Applications of machine learning to diagnosis and treatment of neurodegenerative diseases

• Published in: Nature reviews. Neurology Vol. 16; no. 8; pp. 440 - 456
• Main Authors: Myszczynska, Monika A, Ojamies, Poojitha N, Lacoste, Alix M B, Neil, Daniel, Saffari, Amir, Mead, Richard, Hautbergue, Guillaume M, Holbrook, Joanna D, Ferraiuolo, Laura
• Format: Journal Article
• Language: English
• Published: England Nature Publishing Group 01.08.2020
• Subjects: Artificial intelligence; Degeneration; Humans; Machine learning; Machine Learning - trends; Nervous system; Neurodegenerative Diseases - diagnostic imaging; Neurodegenerative Diseases - therapy; Neuroimaging - methods; Neuroimaging - trends; United Kingdom
• ISSN: 1759-4758; 1759-4766
• DOI: 10.1038/s41582-020-0377-8

Globally, there is a huge unmet need for effective treatments for neurodegenerative diseases. The complexity of the molecular mechanisms underlying neuronal degeneration and the heterogeneity of the patient population present massive challenges to the development of early diagnostic tools and effective treatments for these diseases. Machine learning, a subfield of artificial intelligence, is enabling scientists, clinicians and patients to address some of these challenges. In this Review, we discuss how machine learning can aid early diagnosis and interpretation of medical images as well as the discovery and development of new therapies. A unifying theme of the different applications of machine learning is the integration of multiple high-dimensional sources of data, which all provide a different view on disease, and the automated derivation of actionable insights.