Avyuk Dixit1, Jithin Yohannan2, Michael V Boland3. 1. University of Michigan, Ann Arbor, Michigan. 2. Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland. 3. Department of Ophthalmology, Massachusetts Eye and Ear, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts. Electronic address: Michael_Boland@meei.harvard.edu.
Abstract
PURPOSE: Rule-based approaches to determining glaucoma progression from visual fields (VFs) alone are discordant and have tradeoffs. To detect better when glaucoma progression is occurring, we used a longitudinal data set of merged VF and clinical data to assess the performance of a convolutional long short-term memory (LSTM) neural network. DESIGN: Retrospective analysis of longitudinal clinical and VF data. PARTICIPANTS: From 2 initial datasets of 672 123 VF results from 213 254 eyes and 350 437 samples of clinical data, persons at the intersection of both datasets with 4 or more VF results and corresponding baseline clinical data (cup-to-disc ratio, central corneal thickness, and intraocular pressure) were included. After exclusion criteria-specifically the removal of VFs with high false-positive and false-negative rates and entries with missing data-were applied to ensure reliable data, 11 242 eyes remained. METHODS: Three commonly used glaucoma progression algorithms (VF index slope, mean deviation slope, and pointwise linear regression) were used to define eyes as stable or progressing. Two machine learning models, one exclusively trained on VF data and another trained on both VF and clinical data, were tested. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPRC) calculated on a held-out test set and mean accuracies from threefold cross-validation were used to compare the performance of the machine learning models. RESULTS: The convolutional LSTM network demonstrated 91% to 93% accuracy with respect to the different conventional glaucoma progression algorithms given 4 consecutive VF results for each participant. The model that was trained on both VF and clinical data (AUC, 0.89-0.93) showed better diagnostic ability than a model exclusively trained on VF results (AUC, 0.79-0.82; P < 0.001). CONCLUSIONS: A convolutional LSTM architecture can capture local and global trends in VFs over time. It is well suited to assessing glaucoma progression because of its ability to extract spatiotemporal features that other algorithms cannot. Supplementing VF results with clinical data improves the model's ability to assess glaucoma progression and better reflects the way clinicians manage data when managing glaucoma.
PURPOSE: Rule-based approaches to determining glaucoma progression from visual fields (VFs) alone are discordant and have tradeoffs. To detect better when glaucoma progression is occurring, we used a longitudinal data set of merged VF and clinical data to assess the performance of a convolutional long short-term memory (LSTM) neural network. DESIGN: Retrospective analysis of longitudinal clinical and VF data. PARTICIPANTS: From 2 initial datasets of 672 123 VF results from 213 254 eyes and 350 437 samples of clinical data, persons at the intersection of both datasets with 4 or more VF results and corresponding baseline clinical data (cup-to-disc ratio, central corneal thickness, and intraocular pressure) were included. After exclusion criteria-specifically the removal of VFs with high false-positive and false-negative rates and entries with missing data-were applied to ensure reliable data, 11 242 eyes remained. METHODS: Three commonly used glaucoma progression algorithms (VF index slope, mean deviation slope, and pointwise linear regression) were used to define eyes as stable or progressing. Two machine learning models, one exclusively trained on VF data and another trained on both VF and clinical data, were tested. MAIN OUTCOME MEASURES: Area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPRC) calculated on a held-out test set and mean accuracies from threefold cross-validation were used to compare the performance of the machine learning models. RESULTS: The convolutional LSTM network demonstrated 91% to 93% accuracy with respect to the different conventional glaucoma progression algorithms given 4 consecutive VF results for each participant. The model that was trained on both VF and clinical data (AUC, 0.89-0.93) showed better diagnostic ability than a model exclusively trained on VF results (AUC, 0.79-0.82; P < 0.001). CONCLUSIONS: A convolutional LSTM architecture can capture local and global trends in VFs over time. It is well suited to assessing glaucoma progression because of its ability to extract spatiotemporal features that other algorithms cannot. Supplementing VF results with clinical data improves the model's ability to assess glaucoma progression and better reflects the way clinicians manage data when managing glaucoma.
Authors: Siamak Yousefi; Michael H Goldbaum; Madhusudhanan Balasubramanian; Tzyy-Ping Jung; Robert N Weinreb; Felipe A Medeiros; Linda M Zangwill; Jeffrey M Liebmann; Christopher A Girkin; Christopher Bowd Journal: IEEE Trans Biomed Eng Date: 2014-04 Impact factor: 4.538
Authors: Osamah J Saeedi; Tobias Elze; Loris D'Acunto; Ramya Swamy; Vikram Hegde; Surabhi Gupta; Amin Venjara; Joby Tsai; Jonathan S Myers; Sarah R Wellik; Carlos Gustavo De Moraes; Louis R Pasquale; Lucy Q Shen; Michael V Boland Journal: Ophthalmology Date: 2019-02-04 Impact factor: 12.079
Authors: Daniel Shu Wei Ting; Carol Yim-Lui Cheung; Gilbert Lim; Gavin Siew Wei Tan; Nguyen D Quang; Alfred Gan; Haslina Hamzah; Renata Garcia-Franco; Ian Yew San Yeo; Shu Yen Lee; Edmund Yick Mun Wong; Charumathi Sabanayagam; Mani Baskaran; Farah Ibrahim; Ngiap Chuan Tan; Eric A Finkelstein; Ecosse L Lamoureux; Ian Y Wong; Neil M Bressler; Sobha Sivaprasad; Rohit Varma; Jost B Jonas; Ming Guang He; Ching-Yu Cheng; Gemmy Chui Ming Cheung; Tin Aung; Wynne Hsu; Mong Li Lee; Tien Yin Wong Journal: JAMA Date: 2017-12-12 Impact factor: 56.272
Authors: David F Garway-Heath; Haogang Zhu; Qian Cheng; Katy Morgan; Chris Frost; David P Crabb; Tuan-Anh Ho; Yannis Agiomyrgiannakis Journal: Health Technol Assess Date: 2018-01 Impact factor: 4.014
Authors: Mengyu Wang; Lucy Q Shen; Louis R Pasquale; Paul Petrakos; Sydney Formica; Michael V Boland; Sarah R Wellik; Carlos Gustavo De Moraes; Jonathan S Myers; Osamah Saeedi; Hui Wang; Neda Baniasadi; Dian Li; Jorryt Tichelaar; Peter J Bex; Tobias Elze Journal: Invest Ophthalmol Vis Sci Date: 2019-01-02 Impact factor: 4.799
Authors: Joel S Schuman; Maria De Los Angeles Ramos Cadena; Rebecca McGee; Lama A Al-Aswad; Felipe A Medeiros Journal: Ophthalmol Glaucoma Date: 2021-12-22