Automatic Explainable Progress Prediction of Wet Age-Related Macular Degeneration

Wet age-related macular degeneration (wAMD) is an aggressive pathology representing a leading cause of central vision loss in the elderly population. Currently, diagnosis and treatment evaluation rely on clinical experts visually interpreting tomography scans to identify pathological features for decision-making, a process that is time-consuming and subject to inter-observer variability. Stemming from the success of artificial intelligence (AI) in medicine, this paper proposes two main contributions. First, we address the novel task of automatic diagnosis and monitoring of the treatment outcome for wAMD. We adopt a unique and novel dataset containing recordings from 275 patients over different years of treatment. We show that different AI models significantly outperform the recall (measuring misclassified wAMD worsening) of human evaluation (+20% at least). As a second contribution, we perform an explainability study on the trained AI models, evidencing that the relevant features guiding the predictions are indeed a smaller subset and clinically relevant. Our results pave the way towards trustable automatic diagnosis and treatment evaluation for wAMD and related pathologies, reducing significantly the effort required from clinicians.