Towards Scalable Newborn Screening: Automated General Movement Assessment in Uncontrolled Settings
This work addresses the limited availability of trained clinicians for newborn screening, potentially enabling broader access to early detection of neurodevelopmental disorders.
The paper tackled the problem of scaling up newborn screening for neurodevelopmental disorders by developing an algorithm to automatically classify general movements from infant video recordings, which are challenging due to variability in recording conditions and coarse annotations.
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.