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Hallaj S, Chuter BG, Lieu AC, Singh P, Kalpathy-Cramer J, Xu BY, Christopher M, Zangwill LM, Weinreb RN, Baxter SL. Federated Learning in Glaucoma: A Comprehensive Review and Future Perspectives. Ophthalmol Glaucoma 2024:S2589-4196(24)00143-1. [PMID: 39214457 DOI: 10.1016/j.ogla.2024.08.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2024] [Revised: 08/20/2024] [Accepted: 08/23/2024] [Indexed: 09/04/2024]
Abstract
Current approaches to developing artificial intelligence (AI) models for widespread glaucoma screening have encountered several obstacles. First, glaucoma is a complex condition with a wide range of morphological and clinical presentations. There exists no consensus definition of glaucoma or glaucomatous optic neuropathy. Further, training effective deep learning algorithms poses numerous challenges, including susceptibility to overfitting and lack of generalizability on external data. Therefore, training data should ideally be sourced from large, well-curated, multi-client cohorts to ensure diversity in patient populations, disease presentations, and imaging protocols. However, the construction of centralized repositories for multimodal data faces hurdles such as concerns regarding data sharing, re-identification, storage, regulations, patient privacy, and intellectual property. Federated learning (FL) has emerged as a proposed solution to address some of these concerns by enabling data to remain locally hosted while facilitating distributed model training. This article aims to provide a comprehensive review of the existing literature on FL in the context of its applications for AI tasks related to glaucoma.
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Affiliation(s)
- Shahin Hallaj
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Benton G Chuter
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Alexander C Lieu
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA
| | - Praveer Singh
- Division of Artificial Medical Intelligence, Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA; Moorfields Eye Hospital NHS Foundation Trust, London, UK
| | - Jayashree Kalpathy-Cramer
- Division of Artificial Medical Intelligence, Department of Ophthalmology, University of Colorado School of Medicine, Aurora, CO, USA
| | - Benjamin Y Xu
- Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Mark Christopher
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA
| | - Linda M Zangwill
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA
| | - Robert N Weinreb
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA
| | - Sally L Baxter
- Division of Ophthalmology Informatics and Data Science, Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA 92037, USA; Division of Biomedical Informatics, Department of Medicine, University of California San Diego, La Jolla, CA 92037, USA.
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2
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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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3
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Shi M, Lokhande A, Tian Y, Luo Y, Eslami M, Kazeminasab S, Elze T, Shen LQ, Pasquale LR, Wellik SR, De Moraes CG, Myers JS, Zebardast N, Friedman DS, Boland MV, Wang M. Transformer-Based Deep Learning Prediction of 10-Degree Humphrey Visual Field Tests From 24-Degree Data. Transl Vis Sci Technol 2024; 13:11. [PMID: 39110574 PMCID: PMC11316452 DOI: 10.1167/tvst.13.8.11] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 06/20/2024] [Indexed: 08/12/2024] Open
Abstract
Purpose To predict 10-2 Humphrey visual fields (VFs) from 24-2 VFs and associated non-total deviation features using deep learning. Methods We included 5189 reliable 24-2 and 10-2 VF pairs from 2236 patients, and 28,409 reliable pairs of macular OCT scans and 24-2 VF from 19,527 eyes of 11,560 patients. We developed a transformer-based deep learning model using 52 total deviation values and nine VF test features to predict 68 10-2 total deviation values. The mean absolute error, root mean square error, and the R2 were evaluation metrics. We further evaluated whether the predicted 10-2 VFs can improve the structure-function relationship between macular thinning and paracentral VF loss in glaucoma. Results The average mean absolute error and R2 for 68 10-2 VF test points were 3.30 ± 0.52 dB and 0.70 ± 0.11, respectively. The accuracy was lower in the inferior temporal region. The model placed greater emphasis on 24-2 VF points near the central fixation point when predicting the 10-2 VFs. The inclusion of nine VF test features improved the mean absolute error and R2 up to 0.17 ± 0.06 dB and 0.01 ± 0.01, respectively. Age was the most important 24-2 VF test parameter for 10-2 VF prediction. The predicted 10-2 VFs achieved an improved structure-function relationship between macular thinning and paracentral VF loss, with the R2 at the central 4, 12, and 16 locations of 24-2 VFs increased by 0.04, 0.05 and 0.05, respectively (P < 0.001). Conclusions The 10-2 VFs may be predicted from 24-2 data. Translational Relevance The predicted 10-2 VF has the potential to improve glaucoma diagnosis.
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Affiliation(s)
- Min Shi
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Anagha Lokhande
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Yu Tian
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Yan Luo
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Mohammad Eslami
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Saber Kazeminasab
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Tobias Elze
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Lucy Q. Shen
- Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | - Louis R. Pasquale
- Eye and Vision Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sarah R. Wellik
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL, USA
| | | | - Jonathan S. Myers
- Wills Eye Hospital, Thomas Jefferson University, Philadelphia, PA, USA
| | - Nazlee Zebardast
- Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
| | | | | | - Mengyu Wang
- Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Massachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA
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4
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Huang X, Islam MR, Akter S, Ahmed F, Kazami E, Serhan HA, Abd-Alrazaq A, Yousefi S. Artificial intelligence in glaucoma: opportunities, challenges, and future directions. Biomed Eng Online 2023; 22:126. [PMID: 38102597 PMCID: PMC10725017 DOI: 10.1186/s12938-023-01187-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/01/2023] [Indexed: 12/17/2023] Open
Abstract
Artificial intelligence (AI) has shown excellent diagnostic performance in detecting various complex problems related to many areas of healthcare including ophthalmology. AI diagnostic systems developed from fundus images have become state-of-the-art tools in diagnosing retinal conditions and glaucoma as well as other ocular diseases. However, designing and implementing AI models using large imaging data is challenging. In this study, we review different machine learning (ML) and deep learning (DL) techniques applied to multiple modalities of retinal data, such as fundus images and visual fields for glaucoma detection, progression assessment, staging and so on. We summarize findings and provide several taxonomies to help the reader understand the evolution of conventional and emerging AI models in glaucoma. We discuss opportunities and challenges facing AI application in glaucoma and highlight some key themes from the existing literature that may help to explore future studies. Our goal in this systematic review is to help readers and researchers to understand critical aspects of AI related to glaucoma as well as determine the necessary steps and requirements for the successful development of AI models in glaucoma.
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Affiliation(s)
- Xiaoqin Huang
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA
| | - Md Rafiqul Islam
- Business Information Systems, Australian Institute of Higher Education, Sydney, Australia
| | - Shanjita Akter
- School of Computer Science, Taylors University, Subang Jaya, Malaysia
| | - Fuad Ahmed
- Department of Computer Science & Engineering, Islamic University of Technology (IUT), Gazipur, Bangladesh
| | - Ehsan Kazami
- Ophthalmology, General Hospital of Mahabad, Urmia University of Medical Sciences, Urmia, Iran
| | - Hashem Abu Serhan
- Department of Ophthalmology, Hamad Medical Corporations, Doha, Qatar
| | - Alaa Abd-Alrazaq
- AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Siamak Yousefi
- Department of Ophthalmology, University of Tennessee Health Science Center, Memphis, USA.
- Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, USA.
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5
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Mohammadzadeh V, Vepa A, Li C, Wu S, Chew L, Mahmoudinezhad G, Maltz E, Sahin S, Mylavarapu A, Edalati K, Martinyan J, Yalzadeh D, Scalzo F, Caprioli J, Nouri-Mahdavi K. Prediction of Central Visual Field Measures From Macular OCT Volume Scans With Deep Learning. Transl Vis Sci Technol 2023; 12:5. [PMID: 37917086 PMCID: PMC10627306 DOI: 10.1167/tvst.12.11.5] [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: 03/04/2023] [Accepted: 09/15/2023] [Indexed: 11/03/2023] Open
Abstract
Purpose Predict central 10° global and local visual field (VF) measurements from macular optical coherence tomography (OCT) volume scans with deep learning (DL). Methods This study included 1121 OCT volume scans and 10-2 VFs from 289 eyes (257 patients). Macular scans were used to estimate 10-2 VF mean deviation (MD), threshold sensitivities (TS), and total deviation (TD) values at 68 locations. A three-dimensional (3D) convolutional neural network based on the 3D DenseNet121 architecture was used for prediction. We compared DL predictions to those from baseline linear models. We carried out 10-fold stratified cross-validation to optimize generalizability. The performance of the DL and baseline models was compared based on correlations between ground truth and predicted VF measures and mean absolute error (MAE; ground truth - predicted values). Results Average (SD) MD was -9.3 (7.7) dB. Average (SD) correlations between predicted and ground truth MD and MD MAE were 0.74 (0.09) and 3.5 (0.4) dB, respectively. Estimation accuracy deteriorated with worsening MD. Average (SD) Pearson correlations between predicted and ground truth TS and MAEs for DL and baseline model were 0.71 (0.05) and 0.52 (0.05) (P < 0.001) and 6.5 (0.6) and 7.5 (0.5) dB (P < 0.001), respectively. For TD, correlation (SD) and MAE (SD) for DL and baseline models were 0.69 (0.02) and 0.48 (0.05) (P < 0.001) and 6.1 (0.5) and 7.8 (0.5) dB (P < 0.001), respectively. Conclusions Macular OCT volume scans can be used to predict global central VF parameters with clinically relevant accuracy. Translational Relevance Macular OCT imaging may be used to confirm and supplement central VF findings using deep learning.
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Affiliation(s)
- Vahid Mohammadzadeh
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Arvind Vepa
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Chuanlong Li
- Department of Neurology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Sean Wu
- Department of Computer Science, Pepperdine University, Malibu, CA, USA
| | - Leila Chew
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Golnoush Mahmoudinezhad
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Evan Maltz
- Department of Chemistry and Biochemistry, University of California Los Angeles, Los Angeles, CA, USA
| | - Serhat Sahin
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Apoorva Mylavarapu
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Kiumars Edalati
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Jack Martinyan
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Dariush Yalzadeh
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Fabien Scalzo
- Department of Computer Science, University of California Los Angeles, Los Angeles, CA, USA
| | - Joseph Caprioli
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Kouros Nouri-Mahdavi
- Glaucoma Division, Stein Eye Institute, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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6
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Asaoka R, Murata H. Prediction of visual field progression in glaucoma: existing methods and artificial intelligence. Jpn J Ophthalmol 2023; 67:546-559. [PMID: 37540325 DOI: 10.1007/s10384-023-01009-3] [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: 12/26/2022] [Accepted: 04/13/2023] [Indexed: 08/05/2023]
Abstract
Timely treatment is essential in the management of glaucoma. However, subjective assessment of visual field (VF) progression is not recommended, because it can be unreliable. There are two types of artificial intelligence (AI) strong and weak (machine learning). Weak AIs can perform specific tasks. Linear regression is a method of weak AI. Using linear regression in the real-world clinic has enabled analyzing and predicting VF progression. However, caution is still required when interpreting the results, because whenever the number of VF data sets investigated is small, the predictions can be inaccurate. Several other non-ordinal, or modern AI methods have been constructed to improve prediction accuracy, such as clustering and more modern AI methods of Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement (ANSWERS), Variational Bayes Linear Regression (VBLR), Kalman Filter and sparse modeling (The least absolute shrinkage and selection operator regression: Lasso). It is also possible to improve the prediction performance using retinal thickness measured with optical coherence tomography by using machine learning methods, such as multitask learning.
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Grants
- 19H01114 ministry of education, culture, sports, science, and technology of Japan
- 18KK0253 ministry of education, culture, sports, science and technology of Japan
- 20K09784 ministry of education, culture, sports, science and technology of Japan
- 80635748 ministry of education, culture, sports, science and technology of Japan
- TR-SPRINT japan agency for medical reserach and development
- Grant the Japan Glaucoma Society Project Support Program
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Affiliation(s)
- Ryo Asaoka
- Department of Ophthalmology, Seirei Hamamatsu General Hospital, 2-12-12 Sumiyoshi, Naka-ku, Hamamatsu, Shizuoka, Japan.
- Seirei Christopher University, Hamamatsu, Shizuoka, Japan.
- The Graduate School for the Creation of New Photonics Industries, Hamamatsu, Shizuoka, Japan.
| | - Hiroshi Murata
- Department of Ophthalmology, National Center for Global health and Medicine, Tokyo, Japan
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7
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Kamalipour A, Moghimi S, Khosravi P, Jazayeri MS, Nishida T, Mahmoudinezhad G, Li EH, Christopher M, Liebmann JM, Fazio MA, Girkin CA, Zangwill L, Weinreb RN. Deep Learning Estimation of 10-2 Visual Field Map Based on Circumpapillary Retinal Nerve Fiber Layer Thickness Measurements. Am J Ophthalmol 2023; 246:163-173. [PMID: 36328198 DOI: 10.1016/j.ajo.2022.10.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2022] [Revised: 10/14/2022] [Accepted: 10/20/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE To estimate central 10-degree visual field (VF) map from spectral-domain optical coherence tomography (SD-OCT) retinal nerve fiber layer thickness (RNFL) measurements in glaucoma with artificial intelligence. DESIGN Artificial intelligence (convolutional neural networks) study. METHODS This study included 5352 SD-OCT scans and 10-2 VF pairs from 1365 eyes of 724 healthy patients, patients with suspected glaucoma, and patients with glaucoma. Convolutional neural networks (CNNs) were developed to estimate the 68 individual sensitivity thresholds of 10-2 VF map using all-sectors (CNNA) and temporal-sectors (CNNT) RNFL thickness information of the SD-OCT circle scan (768 thickness points). 10-2 indices including pointwise total deviation (TD) values, mean deviation (MD), and pattern standard deviation (PSD) were generated using the CNN-estimated sensitivity thresholds at individual test locations. Linear regression (LR) models with the same input were used for comparison. RESULTS The CNNA model achieved an average pointwise mean absolute error of 4.04 dB (95% confidence interval [CI] 3.76-4.35) and correlation coefficient (r) of 0.59 (95% CI 0.52-0.64) over 10-2 map and the mean absolute error and r of 2.88 dB (95% CI 2.63-3.15) and 0.74 (95% CI 0.67-0.80) for MD, and 2.31 dB (95% CI 2.03-2.61) and 0.59 (95% CI 0.51-0.65) for PSD estimations, respectively, significantly outperforming the LRA model. CONCLUSIONS The proposed CNNA model improved the estimation of 10-2 VF map based on circumpapillary SD-OCT RNFL thickness measurements. These artificial intelligence methods using SD-OCT structural data show promise to individualize the frequency of central VF assessment in patients with glaucoma and would enable the reallocation of resources from patients at lowest risk to those at highest risk of central VF damage.
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Affiliation(s)
- Alireza Kamalipour
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Sasan Moghimi
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Pooya Khosravi
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Mohammad Sadegh Jazayeri
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Takashi Nishida
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Golnoush Mahmoudinezhad
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Elizabeth H Li
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Mark Christopher
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Jeffrey M Liebmann
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Massimo A Fazio
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Christopher A Girkin
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Linda Zangwill
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - Robert N Weinreb
- From the Hamilton Glaucoma Center (A.K., S.M., T.N., G.M., E.H.L., M.C., M.A.F., L.Z., R.N.W.),; Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California San Diego, La Jolla; School of Medicine (P.K.),; University of California Irvine, Irvine; Department of Civil, Construction, and Environmental Engineering (M.S.J.),; San Diego State University, San Diego, California; Bernard and Shirlee Brown Glaucoma Research Laboratory (J.M.L.),; Department of Ophthalmology, Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York; and the Department of Ophthalmology and Vision Sciences (M.A.F., C.A.G.),; Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, Alabama, USA..
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Moon S, Lee JH, Choi H, Lee SY, Lee J. Deep learning approaches to predict 10-2 visual field from wide-field swept-source optical coherence tomography en face images in glaucoma. Sci Rep 2022; 12:21041. [PMID: 36471039 PMCID: PMC9722778 DOI: 10.1038/s41598-022-25660-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Accepted: 12/02/2022] [Indexed: 12/12/2022] Open
Abstract
Close monitoring of central visual field (VF) defects with 10-2 VF helps prevent blindness in glaucoma. We aimed to develop a deep learning model to predict 10-2 VF from wide-field swept-source optical coherence tomography (SS-OCT) images. Macular ganglion cell/inner plexiform layer thickness maps with either wide-field en face images (en face model) or retinal nerve fiber layer thickness maps (RNFLT model) were extracted, combined, and preprocessed. Inception-ResNet-V2 was trained to predict 10-2 VF from combined images. Estimation performance was evaluated using mean absolute error (MAE) between actual and predicted threshold values, and the two models were compared with different input data. The training dataset comprised paired 10-2 VF and SS-OCT images of 3,025 eyes of 1,612 participants and the test dataset of 337 eyes of 186 participants. Global prediction errors (MAEpoint-wise) were 3.10 and 3.17 dB for the en face and RNFLT models, respectively. The en face model performed better than the RNFLT model in superonasal and inferonasal sectors (P = 0.011 and P = 0.030). Prediction errors were smaller in the inferior versus superior hemifields for both models. The deep learning model effectively predicted 10-2 VF from wide-field SS-OCT images and might help clinicians efficiently individualize the frequency of 10-2 VF in clinical practice.
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Affiliation(s)
- Sangwoo Moon
- grid.262229.f0000 0001 0719 8572Department of Ophthalmology, Pusan National University College of Medicine, Busan, 49241 Korea ,grid.412588.20000 0000 8611 7824Biomedical Research Institute, Pusan National University Hospital, Busan, 49241 Korea
| | - Jae Hyeok Lee
- Department of Medical AI, Deepnoid Inc, Seoul, 08376 Korea
| | - Hyunju Choi
- Department of Medical AI, Deepnoid Inc, Seoul, 08376 Korea
| | - Sun Yeop Lee
- Department of Medical AI, Deepnoid Inc, Seoul, 08376 Korea
| | - Jiwoong Lee
- grid.262229.f0000 0001 0719 8572Department of Ophthalmology, Pusan National University College of Medicine, Busan, 49241 Korea ,grid.412588.20000 0000 8611 7824Biomedical Research Institute, Pusan National University Hospital, Busan, 49241 Korea
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9
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Association of macular structure, function, and vessel density with foveal threshold in advanced glaucoma. Sci Rep 2022; 12:19771. [PMID: 36396716 PMCID: PMC9671888 DOI: 10.1038/s41598-022-24129-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/10/2022] [Indexed: 11/18/2022] Open
Abstract
Identifying new biomarkers associated with central visual function impairment is important in advanced glaucoma patients. This retrospective cross-sectional study enrolled 154 eyes from 154 subjects, consisting of 86 patients with advanced open-angle glaucoma (mean deviation of 24-2 visual field [VF] tests < - 15 dB) and 68 healthy controls. Structure, function, and vessel density (VD) parameters were obtained using optical coherence tomography (OCT), 24-2 standard automated perimetry, and OCT angiography, respectively. The relationships of macular thickness, central 5° and 10° VF mean sensitivity (MS), and macular VD parameters with foveal threshold (FT), representing central visual function, were investigated using partial correlation analyses and linear regression analyses, with age adjustment. Superficial and deep layer macular VD, central 5° and 10° VF MS, and best corrected visual acuity (BCVA) correlated significantly with FT after age adjustment (P < 0.05). In multivariate linear regression analyses, FT associated significantly with BCVA (β = - 8.80, P < 0.001), central 5° MS (β = 0.30, P = 0.037), and deep-layer global parafoveal VD (β = 0.37, P = 0.037). Thus, deep-layer parafoveal VD is an independent predictor of FT and may be a potential biomarker for central visual function in advanced glaucoma.
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Alexopoulos P, Madu C, Wollstein G, Schuman JS. The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques. Front Med (Lausanne) 2022; 9:891369. [PMID: 35847772 PMCID: PMC9279625 DOI: 10.3389/fmed.2022.891369] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2022] [Accepted: 05/23/2022] [Indexed: 11/22/2022] Open
Abstract
The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.
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Affiliation(s)
- Palaiologos Alexopoulos
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Chisom Madu
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
| | - Joel S. Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY, United States
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
- Center for Neural Science, College of Arts & Science, New York University, New York, NY, United States
- Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, Brooklyn, NY, United States
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