Facilitate Robust Early Screening of Cerebral Palsy via General Movements Assessment with Multi-Modality Co-Learning

Wang Yin , Chunling Huang , Linxi Chen

IEEE Transactions on Medical Imaging +

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December 9, 2025

View abstract on PubMed

Summary

CoGMA, a novel AI framework, enhances general movement assessment (GMA) for early cerebral palsy (CP) detection. It uses multimodal data for training, enabling accurate infant neuromotor behavior analysis with skeleton and clinical data alone.

Area Of Science

Neurology
Artificial Intelligence
Infant Development

Background

General Movement Assessment (GMA) is crucial for early cerebral palsy (CP) detection in infants.
Traditional GMA relies on subjective physician judgment, limiting accessibility and scalability.
Existing AI methods for GMA often lack detailed body information, relying solely on motion skeletons.

Purpose Of The Study

To introduce CoGMA, a multi-modality co-learning framework for enhanced General Movement Assessment.
To improve the accuracy and efficiency of neuromotor behavior evaluation in infants.
To address limitations of traditional GMA and current AI approaches.

Main Methods

Developed CoGMA, a novel framework integrating skeleton data, clinical information, RGB video, and text descriptions using a multimodal large language model.
Employed a co-learning strategy during training to enhance representation learning.
Achieved efficient and accurate prediction during inference using only skeleton data and clinical information.

Main Results

CoGMA demonstrated robust performance in evaluating both writhing and fidgety movement stages in infants.
The framework excelled in zero-shot evaluation of fidget movements, overcoming limited training sample issues.
CoGMA significantly enhances GMA methodology for early detection of neuromotor disorders.

Conclusions

CoGMA offers a significant advancement in infant neuromotor behavior assessment and early cerebral palsy detection.
The framework's efficiency and accuracy pave the way for broader clinical application and research.
InfantAnimator tool developed to facilitate anonymized data sharing and collaborative research.