Artificial Intelligence as a Decision-Making Tool in Forensic Dentistry: A Pilot Study with I3M.

Expert determination of the third molar maturity index (I3M) constitutes one of the most common approaches for dental age estimation. This work aimed to investigate the technical feasibility of creating a decision-making tool based on I3M to support expert decision-making. Methods: The dataset consisted of 456 images from France and Uganda. Two deep learning approaches (Mask R-CNN, U-Net) were compared on mandibular radiographs, leading to a two-part instance segmentation (apical and coronal). Then, two topological data analysis approaches were compared on the inferred mask: one with a deep learning component (TDA-DL), one without (TDA). Regarding mask inference, U-Net had a better accuracy (mean intersection over union metric (mIoU)), 91.2% compared to 83.8% for Mask R-CNN. The combination of U-Net with TDA or TDA-DL to compute the I3M score revealed satisfying results in comparison with a dental forensic expert. The mean ± SD absolute error was 0.04 ± 0.03 for TDA, and 0.06 ± 0.04 for TDA-DL. The Pearson correlation coefficient of the I3M scores between the expert and a U-Net model was 0.93 when combined with TDA and 0.89 with TDA-DL. This pilot study illustrates the potential feasibility to automate an I3M solution combining a deep learning and a topological approach, with 95% accuracy in comparison with an expert.

Third molar maturity index for indicating the legal adult age in southeastern France.

The estimation of age in living subjects is today a significant issue because of the increase in the situations in which it arises and its legal consequences. In line with the Study Group on Forensic Age Diagnostics of the German Association of Legal Medicine's recommendations, it is based on the concomitant use of physical examination and methods for estimating bone age and dental age. Given the variations between the populations, the use of the latter requires their validation on all the ethnic groups on which they can be used. This aim of this study was to access the interest of the Cameriere's third molar maturity index (I3M) to indicate if an individual had reached the age of 18 in a sample of individuals from southeastern France. The studied sample consisted of OPTs 339 (184 females and 155 males) subjects aged between 14 and 22 years old. A logistic regression analysis with the adult age and minor age as dichotomous dependent variable and gender and I3M as predictor variables showed that gender was not statistically significant in discriminating adults and minors. The receiver operating curve (ROC) analysis showed the best discrimination performance of the specific cut-off value of I3M<0.08 in discriminating adults from minors. The proportion of accurate classification was 0.897 and 0.916 for females and males, respectively. The specificity (Sp) and sensitivity (Se) results for females were 0.962 and 0.813, respectively. Sp and Se for males were 0.953 and 0.871, respectively. The Bayes post-test probability was 0.961 and 0.955 in females and males respectively. Obtained results showed that a cut-off value of I3M<0.08 may be an additional tool for indicating adult age in the tested population.