Marker-Less Video Analysis of Infant Movements for Early Identification of Neurodevelopmental Disorders.

ABSTRACT: Background/Objectives: The early identification of neurodevelopmental disorders (NDDs) in infants is crucial for effective intervention and improved long-term outcomes. Recent evidence indicates a correlation between deficits in spontaneous movements in newborns and the likelihood of developing NDDs later in life. This study aims to address this aspect by employing a marker-less Artificial Intelligence (AI) approach for the automatic assessment of infants' movements from single-camera video recordings. Methods: A total of 74 high-risk infants were selected from the Italian Network for Early Detection of Autism Spectrum Disorders (NIDA) database and closely observed at five different time points, ranging from 10 days to 24 weeks of age. Automatic motion tracking was performed

SUBMITTER: Bruschetta R 

PROVIDER: S-EPMC11763807 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature