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Spontaneous movements as prognostic tool of neurodevelopmental outcomes in preterm infants: a narrative review
Hyun Iee Shin1, Myung Woo Park1, Woo Hyung Lee2
1Department of Rehabilitation Medicine, Chung-Ang University Hospital, Chung-Ang University College of Medicine, Seoul, Korea.
Insights
Preterm infants at high risk for cerebral palsy can be identified using general movements, which are spontaneous body movements. Automated analysis of these movements offers a promising, objective approach for early detection and intervention.
Area of Science:
- Neonatal care
- Neurodevelopmental outcomes
- Biomarkers for infant brain impairment
Background:
- Premature birth affects millions globally, with survivors facing neurodevelopmental challenges.
- Cerebral palsy is a significant complication in preterm infants, necessitating early detection.
- General movements (GMs) reflect neural activity and serve as a key biomarker for brain dysfunction.
Purpose of the Study:
- To review normal and abnormal general movements in preterm infants.
- To explore the predictive value of GMs for cerebral palsy.
- To summarize advancements in automated analysis of GMs using machine learning.
Main Methods:
- Review of existing literature on general movements and cerebral palsy prediction.
- Analysis of spontaneous infantile movements for neural dysfunction detection.
- Exploration of machine learning algorithms for automated GM assessment.
Main Results:
- Continuous observation of general movements enhances their predictive value for cerebral palsy.
- Automated approaches overcome limitations of traditional qualitative GM assessments.
- Machine learning enables objective and scalable analysis of infantile spontaneous movements.
Conclusions:
- General movements are crucial for identifying preterm infants at high risk of cerebral palsy.
- Automated analysis of GMs using machine learning shows significant potential for early detection.
- Advancing these methods can improve neurodevelopmental outcomes for preterm infants.
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.
