Machine learning in neurodegenerative disease research: a growing trend medrxivpreprint mcgillu machinelearning neurodegnerative neurdegenerativedisease disease
By Dr. Sanchari Sinha Dutta, Ph.D.Aug 4 2023Reviewed by Benedette Cuffari, M.Sc. In a recent review posted to the medRxiv* preprint server, scientists explore the utility of machine learning methods in the field of neurodegenerative disease diagnosis, prognosis, and treatment effect prediction.
Background Neurodegenerative diseases are detrimental age-related pathological conditions associated with progressive deterioration of the neuronal network in the central and peripheral nervous systems. As a result, all neurodegenerative diseases are associated with progressively disabling symptoms that ultimately lead to complete loss of autonomy and death.
To improve the management of these incurable diseases, it is important to understand disease pathogenesis, develop accurate diagnostic and prognostic tools, and discover targeted therapies. The use of machine learning methods is increasing in the field of neurodegenerative disease research for rapidly and accurately analyzing disease-related data, which is essential for supporting diagnostic and therapeutic innovations.
A total of 4,471 studies were screened, 1,485 of which were ultimately included in the final analysis. The information extracted from each study included type of neurodegenerative disease, publication year, sample size, machine learning algorithm data type, primary clinical goal, and machine learning method type. Both qualitative and quantitative analyses of the study results were conducted.
Regarding primary clinical goals, machine learning methods were most frequently used for disease diagnosis, followed by disease prognosis and prediction of treatment effects. Imaging data remained the most commonly used data type for disease diagnosis and prognosis. For the prediction of treatment effect, functional data were the most commonly used data type.
Belgique Dernières Nouvelles, Belgique Actualités
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