Oral cancer (OC) is one of the most common forms of head and neck cancer and continues to have the lowest survival rates worldwide, even with advancements in research and therapy. The prognosis of OC has not significantly improved in recent years, presenting a persistent challenge in the biomedical field. In the field of oncology, artificial intelligence (AI) has seen rapid development, with notable successes being reported in recent times. This systematic review aimed to critically appraise the available evidence regarding the utilization of AI in the diagnosis, classification, and prediction of oral cancer (OC) using histopathological images. An electronic search of several databases, including PubMed, Scopus, Embase, the Cochrane Library, Web of Science, Google Scholar, and the Saudi Digital Library, was conducted for articles published between January 2000 and January 2023. Nineteen articles that met the inclusion criteria were then subjected to critical analysis utilizing QUADAS-2, and the certainty of the evidence was assessed using the GRADE approach. AI models have been widely applied in diagnosing oral cancer, differentiating normal and malignant regions, predicting the survival of OC patients, and grading OC. The AI models used in these studies displayed an accuracy in a range from 89.47% to 100%, sensitivity from 97.76% to 99.26%, and specificity ranging from 92% to 99.42%. The models’ abilities to diagnose, classify, and predict the occurrence of OC outperform existing clinical approaches. This demonstrates the potential for AI to deliver a superior level of precision and accuracy, helping pathologists significantly improve their diagnostic outcomes and reduce the probability of errors. Considering these advantages, regulatory bodies and policymakers should expedite the process of approval and marketing of these products for application in clinical scenarios.

The technological advancements in the field of medical science have led to an escalation in the development of artificial intelligence (AI) applications, which are being extensively used in health sciences. This scoping review aims to outline the application and performance of artificial intelligence models used for diagnosing, treatment planning and predicting the prognosis of orthognathic surgery (OGS). Data for this paper was searched through renowned electronic databases such as PubMed, Google Scholar, Scopus, Web of science, Embase and Cochrane for articles related to the research topic that have been published between January 2000 and February 2022. Eighteen articles that met the eligibility criteria were critically analyzed based on QUADAS-2 guidelines and the certainty of evidence of the included studies was assessed using the GRADE approach. AI has been applied for predicting the post-operative facial profiles and facial symmetry, deciding on the need for OGS, predicting perioperative blood loss, planning OGS, segmentation of maxillofacial structures for OGS, and differential diagnosis of OGS. AI models have proven to be efficient and have outperformed the conventional methods. These models are reported to be reliable and reproducible, hence they can be very useful for less experienced practitioners in clinical decision making and in achieving better clinical outcomes.

Objective: Student self-assessment is a skill that is taught, assessed, and attained in education for lifelong learning. In dental education, developing self-assessment is essential during the early stages of psychomotor learning. This study compared dental students’ self-assessments and instructors’ assessments in competency examinations and evaluated the progress and potential predictors of self-assessment performance, including achievement level and gender, in a Preclinical Operative Dentistry course. Methods: The preclinical operative dentistry assessment and self-assessment records by instructors and students, respectively, were collected retrospectively from intra-semester and final examination assessment rubrics. The instructors’ scoring agreement level was measured using inter-rater reliability and intra-class correlations (ICCs). Students’ self-assessment data were compared with instructors’ assessment data, and agreement was tested using inter-rater reliability estimates. The significance level was set at 0.05. Results: Instructors were calibrated based on significant agreement (p < 0.001) between full-time and part-time instructors (ICCs = 0.888 and 0.859, respectively). A significant difference (p < 0.001) was found between instructors’ and students’ mean scores. Students’ gender was significantly associated with their evaluation of performance in the intra-semester examination (p = 0.048). Most male students (58%) overestimated their performance, while the majority (44%) of female students tend to evaluate their performance accurately. There was no significant difference in the scoring of instructors and high achievers classified in the “excellent” category (p = 0.392). Conclusion: In preclinical operative dentistry education, students’ ability to accurately self-assess the quality of their work varies, and may be predicted by gender and achievement level. Students who underestimate their performance in self-assessment tend not to show improvement in their actual performance.

This study’s aim is to determine the accuracy and precision of the bracket slot height in MBT 0.022″ ceramic brackets. Five brackets from 11 different systems (n = 55) were scanned using micro-computed tomography (micro-CT). The slot height was measured at the face (external) and base (internal) of the slot. Data were analyzed using a One-Sample t-Test, and a Paired t-Test. The lowest external height was seen in OrthoCare Purity^® at 0.02264″ (2.9%), and the greatest in TOC Ghost Advanced^® at 0.02736″ (24.4%). The lowest internal height was seen in Forestadent Glam^® at 0.02020″ (−8.2%), and the greatest in TOC Ghost Advanced^® at 0.2547″ (15.8%). OrthoCare Purity^® measurements corresponded most closely with the expected measurements. TP InVu^® was found to be the most precise bracket for external height (range = 0.00043″) and American Orthodontics 20/40^® for internal height (range = 0.00028″). In assessing slot geometry, all brackets demonstrated a higher mean external slot height compared to the internal measurements at the base of the bracket. Orthodontic bracket slots are larger than expected and slot parallelism was not observed in any bracket brand tested. Similarly, slot dimensions are imprecise where two ‘identical’ brackets have different slot sizes. The clinician should, therefore, assume that play is most likely higher than expected.

Background/Objectives: Dental professionals engage in a variety of dental procedures within a confined workspace that is often challenging to access and navigate. This environment frequently results in static, asymmetrical, and inappropriate postures, which can lead to muscular imbalances and cause pain or damage to the musculoskeletal system. Such issues can adversely affect the dental workforce, resulting in increased absenteeism, reduced productivity, disability, and premature retirement from the profession. Therefore, the objective of this study was to develop and evaluate the performance of an Artificial Intelligence (AI)-based deep learning model designed to assess dental ergonomics. Methods: An AI-based Dental Ergonomic Posture Assessment Model [SBK-DentErgo] was developed through the strategic integration of YOLOv11 and MediaPipe. Model training and validation were conducted using 500 photographs of dental professionals performing procedures on patients, captured from both frontal and sagittal planes. In the initial phase of the study, two calibrated evaluators assessed 50 photographs, demonstrating excellent agreement. In the subsequent phase, five dental specialists, along with the AI model, evaluated the same set of photographs, and the results were recorded. Results: AI-based model demonstrated excellent agreement with that of calibrated evaluators (Kappa = 0.922, p = 0.000). The reliability of AI-based scores was also consistent (ICC = 1.000, p = 0.000). Human evaluation of ergonomic posture exhibited very low sensitivity (20.5%) compared to AI, which showed very high sensitivity (97%). The specificity of human evaluation was also extremely low (9.1%) in contrast to AI (85.7%). The AI model (AUC = 0.917, 95% CI 0.762–1.000) could serve as the ‘gold standard’ in evaluating dental operator ergonomics. Conclusions: This AI model demonstrated exceptional performance in evaluating the working postures of dental professionals, surpassing experienced specialists in both sensitivity and specificity. The model provides real-time feedback, enabling dentists to conduct self-assessments and correct their posture immediately, thereby preventing postural issues.