ArticleJournal of clinical medicine2019
Machine Learning of Infant Spontaneous Movements for the Early Prediction of Cerebral Palsy: A Multi-Site Cohort Study.
Article in Journal of clinical medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it, 117 citations in OpenAlex.
- Pooled it
- Development and Validation of a Deep Learning Method to Predict Cerebral Palsy From Spontaneous Movements in Infants at High Risk.JAMA network open · 2022Trial
- Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants.Scientific reports · 2022Trial
- Video and Wearable Sensor Technologies for Early Detection of Cerebral Palsy in Infants: A Scoping Review.Journal of clinical medicine · 2026Review
- A preregistered, open pipeline for early cerebral palsy risk assessment from infant videos.GigaScience · 2026Article
- An Automated Framework for Pose-Sequence Analysis of Spontaneous Movements in Preterm Infants.IEEE open journal of engineering in medicine and biology · 2026Article
- Using a coloring activity to identify children's development of visual-motor integration: an application of artificial intelligence.Annals of medicine · 2025Article
- Digital Twins for Monitoring Neuromotor Development in Preterm Infants: Conceptual Framework and Proof-of-concept Study.Journal of medical systems · 2025Article
- Estimating Ground Reaction Forces from Gait Kinematics in Cerebral Palsy: A Convolutional Neural Network Approach.Annals of biomedical engineering · 2025Article
- Can AI-Based Video Analysis Help Evaluate the Performance of the Items in the Bayley Scales of Infant Development?Children (Basel, Switzerland) · 2025Article
- Data-Driven Early Prediction of Cerebral Palsy Using AutoML and interpretable kinematic features.medRxiv : the preprint server for health sciences · 2025Article
- A systematic review of portable technologies for the early assessment of motor development in infants.NPJ digital medicine · 2025Article
- Deep learning empowered sensor fusion boosts infant movement classification.Communications medicine · 2025Article
- Predicting Age and Visual-Motor Integration Using Origami Photographs: Deep Learning Study.JMIR formative research · 2025Article
- Quantifying spontaneous infant movements using state-space models.Scientific reports · 2024Article
- Reduction in limb-movement complexity at term-equivalent age is associated with motor developmental delay in very-preterm or very-low-birth-weight infants.Scientific reports · 2024Article
- Machine learning approaches to evaluate infants' general movements in the writhing stage-a pilot study.Scientific reports · 2024Article
- Automated identification of abnormal infant movements from smart phone videos.PLOS digital health · 2024Article
- Artificial intelligence and machine learning approaches in cerebral palsy diagnosis, prognosis, and management: a comprehensive review.PeerJ. Computer science · 2024Review
- Using the center of pressure movement analysis in evaluating spontaneous movements in infants: a comparative study with general movements assessment.Italian journal of pediatrics · 2023Article
Corrections and comments
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Authors and funding
17 authors at 8 institutions in 2 countries.
Funding
Abstract
backgroundEarly identification of cerebral palsy (CP) during infancy will provide opportunities for early therapies and treatments. The aim of the present study was to present a novel machine-learning model, the Computer-based Infant Movement Assessment (CIMA) model, for clinically feasible early CP prediction based on infant video recordings.
methodsThe CIMA model was designed to assess the proportion (%) of CP risk-related movements using a time-frequency decomposition of the movement trajectories of the infant's body parts. The CIMA model was developed and tested on video recordings from a cohort of 377 high-risk infants at 9-15 weeks corrected age to predict CP status and motor function (ambulatory vs. non-ambulatory) at mean 3.7 years age. The performance of the model was compared with results of the general movement assessment (GMA) and neonatal imaging.
resultsThe CIMA model had sensitivity (92.7%) and specificity (81.6%), which was comparable to observational GMA or neonatal cerebral imaging for the prediction of CP. Infants later found to have non-ambulatory CP had significantly more CP risk-related movements (median: 92.8%,
conclusionThe CIMA model may be a clinically feasible alternative to observational GMA.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.