Artificial intelligence in glaucoma diagnosis and management: Has its time come?

Abstract

Artificial intelligence (AI) has been used in glaucoma diagnosis and management.The uses range from summarizing patient notes from electronic medical records to diagnosis based on fundus or optical coherence tomography (OCT) images, detecting progression on imaging or visual fields, and, less commonly, assessing target intraocular pressure (IOP) and predicting surgical failure. [1][2]][4][5][6] Using AI-based classification of disc photographs for population-based screening of undetected glaucoma in India based is an attractive proposition.The implementation of fundus photography-based diabetic retinopathy screening programs across some states is an opportunity.Adding an AI-based grading for the disc is a logical value addition to the program.However, there are challenges from both logistic and AI perspectives.The logistic concerns are applicable to any screening program for glaucoma, the increased pressure on the health care system with these referrals, the high proportion of ungradable images, the false positives and false negative rates, and so on.From the AI standpoint, the replicability of many algorithms in the population has been far below that in a controlled study environment.In addition, algorithms do not always perform adequately in populations of other ethnicities than the test cohort. [2]Any algorithm would need to be adequately validated in appropriate populations before it is rolled out on a larger scale.Fortunately, there are Indian AI disc photograph-based diagnostic algorithms, though adequate large-scale validation is a challenge.

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