Machine learning for classification and prediction of brain diseases: recent advances and upcoming challenges

Abstract

Purpose of review 

Machine learning is an artificial intelligence technique that allows computers to perform a task without being explicitly programmed. Machine learning can be used to assist diagnosis and prognosis of brain disorders. Although the earliest articles date from more than ten years ago, research increases at a very fast pace.

Recent findings 

Recent works using machine learning for diagnosis have moved from classification of a given disease versus controls to differential diagnosis. Intense research has been devoted to the prediction of the future patient state. Although a lot of earlier works focused on neuroimaging as data source, the current trend is on the integration of multimodal data. In terms of targeted diseases, dementia remains dominant but approaches have been developed for a wide variety of neurological and psychiatric diseases.

Summary 

Machine learning is extremely promising for assisting diagnosis and prognosis in brain disorders. Nevertheless, we argue that key challenges remain to be addressed by the community for bringing these tools in clinical routine: good practices regarding validation and reproducible research need to be more widely adopted; extensive generalization studies are required; interpretable models are needed to overcome the limitations of black-box approaches.