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- Title
Deep learning models to predict primary open‐angle glaucoma.
- Authors
Zhou, Ruiwen; Philip Miller, J.; Gordon, Mae; Kass, Michael; Lin, Mingquan; Peng, Yifan; Li, Fuhai; Feng, Jiarui; Liu, Lei
- Abstract
Summary: Glaucoma is a major cause of blindness and vision impairment worldwide, and visual field (VF) tests are essential for monitoring the conversion of glaucoma. While previous studies have primarily focused on using VF data at a single time point for glaucoma prediction, there has been limited exploration of longitudinal trajectories. Additionally, many deep learning techniques treat the time‐to‐glaucoma prediction as a binary classification problem (glaucoma Yes/No), resulting in the misclassification of some censored subjects into the nonglaucoma category and decreased power. To tackle these challenges, we propose and implement several deep‐learning approaches that naturally incorporate temporal and spatial information from longitudinal VF data to predict time‐to‐glaucoma. When evaluated on the Ocular Hypertension Treatment Study (OHTS) dataset, our proposed convolutional neural network (CNN)‐long short‐term memory (LSTM) emerged as the top‐performing model among all those examined. The implementation code can be found online (https://github.com/rivenzhou/VF_prediction).
- Subjects
OPEN-angle glaucoma; DEEP learning; CONVOLUTIONAL neural networks; VISUAL fields; VISION disorders; OCULAR hypertension
- Publication
Stat, 2024, Vol 13, Issue 1, p1
- ISSN
2049-1573
- Publication type
Article
- DOI
10.1002/sta4.649