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Although the methods employed produce encouraging results, most rely on the dry bone analyses or complex imaging techniques that, ultimately, hamper sample collection and, as a consequence, the development of large-scale studies. Thus, we proposed an objective, repeatable, and fully automatic approach to provide a quantitative description of the mandible in orthopantomographies (OPGs).<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Methods<\/jats:bold><\/jats:title><jats:p>We proposed the use of a deep convolutional neural network (CNN) to localize a set of landmarks of the mandible contour automatically from OPGs. Furthermore, we detailed four different descriptors for the mandible shape to be used for a variety of purposes. This includes a set of linear distances and angles calculated from eight anatomical landmarks of the mandible, the centroid size, the shape variations from the mean shape, and a group of shape parameters extracted with a point distribution model.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Results<\/jats:bold><\/jats:title><jats:p>The fully automatic digitization of the mandible contour was very accurate, with a mean point to the curve error of 0.21 mm and a standard deviation comparable to that of a trained expert. The combination of the CNN and the four shape descriptors was validated in the well-known problems of forensic sex and age estimation, obtaining 87.8% of accuracy and a mean absolute error of 1.57 years, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title><jats:bold>Conclusion<\/jats:bold><\/jats:title><jats:p>The methodology proposed, including the shape model, can be valuable in any field that requires a quantitative description of the mandible shape and a visual representation of its changes such as clinical practice, surgery management, dental research, or legal medicine.<\/jats:p><\/jats:sec>","DOI":"10.1007\/s11548-021-02474-2","type":"journal-article","created":{"date-parts":[[2021,8,27]],"date-time":"2021-08-27T15:06:56Z","timestamp":1630076816000},"page":"2215-2224","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Automated description of the mandible shape by deep learning"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5865-9973","authenticated-orcid":false,"given":"Nicol\u00e1s","family":"Vila-Blanco","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1333-0810","authenticated-orcid":false,"given":"Paulina","family":"Varas-Quintana","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0262-3821","authenticated-orcid":false,"given":"\u00c1ngela","family":"Aneiros-Ardao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3317-0853","authenticated-orcid":false,"given":"Inmaculada","family":"Tom\u00e1s","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0532-2351","authenticated-orcid":false,"given":"Mar\u00eda J.","family":"Carreira","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,8,27]]},"reference":[{"issue":"6","key":"2474_CR1","doi-asserted-by":"publisher","first-page":"1376","DOI":"10.1111\/j.1556-4029.2006.00270.x","volume":"51","author":"KS Hu","year":"2006","unstructured":"Hu KS, Koh KS, Han SH, Shin KJ, Kim HJ (2006) Sex determination using nonmetric characteristics of the mandible in Koreans. 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