Abstract

Introduction: Antenatal detection of facial dysmorphism using 3D ultrasound may raise suspicion of underlying genetic conditions, but infrequently leads to definitive antenatal diagnosis. Despite advances in array and non-invasive prenatal testing, not all genetic conditions can be ascertained from such testing. The aim of this study was to investigate the feasibility of quantitative assessment of fetal face features using prenatal 3D ultrasound volumes and statistical shape modelling, to objectively distinguish dysmorphic from normal cases. Methods: 13 normal and 7 abnormal stored 3D ultrasound (Voluson S8-E8, GE Healthcare) fetal face volumes were analysed, at a median gestation of 29 + 4 weeks [25 + 0 – 36 + 1]. The 20 volumes were post-processed to generate 3D surface meshes, subsequently aligned. The surfaces served as input for statistical shape modelling (Deformetrica) to compute the population mean 3D face shape. Principal component analysis (PCA), a mathematical technique used to condense a large amount of 3D data into a smaller number of relevant variables (shape modes), was used to study the 3D shape variations in the population. Results: 10 PCA modes explained >90% of the total shape variability in the population. While the first mode accounted for overall size differences, the second highlighted shape feature changes from an overall proportionate towards a more asymmetric face shape, with wide prominent forehead and undersized, posteriorly positioned chin. Analysis of the subject specific Mahalanobis distance in PCA space suggested differences between normal and abnormal fetuses (median and interquartile range distance values 7.3±5.6 for the normal group vs. 13.3±9.8 for the abnormal group, p=.056). Conclusion: In conclusion, this feasibility study demonstrates that objective characterisation and quantification of fetal facial morphology is possible from 3D ultrasound. This technique has the potential to assist in-utero diagnosis, particularly of rare conditions where facial dysmorphology is a feature.

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