Multi-task learning for activity detection in neovascular age-related macular degeneration

Purpose Comparison of performance and explainability of a multi-task convolutional deep neuronal network to single-task networks for activity detection in neovascular age-dependent macular degeneration. Methods From n = 70 patients (46 female, 24 male) who attended the University Eye Hospital Tübingen 3762 optical coherence tomography B-scans (right eye: 2011, left eye: 1751) were acquired with Heidelberg Spectralis, Heidelberg, Germany. B-scans were graded by a retina specialist and an ophthalmology resident, and then used to develop a multi-task deep learning model to predict disease activity in neovascular age-related macular degeneration along with the presence of sub- and intraretinal fluid. We used performance metrics for comparison to single-task networks and visualized the DNN-based decision with t-distributed stochastic neighbor embedding and clinically validated saliency mapping techniques. Results The multi-task model surpassed single-task networks in accuracy for activity detection (94.2). Further-more, compared to single-task networks, visualizations via t-distributed stochastic neighbor embedding and saliency maps highlighted that multi-task networks’ decisions for activity detection in neovascular age-related macular degeneration were highly consistent with the presence of both sub- and intraretinal fluid. Conclusions Multi-task learning increases the performance of neuronal networks for predicting disease activity, while providing clinicians with an easily accessible decision control, which resembles human reasoning. Translational Relevance By improving nAMD activity detection performance and transparency of automated decisions, multi-task DNNs can support the translation of machine learning research into clinical decision support systems for nAMD activity detection.