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Carayon, Delphine; Adhikari, Kaustubh; Monsarrat, Paul; Dumoncel, Jean; Braga, José; Duployer, Benjamin; Delgado, Miguel; Fuentes-Guajardo, Macarena; de Beer, Frikkie; Hoffman, Jakobus W.; Oettlé, Anna C.; Donat, Richard; Pan, Lei; Ruiz-Linares, Andres; Tenailleau, Christophe; Vaysse, Frédéric; Esclassan, Rémi and Zanolli, Clément
(2019).
DOI: https://doi.org/10.1002/ajpa.23709
Abstract
Objectives: The scoring and analysis of dental nonmetric traits are predominantly accomplished by using the Arizona State University Dental Anthropology System (ASUDAS), a standard protocol based on strict definitions and three‐dimensional dental plaques. However, visual scoring, even when controlled by strict definitions of features, visual reference, and the experience of the observer, includes an unavoidable part of subjectivity. In this methodological contribution, we propose a new quantitative geometric morphometric approach to quickly and efficiently assess the variation of shoveling in modern human maxillary central incisors (UI1).
Materials and methods: We analyzed 87 modern human UI1s by means of virtual imaging and the ASU‐UI1 dental plaque grades using geometric morphometrics by placing semilandmarks on the labial crown aspect. The modern human sample was composed of individuals from Europe, Africa, and Asia and included representatives of all seven grades defined by the ASUDAS method.
Results: Our results highlighted some limitations in the use of the current UI1 ASUDAS plaque, indicating that it did not necessarily represent an objective gradient of expression of a nonmetric tooth feature. Rating of shoveling tended to be more prone to intra‐ and interobserver bias for the highest grades. In addition, our analyses suggest that the observers were strongly influenced by the depth of the lingual crown aspect when assessing the shoveling.
Discussion: In this context, our results provide a reliable and reproducible framework reinforced by statistical results supporting the fact that open scale numerical measurements can complement the ASUDAS method.