Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review
Baker, Stephanie, and Kandasamy, Yogavijayan (2023) Machine learning for understanding and predicting neurodevelopmental outcomes in premature infants: a systematic review. Pediatric Research, 93. pp. 293-299.
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Abstract
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.
| Item ID: | 74365 |
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| Item Type: | Article (Research - C1) |
| ISSN: | 1530-0447 |
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| Copyright Information: | © The Author(s) 2022. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
| Date Deposited: | 01 Jun 2022 02:30 |
| FoR Codes: | 46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460299 Artificial intelligence not elsewhere classified @ 50% 32 BIOMEDICAL AND CLINICAL SCIENCES > 3213 Paediatrics > 321303 Neonatology @ 50% |
| SEO Codes: | 20 HEALTH > 2005 Specific population health (excl. Indigenous health) > 200506 Neonatal and child health @ 50% 22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220403 Artificial intelligence @ 50% |
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