Dentists could fail to notice periapical lesions (PLs) while examining panoramic radiographs. Accordingly, this study aimed to develop an artificial intelligence (AI) designed to address this problem. Materials and methods: a total of 18618 periapical root areas (PRA) on 713 panoramic radiographs were annotated and classified as having or not having PLs. An AI model consisting of two convolutional neural networks (CNNs), a detector and a classifier, was trained on the images. The detector localized PRAs using a bounding-box-based object detection model, while the classifier classified the extracted PRAs as PL or not-PL using a fine-tuned CNN. The classifier was trained and validated on a balanced subset of the original dataset that included 3249 PRAs, and tested on 707 PRAs. Results: the detector achieved an average precision of 74.95%, while the classifier accuracy, sensitivity and specificity were 84%, 81% and 86%, respectively. When integrating both detection and classification models, the proposed method accuracy, sensitivity, and specificity were 84.6%, 72.2%, and 85.6%, respectively. Conclusion: a two-stage CNN model consisting of a detector and a classifier can successfully detect periapical lesions on panoramic radiographs.

We aimed to evaluate the impact of Virtual Reality Dental Simulators (VRDS) on preclinical training in endodontics for undergraduate dental students. Purposive sampling technique was used to target undergraduate dental students at two dental schools: in Qatar and Turkey. After training on endodontic access cavity preparation on upper anterior teeth using acrylic teeth on dental mannequins and virtual reality haptic dental simulator, a questionnaire based on a combination of open- and closed-ended items was distributed to the participants. The sample included 60 dental undergraduate students. The participants reported positive experiences with VRDS and 76% considered it to be helpful in improving their fine motor skills. Endodontic access cavity preparation on VRDS was perceived to be similar to natural and acrylic teeth by 73.34% and 53% of participants, respectively. Overall, 85% of participants supported the use of VRDS training to supplement conventional training on dental mannequins but also recommended the need for improvements in VRDS training in endodontics. The findings of this study underscore the benefits of VRDS in endodontics. Future research involving larger samples from multiple institutions may help to optimize VRDS in undergraduate dental education.

Oral cancer has a high mortality rate primarily due to delayed diagnoses, highlighting the need for early detection of oral lesions. This study presents a novel deep learning framework for multi-class classification-based segmentation, enabling accurate differential diagnosis of 14 common oral lesions—benign, pre-malignant, and malignant—across various mouth locations using photographic images. A dataset of 2,072 clinical images was used to train and validate the model. The proposed framework integrates EfficientNet-B3 for classification and ResNet-101-based Mask R-CNN for segmentation, achieving a classification accuracy of 74.49% and segmentation performance with an average precision (AP50) of 72.18. The gradient-weighted class activation map technique was applied to the model outputs to enable visualization of the specific areas that were most influential for predictive decisions made by the model. This significantly improves upon the state-of-the-art, where previous models achieved lower segmentation accuracy (AP50 < 50%). The framework not only classifies the lesion type but also delineates the lesion boundaries with high precision, which is critical for early detection and differential diagnosis in clinical practice.

This is a global study that evaluated the undergraduate endodontic curriculum in dental schools worldwide. An online questionnaire sent to faculty members involved in undergraduate endodontic teaching at 50 dental institutions across six continents. The questionnaire was a modified version of previously validated survey comprised of 46 questions in three sections on essential aspects of the didactic, preclinical (PT), clinical trainings (CT), and assessments in undergraduate endodontic programs. The response rate was 76%. 63.2% of schools have a separate preclinical course, with 65.8% having a dedicated clinical area. Traditional methods dominate, with 84.2% using phantom heads and 78.9% using natural extracted teeth. The student-to-staff ratio for clinical training ranges from 1:6 to 1:9, with most supervisors specializing in endodontics (76.3%). Lectures and practical sessions are the primary teaching methods. Non-surgical root canal treatment (RCT) is the most common procedure (94.7%), and the majority use stainless steel or rotary nickel-titanium files. Cold lateral compaction is the preferred obturation technique globally (81.6%), except in North America. Advanced technologies like magnifying systems and ultrasonic instruments are rarely used in training. The conclusion emphasizes the need for global standardization and adoption of modern techniques to enhance the quality of endodontic education and care.

Oral health self-efficacy is a fundamental determinant of behavioral changes among elderly patients. Objective: To assess the oral self-efficacy among the Saudi population aged 65 years old and above in Riyadh, Saudi Arabia. Methodology: This was a cross-sectional survey conducted on elderly individuals in Riyadh. An Arabic version of the Geriatric Self-Efficacy Scale for Oral Health (GSEOH) was administered to all participants. The dependent variables included oral function, oral hygiene habits, and dental visits. For the statistical analysis, two independent sample t-tests and a one-way ANOVA test were used. Significance was judged at a p-value less than 0.05. Results: Of 400 participants recruited, 53% were males. About 58% had retained teeth, and 72% had visited a dentist in the past 12 months. Overall, 31.6%, 34.64%, 22.65%, and 11.14% of the participants rated their oral health as good, fairly good, rather poor, and poor, respectively. Age (p < 0.001), educational level (p < 0.001), and working status (p < 0.001) were significantly associated with GSEOH scores. Other sociodemographic characteristics were not found to affect the GSEOH scores. Conclusions: The overall self-efficacy of oral health among Saudi elderly individuals is fairly good. Age, educational level, and occupational status are the main determinants of oral health self-efficacy scores.

Efforts to improve the performance of rotary NiTi instruments by enhancing the properties of NiTi alloy, or their manufacturing processes rather than changes in instrument geometries have been reported. The aim of this study was to compare in-vitro the shaping ability of three different rotary nickel-titanium instruments produced by different manufacturing methods.