Mengyu Wang1, Lucy Q Shen2, Louis R Pasquale3, Michael V Boland4, Sarah R Wellik5, Carlos Gustavo De Moraes6, Jonathan S Myers7, Thao D Nguyen8, Robert Ritch9, Pradeep Ramulu4, Hui Wang10, Jorryt Tichelaar1, Dian Li1, Peter J Bex11, Tobias Elze12. 1. Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts. 2. Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts. 3. Eye and Vision Research Institute, Icahn School of Medicine at Mount Sinai, New York, New York; Channing Division of Network Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. 4. Wilmer Eye Institute, Johns Hopkins University School of Medicine, Baltimore, Maryland. 5. Bascom Palmer Eye Institute, University of Miami School of Medicine, Miami, Florida. 6. Edward S. Harkness Eye Institute, Columbia University Medical Center, New York, New York. 7. Wills Eye Hospital, Thomas Jefferson University, Philadelphia, Pennsylvania. 8. Department of Mechanical Engineering, Johns Hopkins University, Baltimore, Maryland. 9. Einhorn Clinical Research Center, New York Eye and Ear Infirmary of Mount Sinai, New York, New York. 10. Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts; Institute for Psychology and Behavior, Jilin University of Finance and Economics, Changchun, China. 11. Department of Psychology, Northeastern University, Boston, Massachusetts. 12. Schepens Eye Research Institute, Harvard Medical School, Boston, Massachusetts; Complex Structures in Biology and Cognition, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany. Electronic address: tobias-elze@tobias-elze.de.
Abstract
PURPOSE: To quantify the central visual field (VF) loss patterns in glaucoma using artificial intelligence. DESIGN: Retrospective study. PARTICIPANTS: VFs of 8712 patients with 13 951 Humphrey 10-2 test results from 13 951 eyes for cross-sectional analyses, and 824 patients with at least 5 reliable 10-2 test results at 6-month intervals or more from 1191 eyes for longitudinal analyses. METHODS: Total deviation values were used to determine the central VF patterns using the most recent 10-2 test results. A 24-2 VF within a 3-month window of the 10-2 tests was used to stage eyes into mild, moderate, or severe functional loss using the Hodapp-Anderson-Parrish scale at baseline. Archetypal analysis was applied to determine the central VF patterns. Cross-validation was performed to determine the optimal number of patterns. Stepwise regression was applied to select the optimal feature combination of global indices, average baseline decomposition coefficients from central VFs archetypes, and other factors to predict central VF mean deviation (MD) slope based on the Bayesian information criterion (BIC). MAIN OUTCOME MEASURES: The central VF patterns stratified by severity stage based on 24-2 test results and a model to predict the central VF MD change over time using baseline test results. RESULTS: From cross-sectional analysis, 17 distinct central VF patterns were determined for the 13 951 eyes across the spectrum of disease severity. These central VF patterns could be divided into isolated superior loss, isolated inferior loss, diffuse loss, and other loss patterns. Notably, 4 of the 5 patterns of diffuse VF loss preserved the less vulnerable inferotemporal zone, whereas they lost most of the remaining more vulnerable zone described by the Hood model. Inclusion of coefficients from central VF archetypical patterns strongly improved the prediction of central VF MD slope (BIC decrease, 35; BIC decrease of >6 indicating strong prediction improvement) than using only the global indices of 2 baseline VF results. Eyes with baseline VF results with more superonasal and inferonasal loss were more likely to show worsening MD over time. CONCLUSIONS: We quantified central VF patterns in glaucoma, which were used to improve the prediction of central VF worsening compared with using only global indices.
PURPOSE: To quantify the central visual field (VF) loss patterns in glaucoma using artificial intelligence. DESIGN: Retrospective study. PARTICIPANTS: VFs of 8712 patients with 13 951 Humphrey 10-2 test results from 13 951 eyes for cross-sectional analyses, and 824 patients with at least 5 reliable 10-2 test results at 6-month intervals or more from 1191 eyes for longitudinal analyses. METHODS: Total deviation values were used to determine the central VF patterns using the most recent 10-2 test results. A 24-2 VF within a 3-month window of the 10-2 tests was used to stage eyes into mild, moderate, or severe functional loss using the Hodapp-Anderson-Parrish scale at baseline. Archetypal analysis was applied to determine the central VF patterns. Cross-validation was performed to determine the optimal number of patterns. Stepwise regression was applied to select the optimal feature combination of global indices, average baseline decomposition coefficients from central VFs archetypes, and other factors to predict central VF mean deviation (MD) slope based on the Bayesian information criterion (BIC). MAIN OUTCOME MEASURES: The central VF patterns stratified by severity stage based on 24-2 test results and a model to predict the central VF MD change over time using baseline test results. RESULTS: From cross-sectional analysis, 17 distinct central VF patterns were determined for the 13 951 eyes across the spectrum of disease severity. These central VF patterns could be divided into isolated superior loss, isolated inferior loss, diffuse loss, and other loss patterns. Notably, 4 of the 5 patterns of diffuse VF loss preserved the less vulnerable inferotemporal zone, whereas they lost most of the remaining more vulnerable zone described by the Hood model. Inclusion of coefficients from central VF archetypical patterns strongly improved the prediction of central VF MD slope (BIC decrease, 35; BIC decrease of >6 indicating strong prediction improvement) than using only the global indices of 2 baseline VF results. Eyes with baseline VF results with more superonasal and inferonasal loss were more likely to show worsening MD over time. CONCLUSIONS: We quantified central VF patterns in glaucoma, which were used to improve the prediction of central VF worsening compared with using only global indices.
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