Objective: This study aimed to present and evaluate a new deep learning model for determining cervical vertebral maturation (CVM) degree and growth spurts by analyzing lateral cephalometric radiographs. Methods: The study sample included 890 cephalograms. The images were classified into six cervical stages independently by two orthodontists. The images were also categorized into three degrees on the basis of the growth spurt: pre-pubertal, growth spurt, and post-pubertal. Subsequently, the samples were fed to a transfer learning model implemented using the Python programming language and PyTorch library. In the last step, the test set of cephalograms was randomly coded and provided to two new orthodontists in order to compare their diagnosis to the artificial intelligence (AI) model’s performance using weighted kappa and Cohen’s kappa statistical analyses. Results: The model’s validation and test accuracy for the six-class CVM diagnosis were 62.63% and 61.62%, respectively. Moreover, the model’s validation and test accuracy for the three-class classification were 75.76% and 82.83%, respectively. Furthermore, substantial agreements were observed between the two orthodontists as well as one of them and the AI model. Conclusions The newly developed AI model had reasonable accuracy in detecting the CVM stage and high reliability in detecting the pubertal stage. However, its accuracy was still less than that of human observers. With further improvements in data quality, this model should be able to provide practical assistance to practicing dentists in the future.

OBJECTIVEAmong the injury types, sports ones constitute a considerable proportion of patients who refer to the medical centers. This research was conducted to examine the frequency of sports-related injuries referring to Kashan University of Medical Sciences Trauma Research Center from 2005 to 2011.

METHODSThis was a retrospective research in which existing data from the data bank of Kashan University of Medical Sciences Trauma Research Center were employed. The data were extracted from the main source by SPSS version 16.0. Variables such as age, education, occupation and gender were analyzed.

RESULTSThe highest proportion of injuries was observed in students (59.4%) followed by workers (11.8%). Upper and lower extremities were most commonly injured. The most frequent injury was strain (35.4%), followed by sprain (27.7%).

CONCLUSIONThe results of this research showed that the majority of the sports trauma occurrs in students; therefore, they need more attention in regard to sports injuries. Preventive measures such as informing the coaches and teachers as well as increasing the students'awareness about the injury risk can decrease the incidences of sports injuries.

Athletic Injuries ; epidemiology ; Humans ; Iran ; epidemiology ; Retrospective Studies ; Students