SynthesisFrontiers in artificial intelligence2025
Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review.
Synthesis in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- From oxidative stress to epigenetic regulation: Molecular mechanisms of preterm brain injury and neuroprotective strategies (Review).Molecular medicine reports · 2026Review
- Neurodevelopmental Outcome in Very Low Birth Weight Preterm Infants: An Exploratory Multivariable Analysis Including Sonographic Brain Volume Trajectories-Data from the NeoNEVS Project.Children (Basel, Switzerland) · 2026Article
- Neurodevelopmental Trajectories in Preterm Neonates: Integrating Neuroimaging Modalities with Clinical Neurological Outcomes.Diagnostics (Basel, Switzerland) · 2026Review
- Diffuse white matter abnormality is independently predictive of neurodevelopmental outcomes in preterm infants.Archives of disease in childhood. Fetal and neonatal edition · 2026Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and objective: Very preterm infants are highly susceptible to Neurodevelopmental Impairments (NDIs), including cognitive, motor, and language deficits. This paper presents a systematic review of the application of Machine Learning (ML) techniques to predict NDIs in premature infants. Methods: This review presents a comparative analysis of existing studies from January 2018 to December 2023, highlighting their strengths, limitations, and future research directions. Results: We identified 26 studies that fulfilled the inclusion criteria. In addition, we explore the potential of ML algorithms and discuss commonly used data sources, including clinical and neuroimaging data. Furthermore, the inclusion of omics data as a contemporary approach employed, in other diagnostic contexts is proposed. Conclusions: We identified limitations and emphasized the significance of employing multimodal data models and explored various alternatives to address the limitations identified in the reviewed studies. The insights derived from this review guide researchers and clinicians toward improving early identification and intervention strategies for NDIs in this vulnerable population.
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Registered trials
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