Should early cranial MRI of preterm infants become routine?

Vries, Linda S. de; Benders, Manon J. N. L.; Groenendaal, Floris

doi:10.1136/archdischild-2014-308077

Cited by 7 publications

(1 citation statement)

References 11 publications

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“…Quantitative brain MRI aims to offer objective and reliable measures of brain structure, function, and brain connectivity, in the normal and abnormal brain. The main aims of quantitative neonatal MRI are: the development of automatic algorithms for images interpretation ( 58 ), the detection, measurement, and characterization of “subtle” brain abnormalities/injuries ( 59 , 60 ), and prediction of behavior, cognitive and motor long term outcome based on sophisticated algorithms ( 61 ). Quantitative MRI analysis is based on the use of multiple software packages capable of drawing together neuroimaging data processing routines from across, linking them together to implement end-to-end processing and analytic solutions.…”

Section: Mrimentioning

confidence: 99%

Precision Medicine in Neonates: A Tailored Approach to Neonatal Brain Injury

et al. 2021

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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.

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