Applications of machine learning to diagnosis and treatment of neurodegenerative diseases
| Autor: | Laura Ferraiuolo, Joanna D. Holbrook, Richard J. Mead, Poojitha N. Ojamies, Guillaume M. Hautbergue, Alix M. B. Lacoste, Amir Saffari, Monika A Myszczynska, Daniel Neil |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
business.industry Disease Machine learning computer.software_genre Diagnostic tools Unmet needs 03 medical and health sciences Cellular and Molecular Neuroscience Patient population 030104 developmental biology 0302 clinical medicine Medicine Neuronal degeneration Neurology (clinical) Artificial intelligence business computer 030217 neurology & neurosurgery |
| Zdroj: | Nature Reviews Neurology. 16:440-456 |
| ISSN: | 1759-4766 1759-4758 |
| DOI: | 10.1038/s41582-020-0377-8 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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