Comparison of data mining algorithms for sex determination based on mastoid process measurements using cone-beam computed tomography

Purpose@#The mastoid region is ideal for studying sexual dimorphism due to its anatomical position at the base of the skull. This study aimed to determine sex in the Iranian population based on measurements of the mastoid process using different data mining algorithms. @*Materials and Methods@#This retrospective study was conducted on 190 3-dimensional cone-beam computed tomographic (CBCT) images of 105 women and 85 men between the ages of 18 and 70 years. On each CBCT scan, the following 9 landmarks were measured: the distance between the porion and the mastoidale; the mastoid length, height, and width; the distance between the mastoidale and the mastoid incision; the intermastoid distance (IMD); the distance between the lowest point of the mastoid triangle and the most prominent convex surface of the mastoid (MF); the distance between the most prominent convex mastoid point (IMSLD); and the intersecting angle drawn from the most prominent right and left mastoid point (MMCA). Several predictive models were constructed and their accuracy was compared using cross-validation. @*Results@#The results of the t-test revealed a statistically significant difference between the sexes in all variables except MF and MMCA. The random forest model, with an accuracy of 97.0%, had the best performance in predicting sex. The IMSLD and IMD made the largest contributions to predicting sex, while the MMCA variable had the least significant role. @*Conclusion@#These results show the possibility of developing an accurate tool using data mining algorithms for sex determination in the forensic framework.

Dental age estimation using the pulp-to-tooth ratio in canines by neural networks

PURPOSE: It has been proposed that using new prediction methods, such as neural networks based on dental data, could improve age estimation. This study aimed to assess the possibility of exploiting neural networks for estimating age by means of the pulp-to-tooth ratio in canines as a non-destructive, non-expensive, and accurate method. In addition, the predictive performance of neural networks was compared with that of a linear regression model. MATERIALS AND METHODS: Three hundred subjects whose age ranged from 14 to 60 years and were well distributed among various age groups were included in the study. Two statistical software programs, SPSS 21 (IBM Corp., Armonk, NY, USA) and R, were used for statistical analyses. RESULTS: The results indicated that the neural network model generally performed better than the regression model for estimation of age with pulp-to-tooth ratio data. The prediction errors of the developed neural network model were acceptable, with a root mean square error (RMSE) of 4.40 years and a mean absolute error (MAE) of 4.12 years for the unseen dataset. The prediction errors of the regression model were higher than those of the neural network, with an RMSE of 10.26 years and a MAE of 8.17 years for the test dataset. CONCLUSION: The neural network method showed relatively acceptable performance, with an MAE of 4.12 years. The application of neural networks creates new opportunities to obtain more accurate estimations of age in forensic research.

Comparison of conventional and digital radiography in evaluation of the bone lesions healing process in dog jaws

Jaw bone defects are the origin of many complications for patients, and completebone healing has a special importance. Histological examination can be regarded as the [gold standard]. However, noninvasive diagnosis techniques are needed for clinical application. The aim of this study was to compare digital and conventional radiography for detection of bone healing. In this study, premolar teeth of 4 dogs were extracted. 5.5mm bone defect was created in each quadrant of the jaw bone Three months after healing of the extraction sites. Digital and conventional radiography was performed at 15, 30, 45 and 60 days after surgery. At every stage, bone specimens were removed and studied with histomorphometric procedures. At all stages, bone defect density was determined by conventional and digital radiography and compared with the percentage of bone formation determined by histomorphometry. The data were analyzed using descriptive analysis variance test for repeated data and paired t-test using SPSS software. At all stages, the differences between both radiographic methods and histomorphometry were statistically significant. At days 15 and 60, a statistically significant difference was seen between digital and conventional radiography. The differences between digital and conventional radiography at days 30 and 45 were not statistically significant. Digital radiography showed changes more clearly in the early stages of bone formation [day 15] and at later stages [day 60]