{
  "abstract": "OBJECTIVE: To explore whether routine outpatient video combined with deep learning-based pose estimation and clinically interpretable kinematic features can support multi-label phenotyping of co-occurring hyperkinetic movement disorders (HMDs). METHODS: In this exploratory single-centre proof-of-concept study, videos from 21 patients with HMDs and 4 healthy controls were processed with markerless pose estimation (YOLOv8) and 2-dimensional keypoint trajectories transformed into kinematic descriptors spanning statistical, temporal, spectral, and complexity domains. Ten-second windows were aligned to expert annotations for eight hyperkinetic phenomenologies. Conventional supervised classifiers were trained on these tabular descriptors. Window-level predictions were aggregated to the patient level, and label-specific thresholds were tuned on training participants only. RESULTS: In patient-level multi-label performance reporting, (i) the best single pipeline selected by discrimination (StandardScaler + MLP) achieved a macro-AUPRC of 0.821 ± 0.019 and a macro-receiver operating characteristic area under the curve of 0.830 ± 0.029. (ii) The best single pipeline selected by Hamming accuracy (MinMaxScaler + SVM) reached 0.764 ± 0.041. (iii) Under prespecified nested cross-validation with per-label model selection within training folds (primary analysis), macro-AUPRC was 0.717 ± 0.030, macro-AUROC was 0.767 ± 0.069, Hamming accuracy was 0.764 ± 0.014 and patient-label agreement was 153/200 (76.5%). (iv) Post hoc per-label selection of the best-performing pipeline defined an exploratory upper bound of 172/200 (86.0%). INTERPRETATION: In this exploratory study, a hybrid pipeline combining deep learning pose estimation with feature-engineered supervised classification produced encouraging patient-level multi-label performance for co-occurring HMDs. These findings are proof-of-concept; external, multicentre, prospective validation is required before clinical or trial use.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2602.00163",
  "author_total": 18,
  "authors": [
    {
      "name": "Laura Cif"
    },
    {
      "name": "Diane Demailly"
    },
    {
      "name": "Gabriella Horvàth"
    },
    {
      "name": "Juan Dario Ortigoza Escobar"
    },
    {
      "name": "Nathalie Dorison"
    },
    {
      "name": "Mayté Castro Jiménez"
    },
    {
      "name": "Cécile Hubsch"
    },
    {
      "name": "Thomas Wirth"
    },
    {
      "name": "Gun‐marie Hariz"
    },
    {
      "name": "Sophie Huby"
    },
    {
      "name": "Morgan Dornadic"
    },
    {
      "name": "Zohra Souei"
    },
    {
      "name": "Muhammad Mushhood Ur Rehman"
    },
    {
      "name": "Simone Hemm"
    },
    {
      "name": "Mehdi Boulaymen"
    },
    {
      "name": "Eduardo Moraud"
    },
    {
      "name": "Jocelyne Bloch"
    },
    {
      "name": "Xavier Vasques"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/5jaqgk76scxhz.bib",
  "cited_by": 0,
  "doi": "10.1002/acn3.70474",
  "fulltext_url": null,
  "id": "5jaqgk76scxhz",
  "issns": [
    "2328-9503",
    "2328-9503"
  ],
  "landing_url": "https://hal.science/hal-05795909v1",
  "language": "en",
  "licence": "CC-BY",
  "licence_label": "CC BY",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "hal",
      "0.sdv"
    ],
    [
      "openalex",
      "1707|Human Pose and Action Recognition"
    ]
  ],
  "openalex": "W7171135962",
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-25",
  "publisher": "Wiley",
  "ris": "https://research.draconitic.com/paper/5jaqgk76scxhz.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:55Z",
      "native_id": "hal-05795909",
      "source": "hal",
      "url": "https://hal.science/hal-05795909v1"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "hal:0.sdv",
      "subfield": "general"
    }
  ],
  "title": "Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders",
  "title_text": "Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:55Z",
  "url": "https://research.draconitic.com/paper/5jaqgk76scxhz",
  "venue": "Annals of Clinical and Translational Neurology",
  "versions": []
}