Glaucoma Progression Detection and Humphrey Visual Field Prediction Using Discriminative and Generative Vision Transformers

Abstract

Glaucoma is one of the top causes of blindness worldwide. Assessing its progression is critical to determine potential visual impairment and to design sound treatment plans. Standard automated perimetry tests, commonly known as visual field (VF) tests, are clinically used to evaluate the state of functional vision. To provide an accurate and automatic diagnostic tool for clinical decision making in glaucoma progression, we utilize the predictive power of artificial intelligence (AI) and propose two vision transformer (ViT)-based deep learning (DL) networks. First, we optimize a spatiotemporal ViT to classify a subject’s rate of glaucoma progression (GP) using only 3 baseline VFs; we explore threshold mean deviation (MD) rate of change from −0.3 to −1.5 dB/year and achieve up to 89% GP detection accuracy. Second, we develop a VF-to-VF generation architecture via a diffusion model with a ViT backbone. The model predicts future VFs with Pointwise Mean Absolute Error (PMAE) as low as 2.15 dB for mild VF deficits and is the first to extend VF prediction up to 10 years into the future. Our models are trained and validated on our ‘62K+’ dataset, the largest available of VFs to-date including at-risk, minority populations, thus ensuring our models’ generalizability. We establish our computational methods and compare testing results on the publicly available UWHVF dataset. In short, our study utilizes novel AI methods for predicting future rates and patterns of glaucoma progression in order to expedite timely treatment for better patient quality of life. The code is available at https://github.com/AI4VSLab/GP-Detection-VF-Prediction.