TY - JOUR
T1 - Precision Medicine in Neonates
T2 - A Tailored Approach to Neonatal Brain Injury
AU - Tataranno, Maria Luisa
AU - Vijlbrief, Daniel C
AU - Dudink, Jeroen
AU - Benders, Manon J N L
N1 - Publisher Copyright:
© Copyright © 2021 Tataranno, Vijlbrief, Dudink and Benders.
PY - 2021/5/19
Y1 - 2021/5/19
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - brain injury
KW - intraventricular hemorrhage
KW - newborn
KW - personalized medicine
KW - precision medicine
KW - preterm
KW - stroke
UR - https://www.youtube.com/watch?v=yYmnXAh9318
UR - http://www.scopus.com/inward/record.url?scp=85107227601&partnerID=8YFLogxK
U2 - 10.3389/fped.2021.634092
DO - 10.3389/fped.2021.634092
M3 - Review article
C2 - 34095022
SN - 2296-2360
VL - 9
SP - 1
EP - 13
JO - Frontiers in Pediatrics
JF - Frontiers in Pediatrics
M1 - 634092
ER -