Deep learning for the prediction of treatment response in depression

Background: Mood disorders are characterized by heterogeneity in severity, symptoms and treatment response. The possibility of selecting the correct therapy onthe basis of patient-specific biomarker may be a considerable step towards personalized psychiatry. Machine learning methods are gaining increasing popularity inthe medical field. Once trained, the possibility to consider single patients in the analyses instead of whole groups makes them particularly appealing to investigatetreatment response. Deep learning, a branch of machine learning, lately gained attention, due to its effectiveness in dealing with large neuroimaging data and tointegrate them with clinical, molecular or -omics biomarkers.Methods: In this mini-review, we summarize studies that use deep learning methods to predict response to treatment in depression. We performed a bibliographicsearch on PUBMED, Google Scholar and Web of Science using the terms “psychiatry”, “mood disorder”, “depression”, “treatment”, “deep learning”, “neural networks