title: Predicting fetal neurodevelopmental age from ultrasound images.
abstract: We propose an automated framework for predicting age and neurodevelopmental maturation of a fetus based on 3D ultrasound (US) brain image appearance. A topology-preserving manifold representation of the fetal skull enabled design of bespoke scale-invariant image features. Our regression forest model used these features to learn a mapping from age-related sonographic image patterns to fetal age and development. The Sylvian Fissure was identified as a critical region for accurate age estimation, and restricting the search space to this anatomy improved prediction accuracy on a set of 130 healthy fetuses (error ± 3.8 days; r = 0.98 performing the best current clinical method. Our framework remained robust when applied to a routine clinical population.
authors.forename: Ana I L
authors.initials: AI
authors.lastname: Namburete
authors.forename: Mohammad
authors.initials: M
authors.lastname: Yaqub
authors.forename: Bryn
authors.initials: B
authors.lastname: Kemp
authors.forename: Aris T
authors.initials: AT
authors.lastname: Papageorghiou
authors.forename: J Alison
authors.initials: JA
authors.lastname: Noble
mesh_terms: D000375:Aging / Q000502:physiology*; D000465:Algorithms; D001921:Brain / Q000254:growth & development*; D004453:Echoencephalography / Q000379:methods*; D005260:Female; D005865:Gestational Age; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008297:Male; D064847:Multimodal Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016216:Ultrasonography, Prenatal / Q000379:methods*