Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic
Stephanie Baker1, Yogavijayan Kandasamy2,3
1College of Science and Engineering, James Cook University, Cairns, QLD, 4878, Australia. stephanie.baker@jcu.edu.au.
Insights
Machine learning shows promise for predicting neurodevelopmental outcomes in preterm infants. However, further research is needed to explore techniques and identify key predictive features for these infants.
Area of Science:
- Neonatal Medicine
- Neuroscience
- Artificial Intelligence
Background:
- Machine learning is increasingly utilized in healthcare, particularly in neonatal medicine.
- Predicting neurodevelopmental outcomes in preterm infants is a key application.
- This study systematically reviews current findings and challenges in this area.
Conclusions:
- Initial machine learning studies demonstrate promising results for predicting preterm infant neurodevelopmental outcomes.
- Many machine learning techniques require further exploration.
- Consensus on the most predictive clinical and brain features is yet to be established.
Background:
Machine learning has been attracting increasing attention for use in healthcare applications, including neonatal medicine. One application for this tool is in understanding and predicting neurodevelopmental outcomes in preterm infants. In this study, we have carried out a systematic review to identify findings and challenges to date.
Methods:
This systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. Four databases were searched in February 2022, with articles then screened in a non-blinded manner by two authors.
Results:
The literature search returned 278 studies, with 11 meeting the eligibility criteria for inclusion. Convolutional neural networks were the most common machine learning approach, with most studies seeking to predict neurodevelopmental outcomes from images and connectomes describing brain structure and function. Studies to date also sought to identify features predictive of outcomes; however, results varied greatly.
Conclusions:
Initial studies in this field have achieved promising results; however, many machine learning techniques remain to be explored, and the consensus is yet to be reached on which clinical and brain features are most predictive of neurodevelopmental outcomes.
Impact:
This systematic review looks at the question of whether machine learning can be used to predict and understand neurodevelopmental outcomes in preterm infants. Our review finds that promising initial works have been conducted in this field, but many challenges and opportunities remain. Quality assessment of relevant articles is conducted using the Newcastle-Ottawa Scale. This work identifies challenges that remain and suggests several key directions for future research. To the best of the authors' knowledge, this is the first systematic review to explore this topic.
