{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T04:16:22Z","timestamp":1792383382395,"version":"4.4.0"},"reference-count":113,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T00:00:00Z","timestamp":1746489600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["movementdisorders.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Movement Disord Clin Pract"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Evaluation of movement disorders primarily relies on phenomenology. Despite refinements in diagnostic criteria, the accuracy remains suboptimal. Such a gap may be bridged by machine learning and video technology, which permit objective, quantitative, non\u2010invasive motor analysis. Markerless automated video\u2010analysis, namely Computer Vision, emerged as best suited for ecologically\u2010valid assessment.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To systematically review the application of Computer Vision for assessment, diagnosis, and monitoring of movement disorders.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Following the Preferred Reporting Items for Systematic Reviews and Meta\u2010Analyses guidelines, we searched Cochrane, Embase, PubMed, and Scopus databases for articles published between 1984 and September 2024. We used the following search strategy: (\u201cvideo analysis\u201d OR \u201ccomputer vision\u201d) AND (\u201cParkinson's disease\u201d OR \u201cPD\u201d OR \u201ctremor\u201d OR \u201cdystonia\u201d OR \u201cparkinsonism\u201d OR \u201cprogressive supranuclear palsy\u201d OR \u201cPSP\u201d OR \u201cmultiple system atrophy\u201d OR \u201cMSA\u201d OR \u201ccorticobasal syndrome\u201d OR \u201cCBS\u201d OR \u201cchorea\u201d OR \u201cballism\u201d OR \u201cmyoclonus\u201d OR \u201cTourette's syndrome\u201d).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Out of 1099 identified studies, 61 met inclusion criteria, and 10 additional studies were included based on authors\u2019 judgment. Parkinson's disease was the most investigated movement disorder, with gait as the prevalent motor task. OpenPose was the most used pose estimation software. Automated video\u2010analysis consistently achieved diagnostic accuracies exceeding 80% across most movement disorders. For tremor, dystonia severity and tic detection, Computer Vision strongly aligned with accelerometery and clinical assessments.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Computer Vision holds potential to provide non\u2010invasive quantification of presence and severity of movement disorders. Heterogeneity in video settings, software usage, and definition of standardized guidelines for videorecording are challenges to be addressed for real\u2010word applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/mdc3.70123","type":"journal-article","created":{"date-parts":[[2025,5,6]],"date-time":"2025-05-06T07:45:26Z","timestamp":1746517526000},"page":"1229-1243","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Computer Vision Technologies in Movement Disorders: A Systematic Review"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6186-8020","authenticated-orcid":false,"given":"Pasquale Maria","family":"Pecoraro","sequence":"first","affiliation":[{"name":"Operative Research Unit of Neurology Fondazione Policlinico Universitario Campus Bio\u2010Medico  Rome Italy"},{"name":"Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery Universit\u00e0 Campus Bio\u2010Medico di Roma  Rome Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Marsili","sequence":"additional","affiliation":[{"name":"James J. and Joan A. 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