ArticleClinical and experimental pediatrics2023
Spontaneous movements as prognostic tool of neurodevelopmental outcomes in preterm infants: a narrative review.
Article in Clinical and experimental pediatrics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 7 citations in OpenAlex.
- Evaluating the effects of maternal diabetes on early infant neurodevelopment with a novel combination of four neurodevelopmental tools.Early human development · 2026Article
- Early Upper Limb Function in Infants Under Three Months: Associations with Shoulder Biomechanics and General Movement Patterns.Pediatric reports · 2025Article
- Evaluating Non-Invasive Computer Vision-Based Quantification of Neonatal Movement as a Marker of Development in Preterm Infants: A Pilot Study.Healthcare (Basel, Switzerland) · 2025Article
- NeoVault: empowering neonatal research through a neonate data hub.BMC pediatrics · 2024Article
- Neurodevelopmental trajectories from birth to childhood and their impairments: insights from passive, spontaneous, and locomotor-like movements.Frontiers in neurologyReview
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An estimated 15 million infants are born prematurely each year. Although the survival rate of preterm infants has increased with advances in perinatal and neonatal care, many still experience various complications. Since improving the neurodevelopmental outcomes of preterm births is a crucial topic, accurate evaluations should be performed to detect infants at high risk of cerebral palsy. General movements are spontaneous movements involving the whole body as the expression of neural activity and can be an excellent biomarker of neural dysfunction caused by brain impairment in preterm infants. The predictive value of general movements with respect to cerebral palsy increases with continuous observation. Automated approaches to examining general movements based on machine learning can help overcome the limited utilization of assessment tools owing to their qualitative or semiquantitative nature and high dependence on assessor skills and experience. This review covers each of these topics by summarizing normal and abnormal general movements as well as recent advances in automatic approaches based on infantile spontaneous movements.
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Registered trials
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