The use of machine learning methods in neurodegenerative disease research: A scoping review

Machine learning (ML) methods are increasingly used in clinical research, but their extent is complex and largely unknown in the field of neurodegenerative diseases (ND). This scoping review describes state-of-the-art ML in ND research using MEDLINE (PubMed), Embase (Ovid), Central (Cochrane), and Institute of Electrical and Electronics Engineers Xplore. Included articles, published between January 1, 2016, and December 31, 2020, used patient data on Alzheimers disease, multiple sclerosis, amyotrophic lateral sclerosis, Parkinsons disease, or Huntingtons disease that employed ML methods during primary analysis. One reviewer screened citations for inclusion; 5 conducted data extraction. For each article, we abstracted the type of ND; publication year; sample size; ML algorithm data type; primary clinical goal (disease diagnosis/prognosis/prediction of treatment effect); and ML method type. Quantitative and qualitative syntheses of the results were conducted. After screening 4,471 citations and searching 1,677 full-text articles, 1,485 articles were included. The number of articles using ML methods in ND research increased from 172 in 2016 to 490 in 2020, with most of those in Alzheimers disease. The most common data type was imaging data (46.9% of articles), followed by functional (20.6%), clinical (14.2%), biospecimen (6.2%), genetic (5.9%), electrophysiological (5.1%), and molecular (1.1%). Overall, 68.5% of imaging data studies were in Alzheimers disease and 75.9% of functional data studies were in Parkinsons disease. Disease diagnosis was the most common clinical aim in studies using ML methods (73.5%), followed by disease prognosis (21.4%) and prediction of treatment effect (13.5%). We extracted 2,734 ML methods, with support vector machine (n=651, 23.8%), random forest (n=310, 11.3%), and convolutional neural network (n=166, 6.1%) representing the majority. Finally, we identified 322 unique ML methods. There are opportunities for additional research using ML methods for disease prognosis and prediction of treatment effect. Addressing these utilization gaps will be important in future studies. Author SummaryFew state-of-the-art scientific updates have been targeted for broader readerships without indulging in technical jargon. We have learned a lot from Judea Pearl on how to put things into context and make them clear. In this review paper, we identify machine learning methods used in the realm of neurodegenerative diseases and describe how the use of these methods can be enhanced in neurodegenerative disease research.