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.

Orthognathic surgery (OGS) corrects dentofacial deformities and can substantially improve quality of life (QoL). How satisfaction relates to patients' preoperative expectations, surgical magnitude, and demographics remains a subject of debate. Our single-centre retrospective study included consecutive OGS patients with ≥ 6-month follow-up and completed PROMs. Validated instruments comprised the Orthognathic Quality of Life Questionnaire (OQLQ) and selected FACE-Q scales; global satisfaction was rated on a 0-10 VAS. Primary endpoints were postoperative satisfaction and change in QoL. The cohort comprised 290 patients (176 women, 114 men, mean age 23.4 years); 42% underwent single-jaw and 58% bimaxillary surgery. At a mean 12.2-month follow-up, satisfaction was high (VAS 9.1 ± 1.1), with 93.8% reporting that treatment met or exceeded expectations. OQLQ improved from 42.5 ± 17.3 preoperatively to 15.4 ± 12.1 postoperatively (p < 0.0001), with gains across domains; FACE-Q scores were similarly high. Satisfaction did not differ by surgery type (VAS 9.0 ± 1.2 vs 9.2 ± 1.0; p = 0.34), expectation focus (ANOVA p = 0.67), sex (p = 0.42), or age (ρ = -0.05; p = 0.37). OGS yields large QoL gains and very high satisfaction. When treatment plans address the patient's chief concern, satisfaction is independent of surgery magnitude, expectation focus, sex, and age. Structured preoperative counseling, paired with routine PROM monitoring, may help align expectations with achievable outcomes, and support optimal patient-centered results.

Background: Reconstructive surgery with free flaps is a fundamental component in head and neck surgery, but flap failure remains a significant concern. The hemoglobin, albumin, lymphocyte, and platelet (HALP) score is a prognostic factor commonly applied in the evaluation of patients suffering from malignant tumours. This score has emerged as a potential preoperative risk assessment tool; however, its predictive accuracy for free flap failure has not been fully evaluated. The potential of this formula, in the cervical–facial reconstructive area, has not yet been well studied in the international literature. Methods: This retrospective study included patients who had undergone free flap reconstruction in the maxillofacial surgery departments of Perugia and Ancona over the past ten years. To optimize the HALP formula, advanced machine learning technologies, mainly the Julius AI platform, were applied. The HALP formula was refined through three phases: the original formula, a weighted version, and the final “Modified HALP.” Predictive performance was assessed using ROC curve analysis, multivariate logistic regression, and internal validation. Results: The final version of the HALP score, created in the present study, achieved an AUC-ROC of 0.95 (95% CI: 0.91–0.99), outperforming the original and weighted versions. At the optimal cut-off, the final “Modified” HALP score demonstrated 90.91% sensitivity and 92.36% specificity, remaining an independent predictor of flap failure after adjusting for confounders. Conclusions: The aim of the study was to optimize the HALP score by integrating clinical information with the aim of making significant progress in the prediction of failure in reconstructive flaps. The Modified HALP score emerges as a powerful tool for preoperative risk stratification in free flap surgery, potentially improving patient outcomes through targeted interventions. Future research should focus on external validation and exploring the underlying biological mechanisms.