x] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Accepted: 08/29/2023] [Indexed: 09/15/2023] Open
Abstract
INTRODUCTION The purpose of this study was to evaluate the capabilities of large language models such as Chat Generative Pretrained Transformer (ChatGPT) to diagnose glaucoma based on specific clinical case descriptions with comparison to the performance of senior ophthalmology resident trainees. METHODS We selected 11 cases with primary and secondary glaucoma from a publicly accessible online database of case reports. A total of four cases had primary glaucoma including open-angle, juvenile, normal-tension, and angle-closure glaucoma, while seven cases had secondary glaucoma including pseudo-exfoliation, pigment dispersion glaucoma, glaucomatocyclitic crisis, aphakic, neovascular, aqueous misdirection, and inflammatory glaucoma. We input the text of each case detail into ChatGPT and asked for provisional and differential diagnoses. We then presented the details of 11 cases to three senior ophthalmology residents and recorded their provisional and differential diagnoses. We finally evaluated the responses based on the correct diagnoses and evaluated agreements. RESULTS The provisional diagnosis based on ChatGPT was correct in eight out of 11 (72.7%) cases and three ophthalmology residents were correct in six (54.5%), eight (72.7%), and eight (72.7%) cases, respectively. The agreement between ChatGPT and the first, second, and third ophthalmology residents were 9, 7, and 7, respectively. CONCLUSIONS The accuracy of ChatGPT in diagnosing patients with primary and secondary glaucoma, using specific case examples, was similar or better than senior ophthalmology residents. With further development, ChatGPT may have the potential to be used in clinical care settings, such as primary care offices, for triaging and in eye care clinical practices to provide objective and quick diagnoses of patients with glaucoma.
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Affiliation(s)
- Mohammad Delsoz
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA
| | - Hina Raja
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA
| | - Yeganeh Madadi
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA
| | - Anthony A Tang
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA
| | | | - Malik Y Kahook
- Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Siamak Yousefi
- Department of Ophthalmology, Hamilton Eye Institute, University of Tennessee Health Science Center, 930 Madison Ave., Suite 471, Memphis, TN, 38163, USA.
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, TN, USA.
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Li D, Ran AR, Cheung CY, Prince JL. Deep learning in optical coherence tomography: Where are the gaps? Clin Exp Ophthalmol 2023; 51:853-863. [PMID: 37245525 PMCID: PMC10825778 DOI: 10.1111/ceo.14258] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2023] [Revised: 04/24/2023] [Accepted: 05/03/2023] [Indexed: 05/30/2023]
Abstract
Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect the accuracy of quantitative measurements made by post-processing algorithms. Currently, there is a growing interest in applying deep learning (DL) methods to analyse OCT images automatically. This review summarises the trends in DL-based OCT image analysis in ophthalmology, discusses the current gaps, and provides potential research directions. DL in OCT analysis shows promising performance in several tasks: (1) layers and features segmentation and quantification; (2) disease classification; (3) disease progression and prognosis; and (4) referral triage level prediction. Different studies and trends in the development of DL-based OCT image analysis are described and the following challenges are identified and described: (1) public OCT data are scarce and scattered; (2) models show performance discrepancies in real-world settings; (3) models lack of transparency; (4) there is a lack of societal acceptance and regulatory standards; and (5) OCT is still not widely available in underprivileged areas. More work is needed to tackle the challenges and gaps, before DL is further applied in OCT image analysis for clinical use.
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Affiliation(s)
- Dawei Li
- College of Future Technology, Peking University, Beijing, China
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Carol Y. Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, Maryland, USA
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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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Medeiros FA, Lee T, Jammal AA, Al-Aswad LA, Eydelman MB, Schuman JS. The Definition of Glaucomatous Optic Neuropathy in Artificial Intelligence Research and Clinical Applications. Ophthalmol Glaucoma 2023; 6:432-438. [PMID: 36731747 PMCID: PMC10387499 DOI: 10.1016/j.ogla.2023.01.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2022] [Revised: 01/19/2023] [Accepted: 01/23/2023] [Indexed: 06/11/2023]
Abstract
OBJECTIVE Although artificial intelligence (AI) models may offer innovative and powerful ways to use the wealth of data generated by diagnostic tools, there are important challenges related to their development and validation. Most notable is the lack of a perfect reference standard for glaucomatous optic neuropathy (GON). Because AI models are trained to predict presence of glaucoma or its progression, they generally rely on a reference standard that is used to train the model and assess its validity. If an improper reference standard is used, the model may be trained to detect or predict something that has little or no clinical value. This article summarizes the issues and discussions related to the definition of GON in AI applications as presented by the Glaucoma Workgroup from the Collaborative Community for Ophthalmic Imaging (CCOI) US Food and Drug Administration Virtual Workshop, on September 3 and 4, 2020, and on January 28, 2022. DESIGN Review and conference proceedings. SUBJECTS No human or animal subjects or data therefrom were used in the production of this article. METHODS A summary of the Workshop was produced with input and approval from all participants. MAIN OUTCOME MEASURES Consensus position of the CCOI Workgroup on the challenges in defining GON and possible solutions. RESULTS The Workshop reviewed existing challenges that arise from the use of subjective definitions of GON and highlighted the need for a more objective approach to characterize GON that could facilitate replication and comparability of AI studies and allow for better clinical validation of proposed AI tools. Different tests and combination of parameters for defining a reference standard for GON have been proposed. Different reference standards may need to be considered depending on the scenario in which the AI models are going to be applied, such as community-based or opportunistic screening versus detection or monitoring of glaucoma in tertiary care. CONCLUSIONS The development and validation of new AI-based diagnostic tests should be based on rigorous methodology with clear determination of how the reference standards for glaucomatous damage are constructed and the settings where the tests are going to be applied. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
- Felipe A Medeiros
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina; Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina.
| | - Terry Lee
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina
| | - Alessandro A Jammal
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina
| | - Lama A Al-Aswad
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York; Department of Population Health, NYU Langone Health, NYU Grossman School of Medicine, New York, New York
| | | | - Joel S Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York; Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York; Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, New York; Center for Neural Science, NYU, New York, New York; Neuroscience Institute, NYU Langone Health, New York, New York
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Hemelings R, Elen B, Schuster AK, Blaschko MB, Barbosa-Breda J, Hujanen P, Junglas A, Nickels S, White A, Pfeiffer N, Mitchell P, De Boever P, Tuulonen A, Stalmans I. A generalizable deep learning regression model for automated glaucoma screening from fundus images. NPJ Digit Med 2023; 6:112. [PMID: 37311940 PMCID: PMC10264390 DOI: 10.1038/s41746-023-00857-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2022] [Accepted: 06/01/2023] [Indexed: 06/15/2023] Open
Abstract
A plethora of classification models for the detection of glaucoma from fundus images have been proposed in recent years. Often trained with data from a single glaucoma clinic, they report impressive performance on internal test sets, but tend to struggle in generalizing to external sets. This performance drop can be attributed to data shifts in glaucoma prevalence, fundus camera, and the definition of glaucoma ground truth. In this study, we confirm that a previously described regression network for glaucoma referral (G-RISK) obtains excellent results in a variety of challenging settings. Thirteen different data sources of labeled fundus images were utilized. The data sources include two large population cohorts (Australian Blue Mountains Eye Study, BMES and German Gutenberg Health Study, GHS) and 11 publicly available datasets (AIROGS, ORIGA, REFUGE1, LAG, ODIR, REFUGE2, GAMMA, RIM-ONEr3, RIM-ONE DL, ACRIMA, PAPILA). To minimize data shifts in input data, a standardized image processing strategy was developed to obtain 30° disc-centered images from the original data. A total of 149,455 images were included for model testing. Area under the receiver operating characteristic curve (AUC) for BMES and GHS population cohorts were at 0.976 [95% CI: 0.967-0.986] and 0.984 [95% CI: 0.980-0.991] on participant level, respectively. At a fixed specificity of 95%, sensitivities were at 87.3% and 90.3%, respectively, surpassing the minimum criteria of 85% sensitivity recommended by Prevent Blindness America. AUC values on the eleven publicly available data sets ranged from 0.854 to 0.988. These results confirm the excellent generalizability of a glaucoma risk regression model trained with homogeneous data from a single tertiary referral center. Further validation using prospective cohort studies is warranted.
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Affiliation(s)
- Ruben Hemelings
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000, Leuven, Belgium.
- Flemish Institute for Technological Research (VITO), Boeretang 200, 2400, Mol, Belgium.
| | - Bart Elen
- Flemish Institute for Technological Research (VITO), Boeretang 200, 2400, Mol, Belgium
| | - Alexander K Schuster
- Department of Ophthalmology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | | | - João Barbosa-Breda
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
- Cardiovascular R&D Center, Faculty of Medicine of the University of Porto, Alameda Prof. Hernâni Monteiro, 4200-319, Porto, Portugal
- Department of Ophthalmology, Centro Hospitalar e Universitário São João, Alameda Prof. Hernâni Monteiro, 4200-319, Porto, Portugal
| | - Pekko Hujanen
- Tays Eye Centre, Tampere University Hospital, Tampere, Finland
| | - Annika Junglas
- Department of Ophthalmology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Stefan Nickels
- Department of Ophthalmology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Andrew White
- Department of Ophthalmology, The University of Sydney, Sydney, NSW, Australia
| | - Norbert Pfeiffer
- Department of Ophthalmology, University Medical Center Mainz, Langenbeckstr. 1, 55131, Mainz, Germany
| | - Paul Mitchell
- Department of Ophthalmology, The University of Sydney, Sydney, NSW, Australia
| | - Patrick De Boever
- Centre for Environmental Sciences, Hasselt University, Agoralaan building D, 3590, Diepenbeek, Belgium
- University of Antwerp, Department of Biology, 2610, Wilrijk, Belgium
| | - Anja Tuulonen
- Tays Eye Centre, Tampere University Hospital, Tampere, Finland
| | - Ingeborg Stalmans
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Herestraat 49, 3000, Leuven, Belgium
- Ophthalmology Department, UZ Leuven, Herestraat 49, 3000, Leuven, Belgium
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25
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Zhang L, Tang L, Xia M, Cao G. The application of artificial intelligence in glaucoma diagnosis and prediction. Front Cell Dev Biol 2023; 11:1173094. [PMID: 37215077 PMCID: PMC10192631 DOI: 10.3389/fcell.2023.1173094] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/24/2023] [Indexed: 05/24/2023] Open
Abstract
Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups.
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Affiliation(s)
- Linyu Zhang
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Li Tang
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Min Xia
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Guofan Cao
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
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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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Thakur S, Dinh LL, Lavanya R, Quek TC, Liu Y, Cheng CY. Use of artificial intelligence in forecasting glaucoma progression. Taiwan J Ophthalmol 2023; 13:168-183. [PMID: 37484617 PMCID: PMC10361424 DOI: 10.4103/tjo.tjo-d-23-00022] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Accepted: 03/03/2023] [Indexed: 07/25/2023] Open
Abstract
Artificial intelligence (AI) has been widely used in ophthalmology for disease detection and monitoring progression. For glaucoma research, AI has been used to understand progression patterns and forecast disease trajectory based on analysis of clinical and imaging data. Techniques such as machine learning, natural language processing, and deep learning have been employed for this purpose. The results from studies using AI for forecasting glaucoma progression however vary considerably due to dataset constraints, lack of a standard progression definition and differences in methodology and approach. While glaucoma detection and screening have been the focus of most research that has been published in the last few years, in this narrative review we focus on studies that specifically address glaucoma progression. We also summarize the current evidence, highlight studies that have translational potential, and provide suggestions on how future research that addresses glaucoma progression can be improved.
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Affiliation(s)
- Sahil Thakur
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Linh Le Dinh
- Institute of High Performance Computing, The Agency for Science, Technology and Research, Singapore
| | - Raghavan Lavanya
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Ten Cheer Quek
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yong Liu
- Institute of High Performance Computing, The Agency for Science, Technology and Research, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore
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Turner M, Ou Y. At-Home Glaucoma Monitoring: Is it Ready for Prime Time? Ophthalmol Glaucoma 2023; 6:117-120. [PMID: 36184483 DOI: 10.1016/j.ogla.2022.08.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2022] [Revised: 07/28/2022] [Accepted: 08/11/2022] [Indexed: 10/14/2022]
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29
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Soh ZD, Jiang Y, S/O Ganesan SS, Zhou M, Nongiur M, Majithia S, Tham YC, Rim TH, Qian C, Koh V, Aung T, Wong TY, Xu X, Liu Y, Cheng CY. From 2 dimensions to 3rd dimension: Quantitative prediction of anterior chamber depth from anterior segment photographs via deep-learning. PLOS DIGITAL HEALTH 2023; 2:e0000193. [PMID: 36812642 PMCID: PMC9931242 DOI: 10.1371/journal.pdig.0000193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 01/06/2023] [Indexed: 02/04/2023]
Abstract
Anterior chamber depth (ACD) is a major risk factor of angle closure disease, and has been used in angle closure screening in various populations. However, ACD is measured from ocular biometer or anterior segment optical coherence tomography (AS-OCT), which are costly and may not be readily available in primary care and community settings. Thus, this proof-of-concept study aims to predict ACD from low-cost anterior segment photographs (ASPs) using deep-learning (DL). We included 2,311 pairs of ASPs and ACD measurements for algorithm development and validation, and 380 pairs for algorithm testing. We captured ASPs with a digital camera mounted on a slit-lamp biomicroscope. Anterior chamber depth was measured with ocular biometer (IOLMaster700 or Lenstar LS9000) in data used for algorithm development and validation, and with AS-OCT (Visante) in data used for testing. The DL algorithm was modified from the ResNet-50 architecture, and assessed using mean absolute error (MAE), coefficient-of-determination (R2), Bland-Altman plot and intraclass correlation coefficients (ICC). In validation, our algorithm predicted ACD with a MAE (standard deviation) of 0.18 (0.14) mm; R2 = 0.63. The MAE of predicted ACD was 0.18 (0.14) mm in eyes with open angles and 0.19 (0.14) mm in eyes with angle closure. The ICC between actual and predicted ACD measurements was 0.81 (95% CI 0.77, 0.84). In testing, our algorithm predicted ACD with a MAE of 0.23 (0.18) mm; R2 = 0.37. Saliency maps highlighted the pupil and its margin as the main structures used in ACD prediction. This study demonstrates the possibility of predicting ACD from ASPs via DL. This algorithm mimics an ocular biometer in making its prediction, and provides a foundation to predict other quantitative measurements that are relevant to angle closure screening.
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Affiliation(s)
- Zhi Da Soh
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yixing Jiang
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | | | - Menghan Zhou
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | - Monisha Nongiur
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Shivani Majithia
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yih Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Chaoxu Qian
- Department of Ophthalmology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Victor Koh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Ophthalmology, National University Hospital, Singapore
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
- Tsinghua Medicine, Tsinghua University, China
| | - Xinxing Xu
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | - Yong Liu
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
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Kamalipour A, Moghimi S, Khosravi P, Mohammadzadeh V, Nishida T, Micheletti E, Wu JH, Mahmoudinezhad G, Li EHF, Christopher M, Zangwill L, Javidi T, Weinreb RN. Combining Optical Coherence Tomography and Optical Coherence Tomography Angiography Longitudinal Data for the Detection of Visual Field Progression in Glaucoma. Am J Ophthalmol 2023; 246:141-154. [PMID: 36328200 DOI: 10.1016/j.ajo.2022.10.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 10/14/2022] [Accepted: 10/15/2022] [Indexed: 11/05/2022]
Abstract
PURPOSE To use longitudinal optical coherence tomography (OCT) and OCT angiography (OCTA) data to detect glaucomatous visual field (VF) progression with a supervised machine learning approach. DESIGN Prospective cohort study. METHODS One hundred ten eyes of patients with suspected glaucoma (33.6%) and patients with glaucoma (66.4%) with a minimum of 5 24-2 VF tests and 3 optic nerve head and macula images over an average follow-up duration of 4.1 years were included. VF progression was defined using a composite measure including either a "likely progression event" on Guided Progression Analysis, a statistically significant negative slope of VF mean deviation or VF index, or a positive pointwise linear regression event. Feature-based gradient boosting classifiers were developed using different subsets of baseline and longitudinal OCT and OCTA summary parameters. The area under the receiver operating characteristic curve (AUROC) was used to compare the classification performance of different models. RESULTS VF progression was detected in 28 eyes (25.5%). The model with combined baseline and longitudinal OCT and OCTA parameters at the global and hemifield levels had the best classification accuracy to detect VF progression (AUROC = 0.89). Models including combined OCT and OCTA parameters had higher classification accuracy compared with those with individual subsets of OCT or OCTA features alone. Including hemifield measurements significantly improved the models' classification accuracy compared with using global measurements alone. Including longitudinal rates of change of OCT and OCTA parameters (AUROCs = 0.80-0.89) considerably increased the classification accuracy of the models with baseline measurements alone (AUROCs = 0.60-0.63). CONCLUSIONS Longitudinal OCTA measurements complement OCT-derived structural metrics for the evaluation of functional VF loss in patients with glaucoma.
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Affiliation(s)
- Alireza Kamalipour
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Sasan Moghimi
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Pooya Khosravi
- School of Medicine (P.K.), University of California, Irvine, Irvine, California, USA
| | - Vahid Mohammadzadeh
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Takashi Nishida
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Eleonora Micheletti
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Jo-Hsuan Wu
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Golnoush Mahmoudinezhad
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Elizabeth H F Li
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Mark Christopher
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Linda Zangwill
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology
| | - Tara Javidi
- Department of Electrical and Computer Engineering (T.J.), University of California San Diego, La Jolla
| | - Robert N Weinreb
- From the Hamilton Glaucoma (A.K., S.M., V.M., T.N., E.M., J-H.W., G.M., E.H.F.L., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology.
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Fea AM, Ricardi F, Novarese C, Cimorosi F, Vallino V, Boscia G. Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State. Int J Mol Sci 2023; 24:2814. [PMID: 36769127 PMCID: PMC9917798 DOI: 10.3390/ijms24032814] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/07/2023] [Accepted: 01/18/2023] [Indexed: 02/05/2023] Open
Abstract
Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease's development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers.
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Jaumandreu L, Antón A, Pazos M, Rodriguez-Uña I, Rodriguez Agirretxe I, Martinez de la Casa JM, Ayala ME, Parrilla-Vallejo M, Dyrda A, Díez-Álvarez L, Rebolleda G, Muñoz-Negrete FJ. Glaucoma progression. Clinical practice guide. ARCHIVOS DE LA SOCIEDAD ESPANOLA DE OFTALMOLOGIA 2023; 98:40-57. [PMID: 36089479 DOI: 10.1016/j.oftale.2022.08.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Accepted: 05/19/2022] [Indexed: 01/11/2023]
Abstract
OBJECTIVE To provide general recommendations that serve as a guide for the evaluation and management of glaucomatous progression in daily clinical practice based on the existing quality of clinical evidence. METHODS After defining the objectives and scope of the guide, the working group was formed and structured clinical questions were formulated following the PICO (Patient, Intervention, Comparison, Outcomes) format. Once all the existing clinical evidence had been independently evaluated with the AMSTAR 2 (Assessment of Multiple Systematic Reviews) and Cochrane "Risk of bias" tools by at least two reviewers, recommendations were formulated following the Scottish Intercollegiate Guideline network (SIGN) methodology. RESULTS Recommendations with their corresponding levels of evidence that may be useful in the interpretation and decision-making related to the different methods for the detection of glaucomatous progression are presented. CONCLUSIONS Despite the fact that for many of the questions the level of scientific evidence available is not very high, this clinical practice guideline offers an updated review of the different existing aspects related to the evaluation and management of glaucomatous progression.
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Affiliation(s)
- L Jaumandreu
- Servicio de Oftalmología, Hospital Universitario Ramón y Cajal, IRYCIS, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain; Red de Oftalmología RETICS OFTARED del Instituto de Salud Carlos III (ISCIII), Madrid, Spain.
| | - A Antón
- Institut Català de la Retina (ICR), Barcelona, Spain; Universitat Internacional de Catalunya (UIC), Barcelona, Spain; Red de Oftalmología RETICS OFTARED del Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - M Pazos
- Institut Clínic d'Oftalmologia, Hospital Clínic de Barcelona, IDIBAPS, Universitat de Barcelona, Barcelona, Spain; Red de Oftalmología RETICS OFTARED del Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - I Rodriguez-Uña
- Instituto Oftalmológico Fernández-Vega, Universidad de Oviedo, Oviedo, Spain; Red de Oftalmología RETICS OFTARED del Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - I Rodriguez Agirretxe
- Servicio de Oftalmología, Hospital Universitario Donostia, San Sebastián, Gipuzkoa, Spain; Red de Oftalmología RETICS OFTARED del Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - J M Martinez de la Casa
- Servicio de Oftalmología, Hospital Clinico San Carlos, Instituto de investigación sanitaria del Hospital Clínico San Carlos (IsISSC), IIORC, Universidad Complutense de Madrid, Madrid, Spain; Red de Oftalmología RETICS OFTARED del Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - M E Ayala
- Institut Català de la Retina (ICR), Barcelona, Spain; Red de Oftalmología RETICS OFTARED del Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - M Parrilla-Vallejo
- Servicio de Oftalmología, Hospital Universitario Virgen Macarena, Sevilla, Spain; Red de Oftalmología RETICS OFTARED del Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - A Dyrda
- Institut Català de la Retina (ICR), Barcelona, Spain
| | - L Díez-Álvarez
- Servicio de Oftalmología, Hospital Universitario Ramón y Cajal, IRYCIS, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain; Red de Oftalmología RETICS OFTARED del Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - G Rebolleda
- Servicio de Oftalmología, Hospital Universitario Ramón y Cajal, IRYCIS, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain; Red de Oftalmología RETICS OFTARED del Instituto de Salud Carlos III (ISCIII), Madrid, Spain
| | - F J Muñoz-Negrete
- Servicio de Oftalmología, Hospital Universitario Ramón y Cajal, IRYCIS, Universidad de Alcalá, Alcalá de Henares, Madrid, Spain; Red de Oftalmología RETICS OFTARED del Instituto de Salud Carlos III (ISCIII), Madrid, Spain
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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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Razaghi G, Aghsaei Fard M, Hejazi M. Correction of Retinal Nerve Fiber Layer Thickness Measurement on Spectral-Domain Optical Coherence Tomographic Images Using U-net Architecture. J Ophthalmic Vis Res 2023; 18:41-50. [PMID: 36937200 PMCID: PMC10020786 DOI: 10.18502/jovr.v18i1.12724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2021] [Accepted: 11/10/2022] [Indexed: 02/24/2023] Open
Abstract
Purpose In this study, an algorithm based on deep learning was presented to reduce the retinal nerve fiber layer (RNFL) segmentation errors in spectral domain optical coherence tomography (SD-OCT) scans using ophthalmologists' manual segmentation as a reference standard. Methods In this study, we developed an image segmentation network based on deep learning to automatically identify the RNFL thickness from B-scans obtained with SD-OCT. The scans were collected from Farabi Eye Hospital (500 B-scans were used for training, while 50 were used for testing). To remove the speckle noise from the images, preprocessing was applied before training, and postprocessing was performed to fill any discontinuities that might exist. Afterward, output masks were analyzed for their average thickness. Finally, the calculation of mean absolute error between predicted and ground truth RNFL thickness was performed. Results Based on the testing database, SD-OCT segmentation had an average dice similarity coefficient of 0.91, and thickness estimation had a mean absolute error of 2.23 ± 2.1 μm. As compared to conventional OCT software algorithms, deep learning predictions were better correlated with the best available estimate during the test period (r2 = 0.99 vs r2 = 0.88, respectively; P < 0.001). Conclusion Our experimental results demonstrate effective and precise segmentation of the RNFL layer with the coefficient of 0.91 and reliable thickness prediction with MAE 2.23 ± 2.1 μm in SD-OCT B-scans. Performance is comparable with human annotation of the RNFL layer and other algorithms according to the correlation coefficient of 0.99 and 0.88, respectively, while artifacts and errors are evident.
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Affiliation(s)
- Ghazale Razaghi
- Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
| | - Masoud Aghsaei Fard
- Department of Ophthalmology, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Marjaneh Hejazi
- Medical Physics and Biomedical Engineering Department, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging, Bio-Optical Imaging Group, Tehran University of Medical Sciences, Tehran, Iran
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Puneet, Kumar R, Gupta M. Optical coherence tomography image based eye disease detection using deep convolutional neural network. Health Inf Sci Syst 2022; 10:13. [PMID: 35756852 PMCID: PMC9213631 DOI: 10.1007/s13755-022-00182-y] [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: 03/22/2022] [Accepted: 06/08/2022] [Indexed: 12/23/2022] Open
Abstract
Over the past few decades, health care industries and medical practitioners faced a lot of obstacles to diagnosing medical-related problems due to inadequate technology and availability of equipment. In the present era, computer science technologies such as IoT, Cloud Computing, Artificial Intelligence and its allied techniques, etc. play a crucial role in the identification of medical diseases, especially in the domain of Ophthalmology. Despite this, ophthalmologists have to perform the various disease diagnosis task manually which is time-consuming and the chances of error are also very high because some of the abnormalities of eye diseases possess the same symptoms. Furthermore, multiple autonomous systems also exist to categorize the diseases but their prediction rate does not accomplish state-of-art accuracy. In the proposed approach by implementing the concept of Attention, Transfer Learning with the Deep Convolution Neural Network, the model accomplished an accuracy of 97.79% and 95.6% on the training and testing data respectively. This autonomous model efficiently classifies the various oscular disorders namely Choroidal Neovascularization, Diabetic Macular Edema, Drusen from the Optical Coherence Tomography images. It may provide a realistic solution to the healthcare sector to bring down the ophthalmologist burden in the screening of Diabetic Retinopathy.
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Affiliation(s)
- Puneet
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab India
| | - Rakesh Kumar
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab India
| | - Meenu Gupta
- Department of Computer Science and Engineering, Chandigarh University, Mohali, Punjab India
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Chalkidou A, Shokraneh F, Kijauskaite G, Taylor-Phillips S, Halligan S, Wilkinson L, Glocker B, Garrett P, Denniston AK, Mackie A, Seedat F. Recommendations for the development and use of imaging test sets to investigate the test performance of artificial intelligence in health screening. Lancet Digit Health 2022; 4:e899-e905. [PMID: 36427951 DOI: 10.1016/s2589-7500(22)00186-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Revised: 08/11/2022] [Accepted: 09/09/2022] [Indexed: 11/24/2022]
Abstract
Rigorous evaluation of artificial intelligence (AI) systems for image classification is essential before deployment into health-care settings, such as screening programmes, so that adoption is effective and safe. A key step in the evaluation process is the external validation of diagnostic performance using a test set of images. We conducted a rapid literature review on methods to develop test sets, published from 2012 to 2020, in English. Using thematic analysis, we mapped themes and coded the principles using the Population, Intervention, and Comparator or Reference standard, Outcome, and Study design framework. A group of screening and AI experts assessed the evidence-based principles for completeness and provided further considerations. From the final 15 principles recommended here, five affect population, one intervention, two comparator, one reference standard, and one both reference standard and comparator. Finally, four are appliable to outcome and one to study design. Principles from the literature were useful to address biases from AI; however, they did not account for screening specific biases, which we now incorporate. The principles set out here should be used to support the development and use of test sets for studies that assess the accuracy of AI within screening programmes, to ensure they are fit for purpose and minimise bias.
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Affiliation(s)
| | - Farhad Shokraneh
- King's Technology Evaluation Centre, King's College London, London, UK
| | - Goda Kijauskaite
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | | | - Steve Halligan
- Centre for Medical Imaging, Division of Medicine, University College London, London, UK
| | | | - Ben Glocker
- Department of Computing, Imperial College London, London, UK
| | - Peter Garrett
- Department of Chemical Engineering and Analytical Science, University of Manchester, Manchester, UK
| | - Alastair K Denniston
- Department of Ophthalmology, University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK
| | - Anne Mackie
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
| | - Farah Seedat
- UK National Screening Committee, Office for Health Improvement and Disparities, Department of Health and Social Care, London, UK
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Lim WS, Ho HY, Ho HC, Chen YW, Lee CK, Chen PJ, Lai F, Jang JSR, Ko ML. Use of multimodal dataset in AI for detecting glaucoma based on fundus photographs assessed with OCT: focus group study on high prevalence of myopia. BMC Med Imaging 2022; 22:206. [PMID: 36434508 PMCID: PMC9700928 DOI: 10.1186/s12880-022-00933-z] [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: 10/21/2021] [Accepted: 11/10/2022] [Indexed: 11/26/2022] Open
Abstract
BACKGROUND Glaucoma is one of the major causes of blindness; it is estimated that over 110 million people will be affected by glaucoma worldwide by 2040. Research on glaucoma detection using deep learning technology has been increasing, but the diagnosis of glaucoma in a large population with high incidence of myopia remains a challenge. This study aimed to provide a decision support system for the automatic detection of glaucoma using fundus images, which can be applied for general screening, especially in areas of high incidence of myopia. METHODS A total of 1,155 fundus images were acquired from 667 individuals with a mean axial length of 25.60 ± 2.0 mm at the National Taiwan University Hospital, Hsinchu Br. These images were graded based on the findings of complete ophthalmology examinations, visual field test, and optical coherence tomography into three groups: normal (N, n = 596), pre-perimetric glaucoma (PPG, n = 66), and glaucoma (G, n = 493), and divided into a training-validation (N: 476, PPG: 55, G: 373) and test (N: 120, PPG: 11, G: 120) sets. A multimodal model with the Xception model as image feature extraction and machine learning algorithms [random forest (RF), support vector machine (SVM), dense neural network (DNN), and others] was applied. RESULTS The Xception model classified the N, PPG, and G groups with 93.9% of the micro-average area under the receiver operating characteristic curve (AUROC) with tenfold cross-validation. Although normal and glaucoma sensitivity can reach 93.51% and 86.13% respectively, the PPG sensitivity was only 30.27%. The AUROC increased to 96.4% in the N + PPG and G groups. The multimodal model with the N + PPG and G groups showed that the AUROCs of RF, SVM, and DNN were 99.56%, 99.59%, and 99.10%, respectively; The N and PPG + G groups had less than 1% difference. The test set showed an overall 3%-5% less AUROC than the validation results. CONCLUSION The multimodal model had good AUROC while detecting glaucoma in a population with high incidence of myopia. The model shows the potential for general automatic screening and telemedicine, especially in Asia. TRIAL REGISTRATION The study was approved by the Institutional Review Board of the National Taiwan University Hospital, Hsinchu Branch (no. NTUHHCB 108-025-E).
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Affiliation(s)
- Wee Shin Lim
- grid.19188.390000 0004 0546 0241Department of Computer Science and Information Engineering, National Taiwan University, Taipei City 10617, Taiwan, ROC
| | - Heng-Yen Ho
- grid.19188.390000 0004 0546 0241School of Medicine, National Taiwan University, Taipei City 10617, Taiwan, ROC
| | - Heng-Chen Ho
- grid.19188.390000 0004 0546 0241School of Medicine, National Taiwan University, Taipei City 10617, Taiwan, ROC
| | - Yan-Wu Chen
- grid.412036.20000 0004 0531 9758Department of Applied Mathematics, National Sun Yat-Sen University, Kaohsiung City 804201, Taiwan, ROC
| | - Chih-Kuo Lee
- grid.412094.a0000 0004 0572 7815Department of Internal Medicine, National Taiwan University Hospital Hsin-Chu Branch, Hsinchu City 300, Taiwan, ROC
| | - Pao-Ju Chen
- grid.412094.a0000 0004 0572 7815Department of Ophthalmology, National Taiwan University Hospital Hsin-Chu Branch, No. 25, Lane 442, Sec.1, Jingguo Rd., Hsinchu City 300, Taiwan, ROC
| | - Feipei Lai
- grid.19188.390000 0004 0546 0241Department of Electrical Engineering, National Taiwan University, Taipei City 10617, Taiwan, ROC
| | - Jyh-Shing Roger Jang
- grid.19188.390000 0004 0546 0241Department of Computer Science and Information Engineering, National Taiwan University, Taipei City 10617, Taiwan, ROC
| | - Mei-Lan Ko
- grid.412094.a0000 0004 0572 7815Department of Ophthalmology, National Taiwan University Hospital Hsin-Chu Branch, No. 25, Lane 442, Sec.1, Jingguo Rd., Hsinchu City 300, Taiwan, ROC ,grid.38348.340000 0004 0532 0580Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Taipei City 10617, Taiwan, ROC
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Deep Learning-Based Glaucoma Screening Using Regional RNFL Thickness in Fundus Photography. Diagnostics (Basel) 2022; 12:diagnostics12112894. [PMID: 36428954 PMCID: PMC9689347 DOI: 10.3390/diagnostics12112894] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 11/14/2022] [Accepted: 11/14/2022] [Indexed: 11/23/2022] Open
Abstract
Since glaucoma is a progressive and irreversible optic neuropathy, accurate screening and/or early diagnosis is critical in preventing permanent vision loss. Recently, optical coherence tomography (OCT) has become an accurate diagnostic tool to observe and extract the thickness of the retinal nerve fiber layer (RNFL), which closely reflects the nerve damage caused by glaucoma. However, OCT is less accessible than fundus photography due to higher cost and expertise required for operation. Though widely used, fundus photography is effective for early glaucoma detection only when used by experts with extensive training. Here, we introduce a deep learning-based approach to predict the RNFL thickness around optic disc regions in fundus photography for glaucoma screening. The proposed deep learning model is based on a convolutional neural network (CNN) and utilizes images taken with fundus photography and with RNFL thickness measured with OCT for model training and validation. Using a dataset acquired from normal tension glaucoma (NTG) patients, the trained model can estimate RNFL thicknesses in 12 optic disc regions from fundus photos. Using intuitive thickness labels to identify localized damage of the optic nerve head and then estimating regional RNFL thicknesses from fundus images, we determine that screening for glaucoma could achieve 92% sensitivity and 86.9% specificity. Receiver operating characteristic (ROC) analysis results for specificity of 80% demonstrate that use of the localized mean over superior and inferior regions reaches 90.7% sensitivity, whereas 71.2% sensitivity is reached using the global RNFL thicknesses for specificity at 80%. This demonstrates that the new approach of using regional RNFL thicknesses in fundus images holds good promise as a potential screening technique for early stage of glaucoma.
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Jin K, Ye J. Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives. ADVANCES IN OPHTHALMOLOGY PRACTICE AND RESEARCH 2022; 2:100078. [PMID: 37846285 PMCID: PMC10577833 DOI: 10.1016/j.aopr.2022.100078] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/01/2022] [Accepted: 08/18/2022] [Indexed: 10/18/2023]
Abstract
Background The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic. Main text At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns. Conclusions This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.
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Affiliation(s)
- Kai Jin
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Juan Ye
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
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Nunez R, Harris A, Ibrahim O, Keller J, Wikle CK, Robinson E, Zukerman R, Siesky B, Verticchio A, Rowe L, Guidoboni G. Artificial Intelligence to Aid Glaucoma Diagnosis and Monitoring: State of the Art and New Directions. PHOTONICS 2022; 9:810. [PMID: 36816462 PMCID: PMC9934292 DOI: 10.3390/photonics9110810] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Recent developments in the use of artificial intelligence in the diagnosis and monitoring of glaucoma are discussed. To set the context and fix terminology, a brief historic overview of artificial intelligence is provided, along with some fundamentals of statistical modeling. Next, recent applications of artificial intelligence techniques in glaucoma diagnosis and the monitoring of glaucoma progression are reviewed, including the classification of visual field images and the detection of glaucomatous change in retinal nerve fiber layer thickness. Current challenges in the direct application of artificial intelligence to further our understating of this disease are also outlined. The article also discusses how the combined use of mathematical modeling and artificial intelligence may help to address these challenges, along with stronger communication between data scientists and clinicians.
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Affiliation(s)
- Roberto Nunez
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | - Alon Harris
- Department of Ophthalmology, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA
| | - Omar Ibrahim
- Department of Electrical Engineering, Tikrit University, Tikrit P.O. Box 42, Iraq
| | - James Keller
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
| | | | - Erin Robinson
- Department of Social Work, University of Missouri, Columbia, MO 65211, USA
| | - Ryan Zukerman
- Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Irving Medical Center, New York-Presbyterian Hospital, New York, NY 10034, USA
| | - Brent Siesky
- Department of Ophthalmology, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA
| | - Alice Verticchio
- Department of Ophthalmology, Icahn School of Medicine at Mt. Sinai, New York, NY 10029, USA
| | - Lucas Rowe
- Department of Ophthalmology, Indiana University School of Medicine, Indianapolis, IN 46202, USA
| | - Giovanna Guidoboni
- Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA
- Department of Mathematics, University of Missouri, Columbia, MO 65211, USA
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Thompson AC, Falconi A, Sappington RM. Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging. FRONTIERS IN OPHTHALMOLOGY 2022; 2:937205. [PMID: 38983522 PMCID: PMC11182271 DOI: 10.3389/fopht.2022.937205] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/22/2022] [Indexed: 07/11/2024]
Abstract
Glaucoma is a leading cause of progressive blindness and visual impairment worldwide. Microstructural evidence of glaucomatous damage to the optic nerve head and associated tissues can be visualized using optical coherence tomography (OCT). In recent years, development of novel deep learning (DL) algorithms has led to innovative advances and improvements in automated detection of glaucomatous damage and progression on OCT imaging. DL algorithms have also been trained utilizing OCT data to improve detection of glaucomatous damage on fundus photography, thus improving the potential utility of color photos which can be more easily collected in a wider range of clinical and screening settings. This review highlights ten years of contributions to glaucoma detection through advances in deep learning models trained utilizing OCT structural data and posits future directions for translation of these discoveries into the field of aging and the basic sciences.
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Affiliation(s)
- Atalie C. Thompson
- Department of Surgical Ophthalmology, Wake Forest School of Medicine, Winston Salem, NC, United States
- Department of Internal Medicine, Gerontology, and Geriatric Medicine, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Aurelio Falconi
- Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Rebecca M. Sappington
- Department of Surgical Ophthalmology, Wake Forest School of Medicine, Winston Salem, NC, United States
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston Salem, NC, United States
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Al-Aswad LA, Ramachandran R, Schuman JS, Medeiros F, Eydelman MB. Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression. Ophthalmol Glaucoma 2022; 5:e16-e25. [PMID: 35218987 PMCID: PMC9399304 DOI: 10.1016/j.ogla.2022.02.010] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/14/2022] [Accepted: 02/17/2022] [Indexed: 12/15/2022]
Abstract
On September 3, 2020, the Collaborative Community on Ophthalmic Imaging conducted its first 2-day virtual workshop on the role of artificial intelligence (AI) and related machine learning techniques in the diagnosis and treatment of various ophthalmic conditions. In a session entitled "Artificial Intelligence for Glaucoma," a panel of glaucoma specialists, researchers, industry experts, and patients convened to share current research on the application of AI to commonly used diagnostic modalities, including fundus photography, OCT imaging, standard automated perimetry, and gonioscopy. The conference participants focused on the use of AI as a tool for disease prediction, highlighted its ability to address inequalities, and presented the limitations of and challenges to its clinical application. The panelists' discussion addressed AI and health equities from clinical, societal, and regulatory perspectives.
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Affiliation(s)
- Lama A Al-Aswad
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York; Department of Population Health, NYU Langone Health, NYU Grossman School of Medicine, New York, New York.
| | - Rithambara Ramachandran
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York
| | - Joel S Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York; Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York; Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, New York; Center for Neural Science, NYU, New York, New York; Neuroscience Institute, NYU Langone Health, New York, New York
| | - Felipe Medeiros
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina; Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina
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Chen JS, Baxter SL. Applications of natural language processing in ophthalmology: present and future. Front Med (Lausanne) 2022; 9:906554. [PMID: 36004369 PMCID: PMC9393550 DOI: 10.3389/fmed.2022.906554] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2022] [Accepted: 05/31/2022] [Indexed: 11/13/2022] Open
Abstract
Advances in technology, including novel ophthalmic imaging devices and adoption of the electronic health record (EHR), have resulted in significantly increased data available for both clinical use and research in ophthalmology. While artificial intelligence (AI) algorithms have the potential to utilize these data to transform clinical care, current applications of AI in ophthalmology have focused mostly on image-based deep learning. Unstructured free-text in the EHR represents a tremendous amount of underutilized data in big data analyses and predictive AI. Natural language processing (NLP) is a type of AI involved in processing human language that can be used to develop automated algorithms using these vast quantities of available text data. The purpose of this review was to introduce ophthalmologists to NLP by (1) reviewing current applications of NLP in ophthalmology and (2) exploring potential applications of NLP. We reviewed current literature published in Pubmed and Google Scholar for articles related to NLP and ophthalmology, and used ancestor search to expand our references. Overall, we found 19 published studies of NLP in ophthalmology. The majority of these publications (16) focused on extracting specific text such as visual acuity from free-text notes for the purposes of quantitative analysis. Other applications included: domain embedding, predictive modeling, and topic modeling. Future ophthalmic applications of NLP may also focus on developing search engines for data within free-text notes, cleaning notes, automated question-answering, and translating ophthalmology notes for other specialties or for patients, especially with a growing interest in open notes. As medicine becomes more data-oriented, NLP offers increasing opportunities to augment our ability to harness free-text data and drive innovations in healthcare delivery and treatment of ophthalmic conditions.
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Affiliation(s)
- Jimmy S. Chen
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, United States
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, United States
| | - Sally L. Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA, United States
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, CA, United States
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Practical Application of Artificial Intelligence Technology in Glaucoma Diagnosis. J Ophthalmol 2022; 2022:5212128. [PMID: 35957747 PMCID: PMC9357716 DOI: 10.1155/2022/5212128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2022] [Accepted: 06/29/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose. By comparing the performance of different models between artificial intelligence (AI) and doctors, we aim to evaluate and identify the optimal model for future usage of AI. Methods. A total of 500 fundus images of glaucoma and 500 fundus images of normal eyes were collected and randomly divided into five groups, with each group corresponding to one round. The AI system provided diagnostic suggestions for each image. Four doctors provided diagnoses without the assistance of the AI in the first round and with the assistance of the AI in the second and third rounds. In the fourth round, doctor B and doctor D made diagnoses with the help of the AI and the other two doctors without the help of the AI. In the last round, doctor A and doctor B made diagnoses with the help of AI and the other two doctors without the help of the AI. Results. Doctor A, doctor B, and doctor D had a higher accuracy in the diagnosis of glaucoma with the assistance of AI in the second (
,
, and
) and the third round (
,
, and
) than in the first round. The accuracy of at least one doctor was higher than that of AI in the second and third rounds, in spite of no detectable significance (
,
,
, and
). The four doctors’ overall accuracy (
and
) and sensitivity (
and
) as a whole were significantly improved in the second and third rounds. Conclusions. This “Doctor + AI” model can clarify the role of doctors and AI in medical responsibility and ensure the safety of patients, and importantly, this model shows great potential and application prospects.
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Young SL, Jain N, Tatham AJ. The application of advanced imaging techniques in glaucoma. EXPERT REVIEW OF OPHTHALMOLOGY 2022. [DOI: 10.1080/17469899.2022.2101449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
Affiliation(s)
- Su Ling Young
- Princess Alexandra Eye Pavilion, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
| | - Nikhil Jain
- Addenbrooke’s Hospital, Cambridge University Hospitals NHS trust, Cambridge, UK
| | - Andrew J Tatham
- Princess Alexandra Eye Pavilion, Edinburgh, UK
- Centre for Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK
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Morales Domínguez J, Teherán Forero ÓL, Ochoa-Díaz MM, Ramos Clason EC. Validation of the color graduation scale in the optical nerve photograph, an alternative for qualitative classification. ARCHIVOS DE LA SOCIEDAD ESPANOLA DE OFTALMOLOGIA 2022; 97:381-385. [PMID: 35779894 DOI: 10.1016/j.oftale.2022.02.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 10/05/2021] [Indexed: 06/15/2023]
Abstract
BACKGROUND To validate objectively the proposed Teherán-Morales's color grading scale, comparing to the subjective readings of specialists in optic nerve photography. METHODS Concordance study and diagnostic tests, in which 150 photographs of the optic nerve were evaluated, from three groups, glaucomatous neuropathy, neuropathy of other origin and control group with the Teherán-Morales's color scale and the analysis of three experts in optic nerve. Spearman's Rho correlation was performed between both analysis methods. RESULTS In the analysis of all the photographs using Spearman's Rho, we found moderate correlations that were statistically significant P < 0.0001, the highest was in the temporal quadrant by observer 1 (r = 0.650 95% CI 0.546-0.733). In photographs of optic neuropathy, the correlations become moderately high, and statistically significant P < 0.0001, the highest correlation was for the temporal quadrant by observer 1 (r = 0.772 95% CI 0.626-0.865). In the glaucoma and normal eyes groups, there were moderate to low correlations with statistical significance P < 0.05. CONCLUSIONS The Teherán-Morales's scale, for color grading, is useful in detecting color, correlates moderately with the subjective assessment of experts in the optic nerve having its best performance in optic neuropathy with very pale discs. However, in normal or glaucomatous optic discs, it has a low correlation, compared to the subjective clinical assessment.
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Affiliation(s)
- J Morales Domínguez
- Universidad del Sinú, Clínica Oftalmológica de Cartagena, Cartagena, Colombia.
| | - Ó L Teherán Forero
- Departamento de Glaucoma, Clínica Oftalmológica de Cartagena, Cartagena, Colombia
| | - M M Ochoa-Díaz
- Grupo de investigación GIBACUS, Escuela de Medicina, Universidad del Sinú, Seccional Cartagena, Cartagena, Colombia
| | - E C Ramos Clason
- Grupo de investigación GIBACUS, Escuela de Medicina, Universidad del Sinú, Seccional Cartagena, Cartagena, Colombia
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Atalay E, Özalp O, Devecioğlu ÖC, Erdoğan H, İnce T, Yıldırım N. Investigation of the Role of Convolutional Neural Network Architectures in the Diagnosis of Glaucoma using Color Fundus Photography. Turk J Ophthalmol 2022; 52:193-200. [PMID: 35770344 PMCID: PMC9249112 DOI: 10.4274/tjo.galenos.2021.29726] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Objectives: To evaluate the performance of convolutional neural network (CNN) architectures to distinguish eyes with glaucoma from normal eyes. Materials and Methods: A total of 9,950 fundus photographs of 5,388 patients from the database of Eskişehir Osmangazi University Faculty of Medicine Ophthalmology Clinic were labelled as glaucoma, glaucoma suspect, or normal by three different experienced ophthalmologists. The categorized fundus photographs were evaluated using a state-of-the-art two-dimensional CNN and compared with deep residual networks (ResNet) and very deep neural networks (VGG). The accuracy, sensitivity, and specificity of glaucoma detection with the different algorithms were evaluated using a dataset of 238 normal and 320 glaucomatous fundus photographs. For the detection of suspected glaucoma, ResNet-101 architectures were tested with a data set of 170 normal, 170 glaucoma, and 167 glaucoma-suspect fundus photographs. Results: Accuracy, sensitivity, and specificity in detecting glaucoma were 96.2%, 99.5%, and 93.7% with ResNet-50; 97.4%, 97.8%, and 97.1% with ResNet-101; 98.9%, 100%, and 98.1% with VGG-19, and 99.4%, 100%, and 99% with the 2D CNN, respectively. Accuracy, sensitivity, and specificity values in distinguishing glaucoma suspects from normal eyes were 62%, 68%, and 56% and those for differentiating glaucoma from suspected glaucoma were 92%, 81%, and 97%, respectively. While 55 photographs could be evaluated in 2 seconds with CNN, a clinician spent an average of 24.2 seconds to evaluate a single photograph. Conclusion: An appropriately designed and trained CNN was able to distinguish glaucoma with high accuracy even with a small number of fundus photographs.
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Ran AR, Wang X, Chan PP, Chan NC, Yip W, Young AL, Wong MOM, Yung HW, Chang RT, Mannil SS, Tham YC, Cheng CY, Chen H, Li F, Zhang X, Heng PA, Tham CC, Cheung CY. Three-Dimensional Multi-Task Deep Learning Model to Detect Glaucomatous Optic Neuropathy and Myopic Features From Optical Coherence Tomography Scans: A Retrospective Multi-Centre Study. Front Med (Lausanne) 2022; 9:860574. [PMID: 35783623 PMCID: PMC9240220 DOI: 10.3389/fmed.2022.860574] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2022] [Accepted: 04/25/2022] [Indexed: 11/13/2022] Open
Abstract
PurposeWe aim to develop a multi-task three-dimensional (3D) deep learning (DL) model to detect glaucomatous optic neuropathy (GON) and myopic features (MF) simultaneously from spectral-domain optical coherence tomography (SDOCT) volumetric scans.MethodsEach volumetric scan was labelled as GON according to the criteria of retinal nerve fibre layer (RNFL) thinning, with a structural defect that correlated in position with the visual field defect (i.e., reference standard). MF were graded by the SDOCT en face images, defined as presence of peripapillary atrophy (PPA), optic disc tilting, or fundus tessellation. The multi-task DL model was developed by ResNet with output of Yes/No GON and Yes/No MF. SDOCT scans were collected in a tertiary eye hospital (Hong Kong SAR, China) for training (80%), tuning (10%), and internal validation (10%). External testing was performed on five independent datasets from eye centres in Hong Kong, the United States, and Singapore, respectively. For GON detection, we compared the model to the average RNFL thickness measurement generated from the SDOCT device. To investigate whether MF can affect the model’s performance on GON detection, we conducted subgroup analyses in groups stratified by Yes/No MF. The area under the receiver operating characteristic curve (AUROC), sensitivity, specificity, and accuracy were reported.ResultsA total of 8,151 SDOCT volumetric scans from 3,609 eyes were collected. For detecting GON, in the internal validation, the proposed 3D model had significantly higher AUROC (0.949 vs. 0.913, p < 0.001) than average RNFL thickness in discriminating GON from normal. In the external testing, the two approaches had comparable performance. In the subgroup analysis, the multi-task DL model performed significantly better in the group of “no MF” (0.883 vs. 0.965, p-value < 0.001) in one external testing dataset, but no significant difference in internal validation and other external testing datasets. The multi-task DL model’s performance to detect MF was also generalizable in all datasets, with the AUROC values ranging from 0.855 to 0.896.ConclusionThe proposed multi-task 3D DL model demonstrated high generalizability in all the datasets and the presence of MF did not affect the accuracy of GON detection generally.
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Affiliation(s)
- An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Lam Kin Chung. Jet King-Shing Ho Glaucoma Treatment and Research Centre, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Xi Wang
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, Palo Alto, CA, United States
| | - Poemen P. Chan
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Lam Kin Chung. Jet King-Shing Ho Glaucoma Treatment and Research Centre, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Hong Kong Eye Hospital, Hong Kong, Hong Kong SAR, China
| | - Noel C. Chan
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Ophthalmology, Prince of Wales Hospital, Hong Kong, Hong Kong SAR, China
- Department of Ophthalmology, Alice Ho Miu Ling Nethersole Hospital, Hong Kong, Hong Kong SAR, China
| | - Wilson Yip
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Ophthalmology, Prince of Wales Hospital, Hong Kong, Hong Kong SAR, China
- Department of Ophthalmology, Alice Ho Miu Ling Nethersole Hospital, Hong Kong, Hong Kong SAR, China
| | - Alvin L. Young
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Department of Ophthalmology, Prince of Wales Hospital, Hong Kong, Hong Kong SAR, China
| | - Mandy O. M. Wong
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Hong Kong Eye Hospital, Hong Kong, Hong Kong SAR, China
| | - Hon-Wah Yung
- Tuen Mun Eye Centre, Hong Kong, Hong Kong SAR, China
| | - Robert T. Chang
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, United States
| | - Suria S. Mannil
- Department of Ophthalmology, Byers Eye Institute, Stanford University, Palo Alto, CA, United States
| | - Yih Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Duke-National University of Singapore Medical School, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Hao Chen
- Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, Hong Kong SAR, China
| | - Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Centre, Sun Yat-sen University, Guangzhou, China
| | - Pheng-Ann Heng
- Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
| | - Clement C. Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Lam Kin Chung. Jet King-Shing Ho Glaucoma Treatment and Research Centre, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Hong Kong Eye Hospital, Hong Kong, Hong Kong SAR, China
| | - Carol Y. Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- Lam Kin Chung. Jet King-Shing Ho Glaucoma Treatment and Research Centre, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China
- *Correspondence: Carol Y. Cheung,
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Performance of Artificial Intelligence Models Designed for Diagnosis, Treatment Planning and Predicting Prognosis of Orthognathic Surgery (OGS)—A Scoping Review. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12115581] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
The technological advancements in the field of medical science have led to an escalation in the development of artificial intelligence (AI) applications, which are being extensively used in health sciences. This scoping review aims to outline the application and performance of artificial intelligence models used for diagnosing, treatment planning and predicting the prognosis of orthognathic surgery (OGS). Data for this paper was searched through renowned electronic databases such as PubMed, Google Scholar, Scopus, Web of science, Embase and Cochrane for articles related to the research topic that have been published between January 2000 and February 2022. Eighteen articles that met the eligibility criteria were critically analyzed based on QUADAS-2 guidelines and the certainty of evidence of the included studies was assessed using the GRADE approach. AI has been applied for predicting the post-operative facial profiles and facial symmetry, deciding on the need for OGS, predicting perioperative blood loss, planning OGS, segmentation of maxillofacial structures for OGS, and differential diagnosis of OGS. AI models have proven to be efficient and have outperformed the conventional methods. These models are reported to be reliable and reproducible, hence they can be very useful for less experienced practitioners in clinical decision making and in achieving better clinical outcomes.
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Kaskar OG, Wells-Gray E, Fleischman D, Grace L. Evaluating machine learning classifiers for glaucoma referral decision support in primary care settings. Sci Rep 2022; 12:8518. [PMID: 35595794 PMCID: PMC9122936 DOI: 10.1038/s41598-022-12270-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Accepted: 04/18/2022] [Indexed: 11/09/2022] Open
Abstract
Several artificial intelligence algorithms have been proposed to help diagnose glaucoma by analyzing the functional and/or structural changes in the eye. These algorithms require carefully curated datasets with access to ocular images. In the current study, we have modeled and evaluated classifiers to predict self-reported glaucoma using a single, easily obtained ocular feature (intraocular pressure (IOP)) and non-ocular features (age, gender, race, body mass index, systolic and diastolic blood pressure, and comorbidities). The classifiers were trained on publicly available data of 3015 subjects without a glaucoma diagnosis at the time of enrollment. 337 subjects subsequently self-reported a glaucoma diagnosis in a span of 1–12 years after enrollment. The classifiers were evaluated on the ability to identify these subjects by only using their features recorded at the time of enrollment. Support vector machine, logistic regression, and adaptive boosting performed similarly on the dataset with F1 scores of 0.31, 0.30, and 0.28, respectively. Logistic regression had the highest sensitivity at 60% with a specificity of 69%. Predictive classifiers using primarily non-ocular features have the potential to be used for identifying suspected glaucoma in non-eye care settings, including primary care. Further research into finding additional features that improve the performance of predictive classifiers is warranted.
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Affiliation(s)
- Omkar G Kaskar
- North Carolina State University, Raleigh, NC, 27695, USA
| | | | - David Fleischman
- University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Landon Grace
- North Carolina State University, Raleigh, NC, 27695, USA.
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