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arXiv · 2609.29256

Deep learning of longitudinal visual fields predicts glaucoma progression rate and identifies fast progressors

Taiabur Rahman·Siddiqur Rahman·Muhammad Moniruzzaman·Ummay Kawsar·Sayedatunnessa Ratna·Shadman Siddique·Rafsan Siddique·Tausif Ahmad·Tahsin Ahmad·Golam Rabbani
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Abstract

Glaucoma is the leading cause of irreversible blindness, and timely identification of fast progressors is essential to prevent disability. Current practice estimates progression by ordinary least-squares regression of mean deviation (MD) on time, requiring 6--10 visual field (VF) tests over several years to obtain a reliable slope. We present GLAM (Glaucoma Longitudinal Analysis Model), a deep learning framework that ingests longitudinal Humphrey 24-2 total deviation sequences with five clinical features and predicts MD and visual field index progression rates using attention-based fusion and aleatoric uncertainty. On the open-access University of Washington Humphrey Visual Field dataset (4,276 patient-eyes), GLAM achieved an MD-rate mean absolute error of 0.139 dB yr$^{-1}$ ($R^2 = 0.927$; 73.5% reduction over a ridge baseline) and an AUC of 0.990 for fast-progressor detection. VF-only deep learning can match multimodal pipelines for progression prognostication using routinely collected perimetry alone.

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Taiabur Rahman, Siddiqur Rahman, Muhammad Moniruzzaman, Ummay Kawsar, Sayedatunnessa Ratna, Shadman Siddique, Rafsan Siddique, Tausif Ahmad, Tahsin Ahmad, Golam Rabbani. 2026-09-24. Deep learning of longitudinal visual fields predicts glaucoma progression rate and identifies fast progressors. https://arxiv.org/abs/2609.29256

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