title: Learning-based prediction of gestational age from ultrasound images of the fetal brain.
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.
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom. Electronic address: ana.namburete@eng.ox.ac.uk.
authors.forename: Ana I L
authors.initials: AI
authors.lastname: Namburete
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
authors.forename: Richard V
authors.initials: RV
authors.lastname: Stebbing
authors.affiliation: Nuffield Department of Obstetrics and Gynaecology, University of Oxford, Oxford, United Kingdom.
authors.forename: Bryn
authors.initials: B
authors.lastname: Kemp
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
authors.forename: Mohammad
authors.initials: M
authors.lastname: Yaqub
authors.affiliation: Nuffield Department of Obstetrics and Gynaecology, University of Oxford, Oxford, United Kingdom.
authors.forename: Aris T
authors.initials: AT
authors.lastname: Papageorghiou
authors.affiliation: Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom.
authors.forename: J
authors.initials: J
authors.lastname: Alison Noble
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000196:embryology*; D018568:Crown-Rump Length; D004453:Echoencephalography / Q000379:methods*; D005260:Female; D005865:Gestational Age; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016216:Ultrasonography, Prenatal / Q000379:methods*
keywords: Brain development; Fetal brain ultrasound; Gestational age; Regression forest; Surface parametrization