Siamak Yousefi1, Taichi Kiwaki2, Yuhui Zheng2, Hiroki Sugiura2, Ryo Asaoka3, Hiroshi Murata3, Hans Lemij4, Kenji Yamanishi2. 1. Department of Ophthalmology, Department of Genetics, Genomics, and Informatics, University of Tennessee Health Science Center, Memphis, Tennessee, USA; Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan. Electronic address: siamak.yousefi@uthsc.edu. 2. Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan. 3. Department of Ophthalmology, The University of Tokyo, Tokyo, Japan. 4. Rotterdam Eye Hospital, Rotterdam, Netherlands.
Abstract
PURPOSE: Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variable. Region-wise indices sit in between. This study introduces a machine learning-based index for glaucoma progression detection that outperforms global, region-wise, and point-wise indices. DESIGN: Development and comparison of a prognostic index. METHOD: Visual fields from 2085 eyes of 1214 subjects were used to identify glaucoma progression patterns using machine learning. Visual fields from 133 eyes of 71 glaucoma patients were collected 10 times over 10 weeks to provide a no-change, test-retest dataset. The parameters of all methods were identified using visual field sequences in the test-retest dataset to meet fixed 95% specificity. An independent dataset of 270 eyes of 136 glaucoma patients and survival analysis were used to compare methods. RESULTS: The time to detect progression in 25% of the eyes in the longitudinal dataset using global mean deviation (MD) was 5.2 (95% confidence interval, 4.1-6.5) years; 4.5 (4.0-5.5) years using region-wise, 3.9 (3.5-4.6) years using point-wise, and 3.5 (3.1-4.0) years using machine learning analysis. The time until 25% of eyes showed subsequently confirmed progression after 2 additional visits were included were 6.6 (5.6-7.4) years, 5.7 (4.8-6.7) years, 5.6 (4.7-6.5) years, and 5.1 (4.5-6.0) years for global, region-wise, point-wise, and machine learning analyses, respectively. CONCLUSIONS: Machine learning analysis detects progressing eyes earlier than other methods consistently, with or without confirmation visits. In particular, machine learning detects more slowly progressing eyes than other methods.
PURPOSE: Global indices of standard automated perimerty are insensitive to localized losses, while point-wise indices are sensitive but highly variable. Region-wise indices sit in between. This study introduces a machine learning-based index for glaucoma progression detection that outperforms global, region-wise, and point-wise indices. DESIGN: Development and comparison of a prognostic index. METHOD: Visual fields from 2085 eyes of 1214 subjects were used to identify glaucoma progression patterns using machine learning. Visual fields from 133 eyes of 71 glaucomapatients were collected 10 times over 10 weeks to provide a no-change, test-retest dataset. The parameters of all methods were identified using visual field sequences in the test-retest dataset to meet fixed 95% specificity. An independent dataset of 270 eyes of 136 glaucomapatients and survival analysis were used to compare methods. RESULTS: The time to detect progression in 25% of the eyes in the longitudinal dataset using global mean deviation (MD) was 5.2 (95% confidence interval, 4.1-6.5) years; 4.5 (4.0-5.5) years using region-wise, 3.9 (3.5-4.6) years using point-wise, and 3.5 (3.1-4.0) years using machine learning analysis. The time until 25% of eyes showed subsequently confirmed progression after 2 additional visits were included were 6.6 (5.6-7.4) years, 5.7 (4.8-6.7) years, 5.6 (4.7-6.5) years, and 5.1 (4.5-6.0) years for global, region-wise, point-wise, and machine learning analyses, respectively. CONCLUSIONS: Machine learning analysis detects progressing eyes earlier than other methods consistently, with or without confirmation visits. In particular, machine learning detects more slowly progressing eyes than other methods.
Authors: Siamak Yousefi; Tobias Elze; Louis R Pasquale; Osamah Saeedi; Mengyu Wang; Lucy Q Shen; Sarah R Wellik; Carlos G De Moraes; Jonathan S Myers; Michael V Boland Journal: Ophthalmology Date: 2020-03-10 Impact factor: 12.079
Authors: Xiaoqin Huang; Fatemeh Saki; Mengyu Wang; Tobias Elze; Michael V Boland; Louis R Pasquale; Chris A Johnson; Siamak Yousefi Journal: J Glaucoma Date: 2022-06-03 Impact factor: 2.290
Authors: Mengyu Wang; Lucy Q Shen; Louis R Pasquale; Michael V Boland; Sarah R Wellik; Carlos Gustavo De Moraes; Jonathan S Myers; Thao D Nguyen; Robert Ritch; Pradeep Ramulu; Hui Wang; Jorryt Tichelaar; Dian Li; Peter J Bex; Tobias Elze Journal: Ophthalmology Date: 2019-12-12 Impact factor: 12.079
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