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Deep learning (DL) shows notable success in biomedical studies. However, mostDL algorithms work as a black box, exclude biomedical experts, and needextensive data. We introduce the Self-Enhancing Multi-Photon ArtificialIntelligence (SEMPAI), that integrates hypothesis-driven priors in adata-driven DL approach for research on multiphoton microscopy (MPM) of musclefibers. SEMPAI utilizes meta-learning to optimize prior integration, datarepresentation, and neural network architecture simultaneously. This allowshypothesis testing and provides interpretable feedback about the origin ofbiological information in MPM images. SEMPAI performs joint learning of severaltasks to enable prediction for small datasets. The method is applied on anextensive multi-study dataset resulting in the largest joint analysis ofpathologies and function for single muscle fibers. SEMPAI outperformsstate-of-the-art biomarkers in six of seven predictive tasks, including thosewith scarce data. SEMPAI's DL models with integrated priors are superior tothose without priors and to prior-only machine learning approaches.

SEMPAI: a Self-Enhancing Multi-Photon Artificial Intelligence for prior-informed assessment of muscle function and pathology