ArticleAnnals of clinical and translational neurology2026
Deep Learning Pose Estimation for Phenotyping of Co-Occurring Hyperkinetic Movement Disorders.
Article in Annals of clinical and translational neurology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveTo 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).
methodsIn 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.
resultsIn 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%).
interpretationIn 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.
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