An Objective and Easy-to-Use Glaucoma Functional Severity Staging System Based on Artificial Intelligence

The objective of this study was to develop an objective and easy-to-use glaucoma staging system based on visual fields (VFs). A total of 13,231 VFs from 8077 subjects were used to develop models and 8024 VFs from 4445 subjects were used to validate models. We developed an unsupervised machine learni...

Full description

Saved in:
Bibliographic Details
Published inJournal of glaucoma Vol. 31; no. 8; p. 626
Main Authors Huang, Xiaoqin, Saki, Fatemeh, Wang, Mengyu, Elze, Tobias, Boland, Michael V, Pasquale, Louis R, Johnson, Chris A, Yousefi, Siamak
Format Journal Article
LanguageEnglish
Published United States 01.08.2022
Subjects
Online AccessGet more information
ISSN1536-481X
DOI10.1097/IJG.0000000000002059

Cover

Loading…
More Information
Summary:The objective of this study was to develop an objective and easy-to-use glaucoma staging system based on visual fields (VFs). A total of 13,231 VFs from 8077 subjects were used to develop models and 8024 VFs from 4445 subjects were used to validate models. We developed an unsupervised machine learning model to identify clusters with similar VF values. We annotated the clusters based on their respective mean deviation (MD). We computed optimal MD thresholds that discriminate clusters with the highest accuracy based on Bayes minimum error principle. We evaluated the accuracy of the staging system and validated findings based on an independent validation dataset. The unsupervised k -means algorithm discovered 4 clusters with 6784, 4034, 1541, and 872 VFs and average MDs of 0.0 dB (±1.4: SD), -4.8 dB (±1.9), -12.2 dB (±2.9), and -23.0 dB (±3.8), respectively. The supervised Bayes minimum error classifier identified optimal MD thresholds of -2.2, -8.0, and -17.3 dB for discriminating normal eyes and eyes at the early, moderate, and advanced stages of glaucoma. The accuracy of the glaucoma staging system was 94%, based on identified MD thresholds with respect to the initial k -means clusters. We discovered that 4 severity levels based on MD thresholds of -2.2, -8.0, and -17.3 dB, provides the optimal number of severity stages based on unsupervised and supervised machine learning. This glaucoma staging system is unbiased, objective, easy-to-use, and consistent, which makes it highly suitable for use in glaucoma research and for day-to-day clinical practice.
ISSN:1536-481X
DOI:10.1097/IJG.0000000000002059