Suitability of Machine Learning for Atrophy and Fibrosis Development in Neovascular Age-Related Macular Degeneration

Abstract

Anti-VEGF therapy has reduced the risk of legal blindness on neovascular age-related macular degeneration (nAMD), but still several patients develop fibrosis or atrophy in the long-term. Although recent statistical analyses have associated genetic, clinical and imaging biomarkers with the prognosis of patients with nAMD, no studies on the suitability of machine learning (ML) techniques have been conducted. We perform an extensive analysis on the use of ML to predict fibrosis and atrophy development on nAMD patients at 36 months from start of anti-VEGF treatment, using only data from the first 12 months. We use data collected according to real-world practice, which includes clinical and genetic factors. The ML analysis consistently found ETDRS to be relevant for the prediction of atrophy and fibrosis, confirming previous statistical analyses, while genetic variables did not show statistical relevance. The analysis also reveals that predicting one macular degeneration is a complex task given the available data, obtaining in the best case a balance accuracy of 63% and an AUC of 0.72. The lessons learnt during the development of this work can guide future ML-based prediction tasks within the ophthalmology field and help design the data collection process.