Deep learning models to predict primary open-angle glaucoma

Music City Center 

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.