Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings

Daphné Chopard, Sonia Laguna, Kieran Chin-Cheong, Annika Dietz, Anna Badura, Sven Wellmann, Julia E Vogt
Proceedings of the 10th Machine Learning for Healthcare Conference, PMLR 298, 2025.

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 this Paper

BibTeX
@InProceedings{pmlr-v298-chopard25a, title = {Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings}, author = {Chopard, Daphn\'e 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}, editor = {Agrawal, Monica and Deshpande, Kaivalya and Engelhard, Matthew and Joshi, Shalmali and Tang, Shengpu and Urteaga, Iñigo}, volume = {298}, series = {Proceedings of Machine Learning Research}, month = {15--16 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v298/main/assets/chopard25a/chopard25a.pdf}, url = {https://proceedings.mlr.press/v298/chopard25a.html}, 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.} }
Endnote
%0 Conference Paper %T Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings %A Daphné Chopard %A Sonia Laguna %A Kieran Chin-Cheong %A Annika Dietz %A Anna Badura %A Sven Wellmann %A Julia E Vogt %B Proceedings of the 10th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2025 %E Monica Agrawal %E Kaivalya Deshpande %E Matthew Engelhard %E Shalmali Joshi %E Shengpu Tang %E Iñigo Urteaga %F pmlr-v298-chopard25a %I PMLR %U https://proceedings.mlr.press/v298/chopard25a.html %V 298 %X 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.
APA
Chopard, D., Laguna, S., Chin-Cheong, K., Dietz, A., Badura, A., Wellmann, S. & Vogt, J.E.. (2025). Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings. Proceedings of the 10th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 298 Available from https://proceedings.mlr.press/v298/chopard25a.html.

Related Material