Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review - Ortega-Leon, Arantxa; Urda, Daniel; Turias, Ignacio J.; Lubián-López, Simón P.; Benavente-Fernández, Isabel (Frontiers in Artificial Intelligence, 2025)
Background and objectiveVery 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.MethodsThis review presents a comparative analysis of existing studies from January 2018 to December 2023, highlighting their strengths, limitations, and future research directions.ResultsWe 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.ConclusionsWe 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.