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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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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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Asaoka R, Sugisaki K, Inoue T, Yoshikawa K, Kanamori A, Yamazaki Y, Ishikawa S, Uchida K, Iwase A, Araie M. Predicting the Extent of Damage in the Humphrey Field Analyzer 24-2 Visual Fields Using 10-2 Test Results in Patients With Advanced Glaucoma. Transl Vis Sci Technol 2024; 13:2. [PMID: 38306105 PMCID: PMC10851172 DOI: 10.1167/tvst.13.2.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Accepted: 12/23/2023] [Indexed: 02/03/2024] Open
Abstract
Purpose To predict Humphrey Field Analyzer 24-2 test (HFA 24-2) results using 10-2 results. Methods A total of 175 advanced glaucoma eyes (175 patients) with HFA 24-2 mean deviation (MD24-2) of < -20 dB were prospectively followed up for five years using HFA 10-2 and 24-2 (twice and once in a year, respectively). Using all the HFA 24-2 and 10-2 test result pairs measured within three months (350 pairs from 85 eyes, training dataset), a formula to predict HFA 24-2 result using HFA 10-2 results was constructed using least absolute shrinkage and selection operator regression (LASSO). Using 90 different eyes (testing dataset), the absolute differences between the actual and LASSO-predicted MD24-2 and that between the slopes calculated using five actual and LASSO-predicted MD24-2 values, were adopted as the prediction error. Similar analyses were performed for the mean total deviation values (mTD) of the superior (or inferior) hemifield [hemi-mTDsup.24-2(-hemi-mTDinf.24-2)]. Results The prediction error for the LASSO-predicted MD24-2 and its slope were 2.98 (standard deviation [SD] = 1.90) dB and 0.32 (0.33) dB/yr, respectively. The LASSO-predicted hemi-mTDsup.24-2 (hemi-mTDinf.24-2), and its slope were 3.02 (2.89) and 3.76 (2.72) dB, and 0.37 (0.41) and 0.44 (0.38) dB/year, respectively. These prediction errors were within two times SD of repeatability of the simulated stable HFA 24-2 VF parameter series. Conclusions HFA 24-2 results could be predicted using the paired HFA 10-2 results with reasonable accuracy using LASSO in patients with advanced glaucoma. Translational Relevance It is useful to predict HFA24-2 test from HFA10-2 test, when the former is not available, in advanced glaucoma.
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Affiliation(s)
- Ryo Asaoka
- Department of Ophthalmology, Seirei Hamamatsu General Hospital, Hamamatsu, Shizuoka, Japan
- Seirei Christopher University, Hamamatsu, Shizuoka, Japan
- The Graduate School for the Creation of New Photonics Industries, Shizuoka, Japan
- Organization for Innovation and Social Collaboration, National University Corporation Shizuoka University, Hamamatsu, Shizuoka, Japan
| | - Kenji Sugisaki
- Department of Ophthalmology, International University of Health and Welfare, Mita Hospital, Tokyo, Japan
- Department of Ophthalmology, University of Tokyo Graduate School of Medicine, Tokyo, Japan
| | - Toshihiro Inoue
- Department of Ophthalmology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
| | | | - Akiyasu Kanamori
- Department of Surgery, Division of Ophthalmology, Kobe University Graduate School of Medicine, Kobe, Japan
| | | | - Shinichiro Ishikawa
- Department of Ophthalmology, Saga University Faculty of Medicine, Saga, Japan
| | | | | | - Makoto Araie
- Department of Ophthalmology, University of Tokyo Graduate School of Medicine, Tokyo, Japan
- Kanagawa Dental University, Yokohama Clinic, Yokohama, Japan
| | - for Advanced Glaucoma Study Members in Japan Glaucoma Society
- Department of Ophthalmology, Seirei Hamamatsu General Hospital, Hamamatsu, Shizuoka, Japan
- Seirei Christopher University, Hamamatsu, Shizuoka, Japan
- The Graduate School for the Creation of New Photonics Industries, Shizuoka, Japan
- Organization for Innovation and Social Collaboration, National University Corporation Shizuoka University, Hamamatsu, Shizuoka, Japan
- Department of Ophthalmology, International University of Health and Welfare, Mita Hospital, Tokyo, Japan
- Department of Ophthalmology, University of Tokyo Graduate School of Medicine, Tokyo, Japan
- Department of Ophthalmology, Faculty of Life Sciences, Kumamoto University, Kumamoto, Japan
- Yoshikawa Eye Clinic, Machida, Japan
- Department of Surgery, Division of Ophthalmology, Kobe University Graduate School of Medicine, Kobe, Japan
- Yamazaki Eye Clinic, Tokyo, Japan
- Department of Ophthalmology, Saga University Faculty of Medicine, Saga, Japan
- Tokyo Kyosai Hospital, Tokyo, Japan
- Tajimi Iwase Eye Clinic, Tajimi, Japan
- Kanagawa Dental University, Yokohama Clinic, Yokohama, Japan
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Mahmoudinezhad G, Moghimi S, Cheng J, Ru L, Yang D, Agrawal K, Dixit R, Beheshtaein S, Du KH, Latif K, Gunasegaran G, Micheletti E, Nishida T, Kamalipour A, Walker E, Christopher M, Zangwill L, Vasconcelos N, Weinreb RN. Deep Learning Estimation of 10-2 Visual Field Map Based on Macular Optical Coherence Tomography Angiography Measurements. Am J Ophthalmol 2024; 257:187-200. [PMID: 37734638 DOI: 10.1016/j.ajo.2023.09.014] [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: 03/29/2023] [Revised: 09/07/2023] [Accepted: 09/13/2023] [Indexed: 09/23/2023]
Abstract
PURPOSE To develop deep learning (DL) models estimating the central visual field (VF) from optical coherence tomography angiography (OCTA) vessel density (VD) measurements. DESIGN Development and validation of a deep learning model. METHODS A total of 1051 10-2 VF OCTA pairs from healthy, glaucoma suspects, and glaucoma eyes were included. DL models were trained on en face macula VD images from OCTA to estimate 10-2 mean deviation (MD), pattern standard deviation (PSD), 68 total deviation (TD) and pattern deviation (PD) values and compared with a linear regression (LR) model with the same input. Accuracy of the models was evaluated by calculating the average mean absolute error (MAE) and the R2 (squared Pearson correlation coefficients) of the estimated and actual VF values. RESULTS DL models predicting 10-2 MD achieved R2 of 0.85 (95% confidence interval [CI], 74-0.92) for 10-2 MD and MAEs of 1.76 dB (95% CI, 1.39-2.17 dB) for MD. This was significantly better than mean linear estimates for 10-2 MD. The DL model outperformed the LR model for the estimation of pointwise TD values with an average MAE of 2.48 dB (95% CI, 1.99-3.02) and R2 of 0.69 (95% CI, 0.57-0.76) over all test points. The DL model outperformed the LR model for the estimation of all sectors. CONCLUSIONS DL models enable the estimation of VF loss from OCTA images with high accuracy. Applying DL to the OCTA images may enhance clinical decision making. It also may improve individualized patient care and risk stratification of patients who are at risk for central VF damage.
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Affiliation(s)
- Golnoush Mahmoudinezhad
- From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California
| | - Sasan Moghimi
- From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California
| | - Jiacheng Cheng
- Department of Electrical and Computer Engineering (J.C., L.R., K.A., R.D., N.V.), University of California San Diego, La Jolla, California
| | - Liyang Ru
- Department of Electrical and Computer Engineering (J.C., L.R., K.A., R.D., N.V.), University of California San Diego, La Jolla, California
| | - Dongchen Yang
- Department of Computer Science and Engineering (D.Y.), University of California San Diego, La Jolla, California
| | - Kushagra Agrawal
- Department of Electrical and Computer Engineering (J.C., L.R., K.A., R.D., N.V.), University of California San Diego, La Jolla, California
| | - Rajeev Dixit
- Department of Electrical and Computer Engineering (J.C., L.R., K.A., R.D., N.V.), University of California San Diego, La Jolla, California
| | | | - Kelvin H Du
- From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California
| | - Kareem Latif
- From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California
| | - Gopikasree Gunasegaran
- From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California
| | - Eleonora Micheletti
- From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California
| | - Takashi Nishida
- From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California
| | - Alireza Kamalipour
- From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California
| | - Evan Walker
- From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California
| | - Mark Christopher
- From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California
| | - Linda Zangwill
- From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California
| | - Nuno Vasconcelos
- Department of Electrical and Computer Engineering (J.C., L.R., K.A., R.D., N.V.), University of California San Diego, La Jolla, California
| | - Robert N Weinreb
- From the Hamilton Glaucoma Center (G.M., S.M., K.H.D., K.L., G.G., E.M., T.N., A.K., E.W., M.C., L.Z., R.N.W.), Shiley Eye Institute, Viterbi Family Department of Ophthalmology, UC San Diego, La Jolla, California.
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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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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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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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The number of examinations required for the accurate prediction of the progression of the central 10-degree visual field test in glaucoma. Sci Rep 2022; 12:18843. [PMID: 36344722 PMCID: PMC9640563 DOI: 10.1038/s41598-022-23604-z] [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: 12/02/2021] [Accepted: 11/02/2022] [Indexed: 11/09/2022] Open
Abstract
The purpose of the study was to investigate the number of examinations required to precisely predict the future central 10-degree visual field (VF) test and to evaluate the effect of fitting non-linear models, including quadratic regression, exponential regression, logistic regression, and M-estimator robust regression model, for eyes with glaucoma. 180 eyes from 133 open angle glaucoma patients with a minimum of 13 Humphrey Field Analyzer 10-2 SITA standard VF tests were analyzed in this study. Using trend analysis with ordinary least squares linear regression (OLSLR), the first, second, and third future VFs were predicted in a point-wise (PW) manner using a varied number of prior VF sequences, and mean absolute errors (MAE) were calculated. The number of VFs needed to reach the minimum 95% confidence interval (CI) of the MAE of the OLSLR was investigated. We also examined the effect of applying other non-linear models. When predicting the first, second, and third future VFs using OLSLR, the minimum MAE was obtained using VF1-12 (2.15 ± 0.98 dB), VF1-11 (2.33 ± 1.10 dB), and VF1-10 (2.63 ± 1.36 dB), respectively. To reach the 95% CI of these MAEs, 10, 10, and 8 VFs were needed for the first, second and third future VF predictions, respectively. No improvement was observed by applying non-linear regression models. As a conclusion, approximately 8-10 VFs were needed to achieve an accurate prediction of PW VF sensitivity of the 10-degree central VF.
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Shamsi F, Liu R, Owsley C, Kwon M. Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning. Invest Ophthalmol Vis Sci 2022; 63:27. [PMID: 35179554 PMCID: PMC8859491 DOI: 10.1167/iovs.63.2.27] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2021] [Accepted: 01/31/2022] [Indexed: 12/18/2022] Open
Abstract
Purpose Luminance contrast is the fundamental building block of human spatial vision. Therefore contrast sensitivity, the reciprocal of contrast threshold required for target detection, has been a barometer of human visual function. Although retinal ganglion cells (RGCs) are known to be involved in contrast coding, it still remains unknown whether the retinal layers containing RGCs are linked to a person's contrast sensitivity (e.g., Pelli-Robson contrast sensitivity) and, if so, to what extent the retinal layers are related to behavioral contrast sensitivity. Thus the current study aims to identify the retinal layers and features critical for predicting a person's contrast sensitivity via deep learning. Methods Data were collected from 225 subjects including individuals with either glaucoma, age-related macular degeneration, or normal vision. A deep convolutional neural network trained to predict a person's Pelli-Robson contrast sensitivity from structural retinal images measured with optical coherence tomography was used. Then, activation maps that represent the critical features learned by the network for the output prediction were computed. Results The thickness of both ganglion cell and inner plexiform layers, reflecting RGC counts, were found to be significantly correlated with contrast sensitivity (r = 0.26 ∼ 0.58, Ps < 0.001 for different eccentricities). Importantly, the results showed that retinal layers containing RGCs were the critical features the network uses to predict a person's contrast sensitivity (an average R2 = 0.36 ± 0.10). Conclusions The findings confirmed the structure and function relationship for contrast sensitivity while highlighting the role of RGC density for human contrast sensitivity.
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Affiliation(s)
- Foroogh Shamsi
- Department of Psychology, Northeastern University, Boston, Massachusetts, United States
| | - Rong Liu
- Department of Psychology, Northeastern University, Boston, Massachusetts, United States
- Department of Ophthalmology and Visual Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States
- Department of life science and medicine, University of Science and Technology of China, Hefei, China
| | - Cynthia Owsley
- Department of Ophthalmology and Visual Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States
| | - MiYoung Kwon
- Department of Psychology, Northeastern University, Boston, Massachusetts, United States
- Department of Ophthalmology and Visual Sciences, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, Alabama, United States
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Bunod R, Augstburger E, Brasnu E, Labbe A, Baudouin C. [Artificial intelligence and glaucoma: A literature review]. J Fr Ophtalmol 2022; 45:216-232. [PMID: 34991909 DOI: 10.1016/j.jfo.2021.11.002] [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: 11/01/2021] [Accepted: 11/18/2021] [Indexed: 11/26/2022]
Abstract
In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions.
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Affiliation(s)
- R Bunod
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France.
| | - E Augstburger
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France
| | - E Brasnu
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France
| | - A Labbe
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France; Service d'ophtalmologie, hôpital Ambroise-Paré, AP-HP, université de Paris Saclay, 9, avenue Charles-de-Gaulle, 92100 Boulogne-Billancourt, France
| | - C Baudouin
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France; Service d'ophtalmologie, hôpital Ambroise-Paré, AP-HP, université de Paris Saclay, 9, avenue Charles-de-Gaulle, 92100 Boulogne-Billancourt, France
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Asaoka R, Xu L, Murata H, Kiwaki T, Matsuura M, Fujino Y, Tanito M, Mori K, Ikeda Y, Kanamoto T, Inoue K, Yamagami J, Yamanishi K. A Joint Multitask Learning Model for Cross-sectional and Longitudinal Predictions of Visual Field Using OCT. OPHTHALMOLOGY SCIENCE 2021; 1:100055. [PMID: 36246943 PMCID: PMC9560642 DOI: 10.1016/j.xops.2021.100055] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Revised: 08/03/2021] [Accepted: 08/30/2021] [Indexed: 12/12/2022]
Abstract
Purpose We constructed a multitask learning model (latent space linear regression and deep learning [LSLR-DL]) in which the 2 tasks of cross-sectional predictions (using OCT) of visual field (VF; central 10°) and longitudinal progression predictions of VF (30°) were performed jointly via sharing the deep learning (DL) component such that information from both tasks was used in an auxiliary manner (The Association for Computing Machinery's Special Interest Group on Knowledge Discovery and Data Mining [SIGKDD] 2021). The purpose of the current study was to investigate the prediction accuracy preparing an independent validation dataset. Design Cohort study. Participants Cross-sectional training and testing data sets included the VF (Humphrey Field Analyzer [HFA] 10-2 test) and an OCT measurement (obtained within 6 months) from 591 eyes of 351 healthy people or patients with open-angle glaucoma (OAG) and from 155 eyes of 131 patients with OAG, respectively. Longitudinal training and testing data sets included 7984 VF results (HFA 24-2 test) from 998 eyes of 592 patients with OAG and 1184 VF results (HFA 24-2 test) from 148 eyes of 84 patients with OAG, respectively. Each eye had 8 VF test results (HFA 24-2 test). The OCT sequences within the observation period were used. Methods Root mean square error (RMSE) was used to evaluate the accuracy of LSLR-DL for the cross-sectional prediction of VF (HFA 10-2 test). For the longitudinal prediction, the final (eighth) VF test (HFA 24-2 test) was predicted using a shorter VF series and relevant OCT images, and the RMSE was calculated. For comparison, RMSE values were calculated by applying the DL component (cross-sectional prediction) and the ordinary pointwise linear regression (longitudinal prediction). Main Outcome Measures Root mean square error in the cross-sectional and longitudinal predictions. Results Using LSLR-DL, the mean RMSE in the cross-sectional prediction was 6.4 dB and was between 4.4 dB (VF tests 1 and 2) and 3.7 dB (VF tests 1–7) in the longitudinal prediction, indicating that LSLR-DL significantly outperformed other methods. Conclusions The results of this study indicate that LSLR-DL is useful for both the cross-sectional prediction of VF (HFA 10-2 test) and the longitudinal progression prediction of VF (HFA 24-2 test).
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Christopher M, Bowd C, Proudfoot JA, Belghith A, Goldbaum MH, Rezapour J, Fazio MA, Girkin CA, De Moraes G, Liebmann JM, Weinreb RN, Zangwill LM. Deep Learning Estimation of 10-2 and 24-2 Visual Field Metrics Based on Thickness Maps from Macula OCT. Ophthalmology 2021; 128:1534-1548. [PMID: 33901527 DOI: 10.1016/j.ophtha.2021.04.022] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 03/16/2021] [Accepted: 04/19/2021] [Indexed: 01/27/2023] Open
Abstract
PURPOSE To develop deep learning (DL) systems estimating visual function from macula-centered spectral-domain (SD) OCT images. DESIGN Evaluation of a diagnostic technology. PARTICIPANTS A total of 2408 10-2 visual field (VF) SD OCT pairs and 2999 24-2 VF SD OCT pairs collected from 645 healthy and glaucoma subjects (1222 eyes). METHODS Deep learning models were trained on thickness maps from Spectralis macula SD OCT to estimate 10-2 and 24-2 VF mean deviation (MD) and pattern standard deviation (PSD). Individual and combined DL models were trained using thickness data from 6 layers (retinal nerve fiber layer [RNFL], ganglion cell layer [GCL], inner plexiform layer [IPL], ganglion cell-IPL [GCIPL], ganglion cell complex [GCC] and retina). Linear regression of mean layer thicknesses were used for comparison. MAIN OUTCOME MEASURES Deep learning models were evaluated using R2 and mean absolute error (MAE) compared with 10-2 and 24-2 VF measurements. RESULTS Combined DL models estimating 10-2 achieved R2 of 0.82 (95% confidence interval [CI], 0.68-0.89) for MD and 0.69 (95% CI, 0.55-0.81) for PSD and MAEs of 1.9 dB (95% CI, 1.6-2.4 dB) for MD and 1.5 dB (95% CI, 1.2-1.9 dB) for PSD. This was significantly better than mean thickness estimates for 10-2 MD (0.61 [95% CI, 0.47-0.71] and 3.0 dB [95% CI, 2.5-3.5 dB]) and 10-2 PSD (0.46 [95% CI, 0.31-0.60] and 2.3 dB [95% CI, 1.8-2.7 dB]). Combined DL models estimating 24-2 achieved R2 of 0.79 (95% CI, 0.72-0.84) for MD and 0.68 (95% CI, 0.53-0.79) for PSD and MAEs of 2.1 dB (95% CI, 1.8-2.5 dB) for MD and 1.5 dB (95% CI, 1.3-1.9 dB) for PSD. This was significantly better than mean thickness estimates for 24-2 MD (0.41 [95% CI, 0.26-0.57] and 3.4 dB [95% CI, 2.7-4.5 dB]) and 24-2 PSD (0.38 [95% CI, 0.20-0.57] and 2.4 dB [95% CI, 2.0-2.8 dB]). The GCIPL (R2 = 0.79) and GCC (R2 = 0.75) had the highest performance estimating 10-2 and 24-2 MD, respectively. CONCLUSIONS Deep learning models improved estimates of functional loss from SD OCT imaging. Accurate estimates can help clinicians to individualize VF testing to patients.
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Affiliation(s)
- Mark Christopher
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California
| | - Christopher Bowd
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California
| | - James A Proudfoot
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California
| | - Akram Belghith
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California
| | - Michael H Goldbaum
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California
| | - Jasmin Rezapour
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California; Department of Ophthalmology, University Medical Center Mainz, Mainz, Germany
| | - Massimo A Fazio
- School of Medicine, University of Alabama-Birmingham, Birmingham, Alabama
| | | | - Gustavo De Moraes
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Medical Center, New York, New York
| | - Jeffrey M Liebmann
- Bernard and Shirlee Brown Glaucoma Research Laboratory, Edward S. Harkness Eye Institute, Department of Ophthalmology, Columbia University Medical Center, New York, New York
| | - Robert N Weinreb
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California
| | - Linda M Zangwill
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, California.
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Three "Red Lines" for Pattern Recognition-Based Differential Diagnosis Using Optical Coherence Tomography in Clinical Practice. J Neuroophthalmol 2021; 41:385-398. [PMID: 34415273 DOI: 10.1097/wno.0000000000001173] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
BACKGROUND Optical coherence tomography (OCT) devices for imaging of the eye are broadly available. The test is noninvasive, rapid, and well-tolerated by patients. This creates a large number of OCT images and patient referrals. Interpretation of OCT findings at the interface between neurological and ophthalmologic conditions has become a key skill in the neuro-ophthalmology service. Similar to the interpretation of visual fields, recogntion of the vertical and horizontal medians are helpful. A third "red line" is added, which will be reviewed here. EVIDENCE Levels 1a to 5 evidence. ACQUISITION Literature research. RESULTS There is level 1a evidence that neurodegeneration of the brain is associated with inner retinal layer atrophy. Predominantly, this is driven by retrograde (trans-synaptic) axonal degeneration from the brain to the eye. This process typically stops at the level of the inner nuclear layer (INL). Anterograde (Wallerian) axonal degeneration from the eye to the brain can trespass the INL. The geography of atrophy and swelling of individual macular retinal layers distinguishes prechiasmal from postchiasmal pathology. The emerging patterns are a front-back "red line" at the INL; a vertical "red line" through the macula for chiasmal/postchiasmal pathology; and a horizontal "red line" through the macular for pathology pointing to the optic disc. This is summarized by illustrative case vignettes. CONCLUSIONS The interpretation of patterns of individual retinal layer atrophy (3 "red lines") needs to be combined with recognition of localized layer thickening (edema, structural) at the macula. Certain macular patterns point to pathology at the level of the optic disc. This requires revision of the optic disc OCT and will guide need for further investigations. The 3 "red lines" proposed here may be found useful in clinical practice and the related mnemonics ("half moon," "sunset," "rainbow") for teaching.
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Kamalipour A, Moghimi S. Macular Optical Coherence Tomography Imaging in Glaucoma. J Ophthalmic Vis Res 2021; 16:478-489. [PMID: 34394875 PMCID: PMC8358749 DOI: 10.18502/jovr.v16i3.9442] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Accepted: 04/22/2021] [Indexed: 11/24/2022] Open
Abstract
The advent of spectral-domain optical coherence tomography has played a transformative role in posterior segment imaging of the eye. Traditionally, images of the optic nerve head and the peripapillary area have been used to evaluate the structural changes associated with glaucoma. Recently, there is growing evidence in the literature supporting the use of macular spectral-domain optical coherence tomography as a complementary tool for clinical evaluation and research purposes in glaucoma. Containing more than 50% of retinal ganglion cells in a multilayered pattern, macula is shown to be affected even at the earliest stages of glaucomatous structural damage. Risk assessment for glaucoma progression, earlier detection of glaucomatous structural damage, monitoring of glaucoma especially in advanced cases, and glaucoma evaluation in certain ocular conditions including eyes with high myopia, positive history of disc hemorrhage, and certain optic disc phenotypes are specific domains where macular imaging yields complementary information compared to optic nerve head and peripapillary evaluation using optical coherence tomography. Moreover, the development of artificial intelligence models in data analysis has enabled a tremendous opportunity to create an integrated representation of structural and functional alterations observed in glaucoma. In this study, we aimed at providing a brief review of the main clinical applications and future potential utility of macular spectral-domain optical coherence tomography in glaucoma.
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Affiliation(s)
- Alireza Kamalipour
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA, United States
| | - Sasan Moghimi
- Hamilton Glaucoma Center, Shiley Eye Institute, Viterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla, CA, United States
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Artificial intelligence and complex statistical modeling in glaucoma diagnosis and management. Curr Opin Ophthalmol 2021; 32:105-117. [PMID: 33395111 DOI: 10.1097/icu.0000000000000741] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
Abstract
PURPOSE OF REVIEW The field of artificial intelligence has grown exponentially in recent years with new technology, methods, and applications emerging at a rapid rate. Many of these advancements have been used to improve the diagnosis and management of glaucoma. We aim to provide an overview of recent publications regarding the use of artificial intelligence to enhance the detection and treatment of glaucoma. RECENT FINDINGS Machine learning classifiers and deep learning algorithms have been developed to autonomously detect early structural and functional changes of glaucoma using different imaging and testing modalities such as fundus photography, optical coherence tomography, and standard automated perimetry. Artificial intelligence has also been used to further delineate structure-function correlation in glaucoma. Additional 'structure-structure' predictions have been successfully estimated. Other machine learning techniques utilizing complex statistical modeling have been used to detect glaucoma progression, as well as to predict future progression. Although not yet approved for clinical use, these artificial intelligence techniques have the potential to significantly improve glaucoma diagnosis and management. SUMMARY Rapidly emerging artificial intelligence algorithms have been used for the detection and management of glaucoma. These algorithms may aid the clinician in caring for patients with this complex disease. Further validation is required prior to employing these techniques widely in clinical practice.
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Girard MJA, Schmetterer L. Artificial intelligence and deep learning in glaucoma: Current state and future prospects. PROGRESS IN BRAIN RESEARCH 2020; 257:37-64. [PMID: 32988472 DOI: 10.1016/bs.pbr.2020.07.002] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Over the past few years, there has been an unprecedented and tremendous excitement for artificial intelligence (AI) research in the field of Ophthalmology; this has naturally been translated to glaucoma-a progressive optic neuropathy characterized by retinal ganglion cell axon loss and associated visual field defects. In this review, we aim to discuss how AI may have a unique opportunity to tackle the many challenges faced in the glaucoma clinic. This is because glaucoma remains poorly understood with difficulties in providing early diagnosis and prognosis accurately and in a timely fashion. In the short term, AI could also become a game changer by paving the way for the first cost-effective glaucoma screening campaigns. While there are undeniable technical and clinical challenges ahead, and more so than for other ophthalmic disorders whereby AI is already booming, we strongly believe that glaucoma specialists should embrace AI as a companion to their practice. Finally, this review will also remind ourselves that glaucoma is a complex group of disorders with a multitude of physiological manifestations that cannot yet be observed clinically. AI in glaucoma is here to stay, but it will not be the only tool to solve glaucoma.
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Affiliation(s)
- Michaël J A Girard
- Ophthalmic Engineering & Innovation Laboratory (OEIL), Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore.
| | - Leopold Schmetterer
- Ocular Imaging, Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore; School of Chemical and Biomedical Engineering, Nanyang Technological University, Singapore, Singapore; SERI-NTU Advanced Ocular Engineering (STANCE), Singapore, Singapore; Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore, Singapore; Department of Clinical Pharmacology, Medical University of Vienna, Vienna, Austria; Center for Medical Physics and Biomedical Engineering, Medical University of Vienna, Vienna, Austria; Institute of Clinical and Experimental Ophthalmology, Basel, Switzerland.
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