RT journal article
T1 Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review
A1 Ortega León, Arantxa Mireya
A1 Urda, Daniel
A1 Turias Domínguez, Ignacio José
A1 Lubián López, Simón Pedro
A1 Benavente Fernández, Isabel
A2 Ingeniería Informática
A2 Materno-Infantil y Radiología
K1 machine learning
K1 preterm infants
K1 neurodevelopmental impairment
K1 NDIs prediction
K1 NDIs prognosis
AB 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.
PB Frontiers
SN 2624-8212
YR 2025
FD 2025
LK http://hdl.handle.net/10498/36355
UL http://hdl.handle.net/10498/36355
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 10-nov-2026