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• W2034345055 abstract "We propose an automated framework for predicting gestational age (GA) and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. Our method capitalizes on age-related sonographic image patterns in conjunction with clinical measurements to develop, for the first time, a predictive age model which improves on the GA-prediction potential of US images. The framework benefits from a manifold surface representation of the fetal head which delineates the inner skull boundary and serves as a common coordinate system based on cranial position. This allows for fast and efficient sampling of anatomically-corresponding brain regions to achieve like-for-like structural comparison of different developmental stages. We develop bespoke features which capture neurosonographic patterns in 3D images, and using a regression forest classifier, we characterize structural brain development both spatially and temporally to capture the natural variation existing in a healthy population (N=447) over an age range of active brain maturation (18-34weeks). On a routine clinical dataset (N=187) our age prediction results strongly correlate with true GA (r=0.98,accurate within±6.10days), confirming the link between maturational progression and neurosonographic activity observable across gestation. Our model also outperforms current clinical methods by ±4.57 days in the third trimester-a period complicated by biological variations in the fetal population. Through feature selection, the model successfully identified the most age-discriminating anatomies over this age range as being the Sylvian fissure, cingulate, and callosal sulci." @default.
• W2034345055 created "2016-06-24" @default.
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• W2034345055 date "2015-04-01" @default.
• W2034345055 modified "2023-10-18" @default.
• W2034345055 title "Learning-based prediction of gestational age from ultrasound images of the fetal brain" @default.
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• W2034345055 doi "https://doi.org/10.1016/j.media.2014.12.006" @default.
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