Abstract
Glaucoma is a progressive disease that can lead to permanent vision loss, making progression prediction vital for guiding effective treatment. Deep learning aids progression prediction but may yield unequal outcomes across demographic groups. We proposed a model called FairDist, which utilized baseline optical coherence tomography scans to predict glaucoma progression. An equity-aware EfficientNet was trained for glaucoma detection, which was then adapted for progression prediction with knowledge distillation. Model accuracy was measured by the AUC, Sensitivity, Specificity, and equity was assessed using equity-scaled AUC, which adjusts AUC by accounting for subgroup disparities. The mean deviation, fast progression, and total deviation pointwise progression were explored in this work. For both progression types, FairDist achieved the highest AUC and equity-scaled AUC for gender and racial groups, compared to methods with and without unfairness mitigation strategies. FairDist can be generalized to other disease progression prediction tasks to potentially achieve improved performance and fairness.