Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review
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.Methods...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1481338/full |
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| author | Arantxa Ortega-Leon Daniel Urda Ignacio J. Turias Simón P. Lubián-López Simón P. Lubián-López Isabel Benavente-Fernández Isabel Benavente-Fernández Isabel Benavente-Fernández |
| author_facet | Arantxa Ortega-Leon Daniel Urda Ignacio J. Turias Simón P. Lubián-López Simón P. Lubián-López Isabel Benavente-Fernández Isabel Benavente-Fernández Isabel Benavente-Fernández |
| author_sort | Arantxa Ortega-Leon |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-4621e472c35945f591a8eccf474f099c |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-4621e472c35945f591a8eccf474f099c2025-01-20T07:19:52ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-01-01810.3389/frai.2025.14813381481338Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic reviewArantxa Ortega-Leon0Daniel Urda1Ignacio J. Turias2Simón P. Lubián-López3Simón P. Lubián-López4Isabel Benavente-Fernández5Isabel Benavente-Fernández6Isabel Benavente-Fernández7Intelligent Modelling of Systems Research Group, Department of Computer Science Engineering, Algeciras School of Engineering and Technology (ASET), University of Cádiz, Algeciras, SpainGrupo de Inteligencia Computacional Aplicada (GICAP), Departamento de Digitalización, Escuela Politécnica Superior, Universidad de Burgos, Burgos, SpainIntelligent Modelling of Systems Research Group, Department of Computer Science Engineering, Algeciras School of Engineering and Technology (ASET), University of Cádiz, Algeciras, SpainBiomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Puerta del Mar University Hospital, Cádiz, SpainDepartment of Pediatrics, Neonatology Section, Puerta del Mar University Hospital, Cádiz, SpainBiomedical Research and Innovation Institute of Cádiz (INiBICA) Research Unit, Puerta del Mar University Hospital, Cádiz, SpainDepartment of Pediatrics, Neonatology Section, Puerta del Mar University Hospital, Cádiz, SpainPaediatrics Area, Department of Mother and Child Health and Radiology, Medical School, University of Cádiz, Cádiz, SpainBackground 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.https://www.frontiersin.org/articles/10.3389/frai.2025.1481338/fullmachine learningpreterm infantsneurodevelopmental impairmentNDIs predictionNDIs prognosis |
| spellingShingle | Arantxa Ortega-Leon Daniel Urda Ignacio J. Turias Simón P. Lubián-López Simón P. Lubián-López Isabel Benavente-Fernández Isabel Benavente-Fernández Isabel Benavente-Fernández Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review Frontiers in Artificial Intelligence machine learning preterm infants neurodevelopmental impairment NDIs prediction NDIs prognosis |
| title | Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review |
| title_full | Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review |
| title_fullStr | Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review |
| title_full_unstemmed | Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review |
| title_short | Machine learning techniques for predicting neurodevelopmental impairments in premature infants: a systematic review |
| title_sort | machine learning techniques for predicting neurodevelopmental impairments in premature infants a systematic review |
| topic | machine learning preterm infants neurodevelopmental impairment NDIs prediction NDIs prognosis |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1481338/full |
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