Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model

Autor:	Susmita Saha, Alex Pagnozzi, Pierrick Bourgeat, Joanne M. George, DanaKai Bradford, Paul B. Colditz, Roslyn N. Boyd, Stephen E. Rose, Jurgen Fripp, Kerstin Pannek
Jazyk:	angličtina
Rok vydání:	2020
Předmět:	Preterm infants Neurodevelopment Motor outcome Neuro-sensory motor development assessment Deep learning Convolutional neural network Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Zdroj:	NeuroImage, Vol 215, Iss , Pp 116807- (2020)
Druh dokumentu:	article
ISSN:	1095-9572
DOI:	10.1016/j.neuroimage.2020.116807
Popis:	Background and aims: Preterm birth imposes a high risk for developing neuromotor delay. Earlier prediction of adverse outcome in preterm infants is crucial for referral to earlier intervention. This study aimed to predict abnormal motor outcome at 2 years from early brain diffusion magnetic resonance imaging (MRI) acquired between 29 and 35 weeks postmenstrual age (PMA) using a deep learning convolutional neural network (CNN) model. Methods: Seventy-seven very preterm infants (born
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/746ec08494bc439fbf01ce01e08c8755 Zobrazit plný text záznamu Full Text from ScienceDirect View record in DOAJ