Automating General Movements Assessment with quantitative deep learning to facilitate early screening of cerebral palsy.

ABSTRACT: The Prechtl General Movements Assessment (GMA) is increasingly recognized for its role in evaluating the integrity of the developing nervous system and predicting motor dysfunctions, particularly in conditions such as cerebral palsy (CP). However, the necessity for highly trained professionals has hindered the adoption of GMA as an early screening tool in some countries. In this study, we propose a deep learning-based motor assessment model (MAM) that combines infant videos and basic characteristics, with the aim of automating GMA at the fidgety movements (FMs) stage. MAM demonstrates strong performance, achieving an Area Under the Curve (AUC) of 0.967 during external validation. Importantly, it adheres closely to the principles of GMA and exhibits robust interpretability, as it can accura

SUBMITTER: Gao Q 

PROVIDER: S-EPMC10721621 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature