Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review
Arantxa Ortega-Leon1, Daniel Urda2, Ignacio J Turias1
1Intelligent Modelling of Systems Research Group, Department of Computer Science Engineering, Algeciras School of Engineering and Technology (ASET), University of Cádiz, Algeciras, Spain.
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.
