{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T10:00:48Z","timestamp":1794045648387,"version":"4.8.0"},"reference-count":68,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T00:00:00Z","timestamp":1732147200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Institute of Health","award":["1T32NS091006-10"],"award-info":[{"award-number":["1T32NS091006-10"]}]},{"name":"Institute for Translational Medicine and Therapeutics of the Perelman School of Medicine at the University of Pennsylvania","award":["1T32NS091006-10"],"award-info":[{"award-number":["1T32NS091006-10"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JCM"],"abstract":"<jats:p>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\u2019s 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, twenty-three reported quantitative phenotyping, and six reported both. Diagnostic accuracy was reported in 36 of 38 and correlation coefficients in 10 of 23 studies. Kinematics (e.g., accelerometers and inertial measurement units) were the most used dataset. Diagnostic accuracy was reported in 36 studies and ranged from 56 to 100% compared to clinical diagnoses to differentiate them from healthy controls. The correlation coefficient was reported in 10 studies and ranged from 0.54 to 0.99 compared to clinical ratings for quantitative phenotyping. Five studies had an overall judgment of \u201clow risk of bias\u201d and three had external validation. Conclusion: There is a need to adopt AI-based research guidelines to minimize reporting heterogeneity and bolster clinical interpretability.<\/jats:p>","DOI":"10.3390\/jcm13237009","type":"journal-article","created":{"date-parts":[[2024,11,21]],"date-time":"2024-11-21T06:57:54Z","timestamp":1732172274000},"page":"7009","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Artificial Intelligence in the Diagnosis and Quantitative Phenotyping of Hyperkinetic Movement Disorders: A Systematic Review"],"prefix":"10.3390","volume":"13","author":[{"given":"Joaquin A.","family":"Vizcarra","sequence":"first","affiliation":[{"name":"Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA"},{"name":"Parkinson\u2019s Disease Research, Education and Clinical Center, Philadelphia Veterans Affairs Medical Center, Philadelphia, PA 19104, USA"},{"name":"Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sushuma","family":"Yarlagadda","sequence":"additional","affiliation":[{"name":"Department of Neurology, Emory University School of Medicine, Atlanta, GA 30322, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1849-2085","authenticated-orcid":false,"given":"Kevin","family":"Xie","sequence":"additional","affiliation":[{"name":"Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA"},{"name":"Department of Bioengineering, School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Colin A.","family":"Ellis","sequence":"additional","affiliation":[{"name":"Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA"},{"name":"Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Meredith","family":"Spindler","sequence":"additional","affiliation":[{"name":"Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4045-490X","authenticated-orcid":false,"given":"Lauren H.","family":"Hammer","sequence":"additional","affiliation":[{"name":"Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA"},{"name":"Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Duker, A.P., and Espay, A.J. 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