Literature DB >> 28209493

Quantitative analysis of fetal facial morphology using 3D ultrasound and statistical shape modeling: a feasibility study.

Andrea Dall'Asta1, Silvia Schievano2, Jan L Bruse2, Gowrishankar Paramasivam3, Christine Tita Kaihura4, David Dunaway5, Christoph C Lees6.   

Abstract

BACKGROUND: The antenatal detection of facial dysmorphism using 3-dimensional ultrasound may raise the suspicion of an underlying genetic condition but infrequently leads to a definitive antenatal diagnosis. Despite advances in array and noninvasive prenatal testing, not all genetic conditions can be ascertained from such testing.
OBJECTIVES: The aim of this study was to investigate the feasibility of quantitative assessment of fetal face features using prenatal 3-dimensional ultrasound volumes and statistical shape modeling. STUDY 
DESIGN: Thirteen normal and 7 abnormal stored 3-dimensional ultrasound fetal face volumes were analyzed, at a median gestation of 29+4 weeks (25+0 to 36+1). The 20 3-dimensional surface meshes generated were aligned and served as input for a statistical shape model, which computed the mean 3-dimensional face shape and 3-dimensional shape variations using principal component analysis.
RESULTS: Ten shape modes explained more than 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 toward a more asymmetric face shape with a wide prominent forehead and an undersized, posteriorly positioned chin. Analysis of the Mahalanobis distance in principal component analysis shape space suggested differences between normal and abnormal fetuses (median and interquartile range distance values, 7.31 ± 5.54 for the normal group vs 13.27 ± 9.82 for the abnormal group) (P = .056).
CONCLUSION: This feasibility study demonstrates that objective characterization and quantification of fetal facial morphology is possible from 3-dimensional ultrasound. This technique has the potential to assist in utero diagnosis, particularly of rare conditions in which facial dysmorphology is a feature.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  3-dimensional ultrasound; facial dysmorphism; genetic syndrome; prenatal diagnosis; principal component analysis; statistical shape modeling

Mesh:

Year:  2017        PMID: 28209493     DOI: 10.1016/j.ajog.2017.02.007

Source DB:  PubMed          Journal:  Am J Obstet Gynecol        ISSN: 0002-9378            Impact factor:   8.661


  1 in total

1.  Convolutional mesh autoencoders for the 3-dimensional identification of FGFR-related craniosynostosis.

Authors:  Eimear O' Sullivan; Lara S van de Lande; Stefanos Zafeiriou; David J Dunaway; Athanasios Papaioannou; Richard W F Breakey; N Owase Jeelani; Allan Ponniah; Christian Duncan; Silvia Schievano; Roman H Khonsari
Journal:  Sci Rep       Date:  2022-02-09       Impact factor: 4.996

  1 in total

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