Using three-dimensional geometric morphometry for facial analysis in patients with the oculo-auriculo-vertebral spectrum.

AIM: To utilize three-dimensional (3D) geometric morphometry for visualization of the level of facial asymmetry in patients with the oculo-auriculo-vertebral spectrum (OAVS). MATERIALS AND METHODS: Three-dimensional facial scans of 25 Czech patients with OAVS were processed. The patients were divided into subgroups according to Pruzansky classification. For 13 of them, second 3D facial scans were obtained. The 3D facial scans were processed using geometric morphometry. Soft tissue facial asymmetry in the sagittal plane and its changes in two time spots were visualized using colour-coded maps with a thermometre-like scale. RESULTS: Individual facial asymmetry was visualized in all patients as well as the mean facial asymmetry for every Pruzansky subgroup. The mean colour-coded maps of type I and type IIA subgroups showed no differences in facial asymmetry, more pronounced asymmetry in the middle and the lower facial third was found between type IIA and type IIB (maximum 1.5 mm) and between type IIB and type III (maximum 2 mm). The degree of intensity facial asymmetry in affected middle and lower facial thirds did not change distinctly during the two time spots in all subgroups. CONCLUSIONS: The 3D geometric morphometry in OAVS patients could be a useful tool for objective facial asymmetry assessment in patients with OAVS. The calculated colour-coded maps are illustrative and useful for clinical evaluation.

Body mass index is an overlooked confounding factor in existing clustering studies of 3D facial scans of children with autism spectrum disorder.

Cluster analyzes of facial models of autistic patients aim to clarify whether it is possible to diagnose autism on the basis of facial features and further to stratify the autism spectrum disorder. We performed a cluster analysis of sets of 3D scans of ASD patients (116) and controls (157) using Euclidean and geodesic distances in order to recapitulate the published results on the Czech population. In the presented work, we show that the major factor determining the clustering structure and consequently also the correlation of resulting clusters with autism severity degree is body mass index corrected for age (BMIFA). After removing the BMIFA effect from the data in two independent ways, both the cluster structure and autism severity correlations disappeared. Despite the fact that the influence of body mass index (BMI) on facial dimensions was studied many times, this is the first time to our knowledge when BMI was incorporated into the faces clustering study and it thereby casts doubt on previous results. We also performed correlation analysis which showed that the only correction used in the existing clustering studies-dividing the facial distance by the average value within the face-is not eliminating correlation between facial distances and BMIFA within the facial cohort.