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.
Insights
Machine learning (ML) models can predict Neurodevelopmental Impairments (NDIs) in very preterm infants. This review analyzes ML applications, suggesting multimodal data for improved early identification and intervention strategies.
Area of Science:
- Neonatal Medicine
- Artificial Intelligence
- Neuroscience
Background:
- Very preterm infants face high risks of Neurodevelopmental Impairments (NDIs), affecting cognitive, motor, and language skills.
- Early identification of NDIs is crucial for timely intervention in this vulnerable population.
Purpose of the Study:
- To systematically review the application of Machine Learning (ML) techniques for predicting NDIs in premature infants.
- To provide a comparative analysis of ML studies in this field from 2018-2023.
Main Methods:
- Systematic review of scientific literature published between January 2018 and December 2023.
- Analysis of 26 selected studies focusing on ML algorithms and data sources for NDI prediction.
- Exploration of common data types including clinical, neuroimaging, and omics data.
Main Results:
- Identified 26 relevant studies applying ML to predict NDIs in preterm infants.
- Commonly utilized data sources include clinical and neuroimaging data.
- Omics data is proposed as a valuable addition for enhanced predictive accuracy.
Conclusions:
- Multimodal data models are significant for overcoming limitations in current NDI prediction.
- Insights guide researchers and clinicians in developing improved early identification and intervention strategies.
- Enhanced ML approaches promise better outcomes for preterm infants at risk of NDIs.
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.
