Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma

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

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)731-738
Number of pages8
JournalOphthalmology
Volume127
Issue number6
DOIs
StatePublished - Jun 2020

ASJC Scopus subject areas

  • Ophthalmology

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    Wang, M., Shen, L. Q., Pasquale, L. R., Boland, M. V., Wellik, S. R., De Moraes, C. G., Myers, J. S., Nguyen, T. D., Ritch, R., Ramulu, P., Wang, H., Tichelaar, J., Li, D., Bex, P. J., & Elze, T. (2020). Artificial Intelligence Classification of Central Visual Field Patterns in Glaucoma. Ophthalmology, 127(6), 731-738. https://doi.org/10.1016/j.ophtha.2019.12.004