ArticleNature communications2023
Automating General Movements Assessment with quantitative deep learning to facilitate early screening of cerebral palsy.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed.
- Standardizing early cerebral palsy detection in high-risk infants: reducing age at diagnosis through a quality improvement initiative.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- Automated contactless radar-derived movement index for outpatient motor surveillance.Pediatric research · 2026Article
- Automatic Infant Movement Assessment Using Pose-LBP Features and a Cost-Sensitive Subspace kNN Ensemble.Bioengineering (Basel, Switzerland) · 2026Article
- The future of follow-up: technology-enabled medical and developmental follow-up care for NICU graduates.Journal of perinatology : official journal of the California Perinatal Association · 2026Review
- Infants' spontaneous movements explore arm dynamics.Communications biology · 2026Article
- Automatic pose estimation in newborn infants: Lessons from the Baby Grow study.Behavior research methods · 2026Article
- A preregistered, open pipeline for early cerebral palsy risk assessment from infant videos.GigaScience · 2026Article
- Multimodal intelligent prediction model for in vitro fertilization.NPJ digital medicine · 2026Article
- Applying artificial intelligence in neurodevelopmental disorders management and research.European journal of medical research · 2026Review
- Emerging modalities for neuroprognostication in neonatal encephalopathy: harnessing the potential of artificial intelligence.Pediatric research · 2025Review
- Movement smoothness at term-equivalent age is associated with motor developmental delay in preterm infants.Scientific reports · 2025Article
- Towards universal early screening for cerebral palsy: a roadmap for automated General Movements Assessment.EClinicalMedicine · 2025Review
- The Pathway Is Clear but the Road Remains Unpaved: A Scoping Review of Implementation of Tools for Early Detection of Cerebral Palsy.Children (Basel, Switzerland) · 2025Review
- Autonomous screening of infants at high risk for neurodevelopmental impairments using a radar sensor and machine learning.Scientific reports · 2025Article
- Autonomous Self-Evolving Research on Biomedical Data: The DREAM Paradigm.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Sex-Related Differences in Hip Kinematics During General Movements in Early Infancy: A Biomechanical Cross-Sectional Study.Children (Basel, Switzerland) · 2025Article
- Comparison of marker-less 2D image-based methods for infant pose estimation.Scientific reports · 2025Article
- Data-Driven Early Prediction of Cerebral Palsy Using AutoML and interpretable kinematic features.medRxiv : the preprint server for health sciences · 2025Article
- Automation to approximate the Gestalt: applying machine learning to the general movement assessment.Pediatric research · 2025Article
- A systematic review of portable technologies for the early assessment of motor development in infants.NPJ digital medicine · 2025Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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 accurately identify FMs within videos, showing substantial agreement with expert assessments. Leveraging the predicted FMs frequency, a quantitative GMA method is introduced, which achieves an AUC of 0.956 and enhances the diagnostic accuracy of GMA beginners by 11.0%. The development of MAM holds the potential to significantly streamline early CP screening and revolutionize the field of video-based quantitative medical diagnostics.
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