Publication

Deep Learning Pose Estimation for Phenotyping of Co‐Occurring Hyperkinetic Movement Disorders

2026

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.

Official source

https://infoscience.epfl.ch/entities/publication/ac35504c-78fc-40cb-866f-e57ba491bd9c

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2026

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/ac35504c-78fc-40cb-866f-e57ba491bd9c

About this result

Related concepts (32)

Machine learning

Machine learning (ML) is an umbrella term for solving problems for which development of algorithms by human programmers would be cost-prohibitive, and instead the problems are solved by helping machines 'discover' their 'own' algorithms, without needing to be explicitly told what to do by any human-developed algorithms. Recently, generative artificial neural networks have been able to surpass results of many previous approaches.

Model selection

Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. In the context of learning, this may be the selection of a statistical model from a set of candidate models, given data. In the simplest cases, a pre-existing set of data is considered. However, the task can also involve the design of experiments such that the data collected is well-suited to the problem of model selection.

Patient safety

Patient safety is a discipline that emphasizes safety in health care through the prevention, reduction, reporting and analysis of error and other types of unnecessary harm that often lead to adverse patient events. The frequency and magnitude of avoidable adverse events, often known as patient safety incidents, experienced by patients was not well known until the 1990s, when multiple countries reported significant numbers of patients harmed and killed by medical errors.

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