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The face in marfan syndrome: A 3D quantitative approach for a better definition of dysmorphic features.

Claudia DolciValentina PucciarelliDaniele M GibelliMarina CodariSusan MarelliGiuliana TrifiròAlessandro PiniChiarella Sforza
Published in: Clinical anatomy (New York, N.Y.) (2017)
Marfan syndrome (MFS) is a rare hereditable disorder of connective tissue caused by mutations in the fibrillin-1 gene FBN1. Timely diagnosis of MFS is essential to prevent life-threatening cardiovascular complications; nevertheless it can be difficult owing to the phenotypic variability of the syndrome. No clear quantitative definition of facial abnormalities associated with MFS is available. The aim of this study was to improve the definition of the facial phenotype associated with MFS and to verify the usefulness of a 3D noninvasive quantitative approach for its early recognition. 3D facial images of 61 Italian subjects with MFS, aged 16-64 years (21 males, 38 ± 15 years; 40 females, 41 ± 13 years) were obtained by stereophotogrammetry. From the coordinates of 17 soft-tissue facial landmarks, linear distances and angles were computed; z score values were calculated to compare patients with healthy reference subjects (400 males, 379 females) matched for sex and age. Student's t test was used for statistical comparisons. All subjects with MFS showed greater facial divergence (P < 0.001; mean z score +1.9) and a lower facial height index (P < 0.001; mean z score -1.9) than reference subjects, both values being influenced by a shorter mandibular ramus (P < 0.001; mean z score -1.9) and a mild but significant increase in facial height (P < 0.001; mean z score +1.2). Palpebral down-slanting was found in 85% of MFS subjects. There were no sex differences. Quantitative abnormalities identified in this study enrich information about the facial dysmorphism in MFS and confirm its usefulness for early recognition of the disease. Clin. Anat. 31:380-386, 2018. © 2017 Wiley Periodicals, Inc.
Keyphrases
  • soft tissue
  • high resolution
  • body mass index
  • mass spectrometry
  • deep learning
  • physical activity
  • social media
  • convolutional neural network