{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,7]],"date-time":"2026-10-07T16:02:40Z","timestamp":1791388960974,"version":"4.3.3"},"reference-count":41,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T00:00:00Z","timestamp":1784937600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T00:00:00Z","timestamp":1784937600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100001595","name":"Dystonia Medical Research Foundation","doi-asserted-by":"publisher","award":["126616598 RR0001"],"award-info":[{"award-number":["126616598 RR0001"]}],"id":[{"id":"10.13039\/100001595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Ann Clin Transl Neurol"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To explore whether routine outpatient video combined with deep learning\u2010based pose estimation and clinically interpretable kinematic features can support multi\u2010label phenotyping of co\u2010occurring hyperkinetic movement disorders (HMDs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>In this exploratory single\u2010centre proof\u2010of\u2010concept study, videos from 21 patients with HMDs and 4 healthy controls were processed with markerless pose estimation (YOLOv8) and 2\u2010dimensional keypoint trajectories transformed into kinematic descriptors spanning statistical, temporal, spectral, and complexity domains. Ten\u2010second windows were aligned to expert annotations for eight hyperkinetic phenomenologies. Conventional supervised classifiers were trained on these tabular descriptors. Window\u2010level predictions were aggregated to the patient level, and label\u2010specific thresholds were tuned on training participants only.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In patient\u2010level multi\u2010label performance reporting, (i) the best single pipeline selected by discrimination (StandardScaler + MLP) achieved a macro\u2010AUPRC of 0.821\u2009\u00b1\u20090.019 and a macro\u2013receiver operating characteristic area under the curve of 0.830\u2009\u00b1\u20090.029. (ii) The best single pipeline selected by Hamming accuracy (MinMaxScaler + SVM) reached 0.764\u2009\u00b1\u20090.041. (iii) Under prespecified nested cross\u2010validation with per\u2010label model selection within training folds (primary analysis), macro\u2010AUPRC was 0.717\u2009\u00b1\u20090.030, macro\u2010AUROC was 0.767\u2009\u00b1\u20090.069, Hamming accuracy was 0.764\u2009\u00b1\u20090.014 and patient\u2013label agreement was 153\/200 (76.5%). (iv) Post hoc per\u2010label selection of the best\u2010performing pipeline defined an exploratory upper bound of 172\/200 (86.0%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Interpretation<\/jats:title>\n                    <jats:p>In this exploratory study, a hybrid pipeline combining deep learning pose estimation with feature\u2010engineered supervised classification produced encouraging patient\u2010level multi\u2010label performance for co\u2010occurring HMDs. These findings are proof\u2010of\u2010concept; external, multicentre, prospective validation is required before clinical or trial use.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/acn3.70474","type":"journal-article","created":{"date-parts":[[2026,7,25]],"date-time":"2026-07-25T14:38:15Z","timestamp":1784990295000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Deep Learning Pose Estimation for Phenotyping of Co\u2010Occurring Hyperkinetic Movement Disorders"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9062-1665","authenticated-orcid":false,"given":"Laura","family":"Cif","sequence":"first","affiliation":[{"name":"Service of Neurology, Department of Clinical Neurosciences Lausanne University Hospital (CHUV), University of Lausanne (UNIL)  Lausanne Switzerland"},{"name":"Institut du Neurone  Montferrier sur Lez 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University of Lausanne (UNIL)  Lausanne Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4427-5765","authenticated-orcid":false,"given":"Thomas","family":"Wirth","sequence":"additional","affiliation":[{"name":"Department of Neurology University Hospital of Strasbourg  Strasbourg France"},{"name":"Strasbourg Neuroscience Institute Strasbourg University  Strasbourg France"},{"name":"Institute of Genetics and Cellular and Molecular Biology, INSERM\u2010U964, CNRS\u2010UMR7104 University of Strasbourg  Illkirch\u2010Graffenstaden France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gun\u2010Marie","family":"Hariz","sequence":"additional","affiliation":[{"name":"Department of Clinical Neuroscience Ume\u00e5 University  Ume\u00e5 Sweden"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sophie","family":"Huby","sequence":"additional","affiliation":[{"name":"Department of Neurology CHU Montpellier  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UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simone","family":"Hemm","sequence":"additional","affiliation":[{"name":"Institute for Medical Engineering and Medical Informatics, School of Life Sciences University of Applied Sciences and Arts Northwestern Switzerland  Muttenz Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mehdi","family":"Boulaymen","sequence":"additional","affiliation":[{"name":"Institut du Neurone  Montferrier sur Lez France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eduardo M.","family":"Moraud","sequence":"additional","affiliation":[{"name":"NeuroRestore, Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne University Hospital Lausanne, University of Lausanne  Lausanne Switzerland"},{"name":"Neuro\u2010X Institute Ecole Polytechnique F\u00e9d\u00e9rale de Lausanne  Geneva 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