[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127208-en":3,"doc-seo-127208-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},127208,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Machine learning techniques for predicting neurodevelopmental impairments in premature infants - a systematic review","Very preterm infants face high susceptibility to neurodevelopmental impairments (NDIs), spanning cognitive, motor, and language deficits. A systematic review evaluates how machine learning (ML) methods are used to predict NDIs in premature infants. The review compares studies published from January 2018 to December 2023, identifying strengths, limitations, and future research needs. Findings highlight the relevance of multimodal data models, common clinical and neuroimaging sources, and proposed roles for omics data to support earlier identification and intervention.","TYPE Systematic Review PUBLISHED 20 January 2025 DOI 10. 3389/frai.2025.1481338  \nOPEN ACCESS  \nEDITED BY  \nGeorgios Leontidis,  \nUniversity of Aberdeen, United Kingdom  \nREVIEWED BY  \nAjey Kumar,  \nSymbiosis International (Deemed University), India  \nAngela Villareal,  \nNational Open and Distance University, Colombia  \n*CORRESPONDENCE  \nArantxa Ortega-Leon  \n [arantxa.ortega@uca.es](arantxa.ortega@uca.es)  \nRECEIVED 15 August 2024  \nACCEPTED 02 January 2025  \nPUBLISHED 20 January 2025  \nCITATION  \nOrtega-Leon A, Urda D, Turias IJ,  \nLubián-López SP and Benavente-Fernández I (2025) Machine learning techniques for predicting neurodevelopmental impairmentsin premature infants: a systematic review.  \nFront. Artif. Intell. 8:1481338 .  \ndoi: 10.3389/frai.2025.1481338  \nCOPYRIGHT  \n© 2025 Ortega-Leon, Urda, Turias,  \nLubián-López and Benavente-Fernández. 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) and the copyright owner(s) 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.  \nMachine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review  \nArantxa Ortega-Leon1*, Daniel Urda2 , Ignacio J. Turias1 ,  \nSimón P. Lubián-López3,4 and Isabel Benavente-Fernández3,4,5  \n1 Intelligent Modelling of Systems Research Group, Department of Computer Science Engineering, Algeciras School of Engineering and Technology (ASET), University of Cádiz, Algeciras, Spain, 2 Grupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Digitalización, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain, 3 Biomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Puerta del Mar University Hospital, Cádiz, Spain, 4 Department of Pediatrics, Neonatology Section, Puerta del Mar University Hospital, Cádiz, Spain, 5 Paediatrics Area, Department of Mother and Child Health and Radiology, Medical School, University of Cádiz, Cádiz, Spain  \nBackground 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.  \nMethods: This review presents a comparative analysis of existing studies from January 2018 to December 2023, highlighting their strengths, limitations, and future research directions.  \nResults: 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.  \nConclusions: 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.  \nKEYWORDS  \nmachine learning, preterm infants, neurodevelopmental impairment, NDIs prediction, NDIs prognosis  \n1 Introduction  \nIn this paper, we explore key topics related to preterm infants. We will review which factors are associated with long-term NeuroDevelopmental Impairments (NDIs) in this high-risk population and evaluate machine learning models applied to predict NDIs in very preterm infants (VPIs) .  \nVery preterm infants are de􀀂ned as those born before 32 weeks of gestation, and are exposed to a higher risk of NDIs. 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