PhD Scientific Days 2022

Clinical Medicine I. (Poster discussion will take place in the Aula during the Coffee Break)

The role of palatal geometry in sex discrimination and human identification

Text of the abstract

Introduction: 3D superimposition of the complete palatal model (including the geometry and surface morphology) can distinguish identical twins, indicating that the digital model might be able to aid identification.
Aim: This study aimed to evaluate the discriminative potential of palatal geometry using digital intraoral scans in serving as contextual evidence for supporting determinations of sex discrimination when no other comparative data is available and source conclusion by assisting identification.
Method: The palates of 64 monozygotic (MZT) and 39 dizygotic (DZT) twins were digitized three times using an intraoral dental scanner. In the first two studies, the mean absolute distance (MAD) between surfaces of two scans acquired from two siblings of the same twin was calculated in the original scans and after removing the palatal rugae from the model by digital smoothing. The palatal height, depth, and width in the third study were measured and loaded into a discriminant function.
Results: The MAD between siblings' scans using original and smoothed scans was not significantly different in either MZT (0.430±0.018 mm versus 0.425±0.022 mm p=0.061) or DZT (0.621±0.058 mm versus 0.586±0.053 mm, p=0.284). By combining the height, depth, and width into a discriminative function, the sex could be determined by 83.9%, identity by 91.2% sensitivity, and twining by 68.5%.
Conclusion: The difference in the 3D palatal model between twin siblings is largely due to palatal geometry. Therefore, geometry can be used as an adjunct metric for limiting the possible matches in a dental 3D database in determining sex and identity if no other evidence is available.
Funding: Supported by „Az orvos-, egészségtudományi- és gyógyszerészképzés
tudományos műhelyeinek fejlesztése”, EFOP-3.6.3-VEKOP-16-2017-00009, Semmelweis 250+ Kiválósági PhD Ösztöndíj and supported by ÚNKP-21-3-II-SE-4.