Canines are usually used in anthropological and forensic sciences for sex and age determination. The best methods to estimate age are based on secondary dentine apposition, evaluated from periapical X-rays. The aim of this study was to propose a new method of sex and age estimation using 3D models to obtain more precise predictions using tooth volumes. Fifty-eight dental CT scans of patients aged 14-74 with a well-balanced sex ratio composed the sample. One hundred and thirty-three healthy canines were modeled (Mimics 12.0). The sample was divided into a training sample and a validation sample. An age formula was determined using the "pulp volume/tooth volume" ratio. Sex prediction was adjusted with total volumes. Applying the equations to the validation sample, no significant difference was found between the real and predicted ages, and 100% of the sex predictions were correct. This preliminary study gives interesting results, and this method is worth being tested on a larger data sample.

Accurate age determination is fundamental in both forensic medicine and anthropology. Many methods that relate dental characteristics to adult age have been proposed, but there is still no simple and reliable method that does not damage the study material. The aim of this work was to propose a relevant and practical technique for determining age in adults that could be used in both living and deceased individuals. The sample was composed of 210 CT scans from individuals aged from 15 to 85 years old, with four healthy canines present in the mouth. The 840 canines were modelled using Mimics® 10.01 software. The pulp volume/total volume ratio ×100 was determined for each tooth. Seven mathematical models, corresponding to all possible real situations, were determined by the weighted least squares method and ranked in order of relative performance. The adequacy of the seven models to the data was very high with the regressions proposed (0.915 < R (2) adjusted < 0.964). Ranked in order of performance, the maxillary model was the most powerful of the seven models for age determination, followed by the 4 canines model, the 13 model and the 23 model.

Gender determination is a fundamental issue in forensic anthropology. Many techniques based on bone and dental remains have been proposed. It is not always possible to implement the techniques using bones, but teeth are often perfectly preserved. It has been demonstrated that the canine has the greatest sexual dimorphism, and the aim of this work was to provide an easy and accurate dental technique for determining the gender in the absence of other skeletal elements. The sample was composed of 210 CT scans with four healthy canines. The 840 canines were modeled using MIMICS® 10.01 software. The total volume of each tooth was determined. Seven mathematical models were determined by binary logistic regressions and ranked in order of relative performance. The seven proposed predictive models thus performed (0.910≤AUC≤0.938), with overall rates of correct predictions between 82.38 and 85.24%. The 4-canine model is the most powerful for predicting the gender.

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The aim of this study was to establish a prediction formula to allow for the determination of sex among the southeastern French population using dental measurements. The sample consisted of 105 individuals (57 males and 48 females, aged between 18 and 25 years). Dental measurements were calculated using Euclidean distances, in three-dimensional space, from point coordinates obtained by a Microscribe. A multiple logistic regression analysis was performed to establish the prediction formula. Among 12 selected dental distances, a stepwise logistic regression analysis highlighted the two most significant discriminate predictors of sex: one located at the mandible and the other at the maxilla. A cutpoint was proposed to prediction of true sex. The prediction formula was then tested on a validation sample (20 males and 34 females, aged between 18 and 62 years and with a history of orthodontics or restorative care) to evaluate the accuracy of the method.

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