Background: Temporomandibular disorders (TMDs) represent a prevalent multifactorial condition that impacts a significant portion of the global population. The objective of this study was to employ Fonseca’s questionnaire for an initial assessment of TMDs. Methods: A cross-sectional study was conducted on a sample of 250 undergraduates from the Dental School of the University of Perugia, Italy. The chi-square test, with a significance level set at p < 0.05, was used to evaluate a statistically significant relationship between TMDs and several variables such as gender, age, employed/unemployed, and physically active or not. Results: The data obtained through the questionnaire indicated that a considerable percentage of students (78%) exhibited signs consistent with TMDs. The most frequently reported signs and symptoms included psychological stress (49.6%), dental clenching and grinding (34%), joint clicking (33.6%), frequent headaches (15.2%), and neck pain (23.2%). Notably, when considering moderate to severe symptoms of TMDs, females were more significantly affected than males. Furthermore, factors such as age, employment status, and physical activity did not appear to influence the prevalence of TMDs. Conclusions: The high prevalence of TMDs identified within this young population (university students), as measured by this questionnaire (albeit warranting validation through more rigorous methodologies) underscores the necessity for the implementation of new preventive strategies that specifically address this demographic.

Background: The surgery-first approach (SFA) in orthognathic surgery eliminates the need for pre-surgical orthodontic treatment, significantly reducing overall treatment time. However, reliance on a compromised occlusion introduces risks of condylar displacement and remodeling. This study employs artificial intelligence (AI) and deep learning to analyze condylar behavior, comparing the outcomes of SFA to the traditional surgery-late approach (SLA). Methods: A retrospective analysis was conducted on 77 patients (18 SFA and 59 SLA) treated at Perugia Hospital between 2016 and 2022. Preoperative (T0) and 12-month postoperative (T1) cone-beam computed tomography (CBCT) scans were analyzed using the 3D Slicer software and its Dental Segmentator extension, powered by a convolutional neural network (CNN). This automated approach reduced segmentation time from 7 h to 5 min. Pre- and postoperative 3D models were compared to assess linear and rotational deviations in condylar morphology, stratified via dentoskeletal classification and surgical techniques. Results: Both the SFA and SLA achieved high surgical accuracy (<2 mm linear deviation and <2° rotational deviation). The SFA and SLA exhibited similar rates of condylar surface remodeling, with minor differences in resorption and formation across dentoskeletal classifications. Mean surface changes were 0.41 mm (SFA) and 0.36 mm (SLA, p < 0.05). Conclusions: Deep learning enables rapid, precise CBCT analysis and shows promise for the early detection of condylar changes. The SFA does not increase adverse effects on condylar morphology compared to SLA, supporting its safety and efficacy when integrated with AI technologies.