Artificial Intelligence in the Diagnosis and Quantitative Phenotyping of Hyperkinetic Movement Disorders: A Systematic Review.

ABSTRACT: Background: Hyperkinetic movement disorders involve excessive, involuntary movements such as ataxia, chorea, dystonia, myoclonus, tics, and tremor. Recent advances in artificial intelligence (AI) allow investigators to integrate multimodal instrumented movement measurements and imaging techniques and to analyze these data together at scale. In this systematic review, we aim to characterize AI's performance in diagnosing and quantitatively phenotyping these disorders. Methods: We searched PubMed and Embase using a semi-automated article-screening pipeline. Results: Fifty-five studies met the inclusion criteria (n = 11,946 subjects). Thirty-five studies used machine learning, sixteen used deep learning, and four used both. Thirty-eight studies reported disease diagnosis,

SUBMITTER: Vizcarra JA 

PROVIDER: S-EPMC11642074 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature