Artificial intelligence for detection of retinal toxicity in chloroquine and hydroxychloroquine therapy using multifocal electroretinogram waveforms

Chloroquine and hydroxychloroquine, while effective in rheumatology, pose risks of retinal toxicity, necessitating regular screening to prevent visual disability. The gold standard for screening includes retinal imaging and automated perimetry, with multifocal electroretinography (mfERG) being a recognized but less accessible method. This study explores the efficacy of Artificial Intelligence (AI) algorithms for detecting retinal damage in patients undergoing (hydroxy-)chloroquine therapy. We analyze the mfERG data, comparing the performance of AI models that utilize raw mfERG time-series signals against models using conventional waveform parameters. Our classification models aimed to identify maculopathy, and regression models were developed to predict perimetric sensitivity. The findings reveal that while regression models were more adept at predicting non-disease-related variation, AI-based models, particularly those utilizing full mfERG traces, demonstrated superior predictive power for disease-related changes compared to linear models. This indicates a significant potential to improve diagnostic capabilities, although the unbalanced nature of the dataset may limit some applications.