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SYSTEMATIC REVIEW article

Front. Artif. Intell.
Sec. Machine Learning and Artificial Intelligence
Volume 8 - 2025 | doi: 10.3389/frai.2025.1481338

Machine learning techniques for predicting neurodevelopmental impairment in premature infants: a systematic review

Provisionally accepted
Arantxa Ortega-Leon Arantxa Ortega-Leon 1*Daniel Urda Daniel Urda 2Ignacio Turias Ignacio Turias 1Simón Lubián-López Simón Lubián-López 3,4Isabel Benavente-Fernádez Isabel Benavente-Fernádez 1,3,4
  • 1 University of Cádiz, Cádiz, Spain
  • 2 University of Burgos, Burgos, Spain
  • 3 Hospital Universitario Puerta del Mar, Cadiz, Spain
  • 4 Institute for Biomedical Research and Innovation of Cádiz, University of Cádiz, Cádiz, Spain

The final, formatted version of the article will be published soon.

    For full guidelines regarding your manuscript please refer to 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.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.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.

    Keywords: machine learning, preterm infants, neurodevelopmental impairment, NDIs prediction, NDIs prognosis

    Received: 15 Aug 2024; Accepted: 02 Jan 2025.

    Copyright: © 2025 Ortega-Leon, Urda, Turias, Lubián-López and Benavente-Fernádez. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Arantxa Ortega-Leon, University of Cádiz, Cádiz, Spain

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.