Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings
Abstract
General movements (GMs) are spontaneous, coordinated body movements in infants that offer valuable insights into the developing nervous system. Assessed through the Prechtl GM Assessment (GMA), GMs are reliable predictors for neurodevelopmental disorders. However, GMA requires specifically trained clinicians, who are limited in number. To scale up newborn screening, there is a need for an algorithm that can automatically classify GMs from infant video recordings. This data poses challenges, including variability in recording length, device type, and setting, with each video coarsely annotated for overall movement quality. In this work, we introduce a tool for extracting features from these recordings and explore various machine learning techniques for automated GM classification.
Cite
Text
Chopard et al. "Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings." Proceedings of the 10th Machine Learning for Healthcare Conference, 2025.Markdown
[Chopard et al. "Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings." Proceedings of the 10th Machine Learning for Healthcare Conference, 2025.](https://mlanthology.org/mlhc/2025/chopard2025mlhc-scalable/)BibTeX
@inproceedings{chopard2025mlhc-scalable,
title = {{Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings}},
author = {Chopard, Daphné and Laguna, Sonia and Chin-Cheong, Kieran and Dietz, Annika and Badura, Anna and Wellmann, Sven and Vogt, Julia E},
booktitle = {Proceedings of the 10th Machine Learning for Healthcare Conference},
year = {2025},
volume = {298},
url = {https://mlanthology.org/mlhc/2025/chopard2025mlhc-scalable/}
}