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Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury
Maria Luisa Tataranno1, Daniel C Vijlbrief1, Jeroen Dudink1
1Department of Neonatology, Wilhelmina Children's Hospital/University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands.
Insights
Predicting long-term outcomes for neonates at risk of brain injury remains challenging. New machine learning algorithms and omics analysis offer hope for personalized neonatal neurodevelopmental assessment and precision medicine.
Area of Science:
- Neonatal neurology
- Neurodevelopmental disorders
- Precision medicine
Background:
- Neonatal brain injury and neurodevelopmental impairment pose significant challenges.
- Current prognostic methods (CUS, MRI, EEG, NIRS, general movements) lack individual predictive accuracy.
- Precision medicine is needed for individualized neonatal care.
Purpose of the Study:
- To review common neonatal neurological diseases, risk factors, and treatments.
- To explore the potential of machine learning and omics in neonatal prognosis.
- To discuss the future of precision medicine in predicting neonatal outcomes.
Main Methods:
- Review of current literature on neonatal neurological diseases and prognostication.
- Discussion of machine learning algorithms applied to clinical, neuromonitoring, neuroimaging, and genetic data.
- Exploration of multi-biomarker (omics) assays for enhanced prediction.
Main Results:
- Current methods for predicting neonatal outcomes are limited in precision.
- Machine learning and omics data offer a promising avenue for improved predictive accuracy.
- Synergistic application of diverse data types and quantitative analysis can enable patient-targeted decision-making.
Conclusions:
- Advances in machine learning and omics analysis are poised to revolutionize neonatal neurodevelopmental outcome prediction.
- Precision medicine approaches, integrating multi-modal data, will enable individualized diagnosis, therapy, and prognosis.
- Future neonatology will benefit from data-driven, patient-specific predictive models.
Abstract:
Despite advances in neonatal care to prevent neonatal brain injury and neurodevelopmental impairment, predicting long-term outcome in neonates at risk for brain injury remains difficult. Early prognosis is currently based on cranial ultrasound (CUS), MRI, EEG, NIRS, and/or general movements assessed at specific ages, and predicting outcome in an individual (precision medicine) is not yet possible. New algorithms based on large databases and machine learning applied to clinical, neuromonitoring, and neuroimaging data and genetic analysis and assays measuring multiple biomarkers (omics) can fulfill the needs of modern neonatology. A synergy of all these techniques and the use of automatic quantitative analysis might give clinicians the possibility to provide patient-targeted decision-making for individualized diagnosis, therapy, and outcome prediction. This review will first focus on common neonatal neurological diseases, associated risk factors, and most common treatments. After that, we will discuss how precision medicine and machine learning (ML) approaches could change the future of prediction and prognosis in this field.
