The study of tooth morphology is a critical component of the dental curriculum, highlighting the importance for dental students to acquire comprehensive and detailed knowledge of the complex structure of teeth. This study compared the educational outcomes of two student cohorts in a tooth morphology course, using traditional methods for the control group and additional digital video-based resources for the experimental group. We hypothesized that early integration of digital resources would significantly reduce the learning time. We retrospectively analyzed two groups of Master of Dentistry students. The control group (42 students) was taught using the traditional \'tooth puzzle\' method, while the experimental group (42 students) supplemented traditional teaching with digital video-based tools developed by our department. Both groups\' curricula culminated in a practical post-course test requiring the identification of 40 teeth, along with a mid-course test to track the students\' learning progression. The number and type of incorrectly identified teeth were recorded. The mid-course test showed significant performance differences. The control group had a median (Q1, Q3) value of faults of 12.0 (7.8, 20.5), whereas the respective value for the experimental group was 4.0 (0.0, 8.0) (p < 0.001). In the control group, none achieved faultless results, with only two students (4.8%) having at most two faults, and six students (14.3%) having no more than four faults. The control group averaged 13.5 faults per student, with 19 students (45.2%) failing the test. Conversely, the experimental group showed improved performance: 12 students (28.6%) had no faults, and 25 students (59.5%) had four or fewer faults. The experimental group averaged 5.2 faults per student, with only four students (9.5%) failing. By the end of the course, both groups achieved commendable results on the practical tooth identification test. The experimental group slightly outperformed the control group, though the difference was not significant. The median (Q1, Q3) values were 0.0 (0.0, 2.5) and 1.0 (0.0, 4.5) for the experimental and control groups, respectively (p = 0.372). The students using both traditional and structured digital video-based tools showed greater learning advancement than those using only the traditional \'tooth puzzle\' method.

OBJECTIVE: The aim of this study was to evaluate the accuracy of a combined approach based on an isotopological remeshing and statistical shape analysis (SSA) to capture key anatomical features of altered and intact premolars. Additionally, the study compares the capabilities of four Machine Learning (ML) algorithms in identifying or simulating tooth alterations.
METHODS: 113 premolar surfaces from a multicenter database were analyzed. These surfaces were processed using an isotopological remeshing method, followed by a SSA. Mean Euclidean distances between the initial and remeshed STL files were calculated to assess deviation in anatomical landmark positioning. Seven anatomical features were extracted from each tooth, and their correlations with shape modes and morphological characteristics were explored. Four ML algorithms, validated through three-fold cross-validation, were assessed for their ability to classify tooth types and alterations. Additionally, twenty intact teeth were altered and then reconstructed to verify the method\'s accuracy.
RESULTS: The first five modes encapsulated 76.1% of the total shape variability, with a mean landmark positioning deviation of 10.4 µm (±6.4). Significant correlations were found between shape modes and specific morphological features. The optimal ML algorithms demonstrated high accuracy (>83%) and precision (>86%). Simulations on intact teeth showed discrepancies in anatomical features below 3%.
CONCLUSIONS: The combination of an isotopological remeshing with SSA showed good reliability in capturing key anatomical features of the tooth.
CONCLUSIONS: The encouraging performance of ML algorithms suggests a promising direction for supporting practitioners in diagnosing and planning treatments for patients with altered teeth, ultimately improving preventive care.

BACKGROUND: Understanding the root canal morphology is essential for the success of root canal treatment. Therefore, this study aimed to evaluate and analyze the root canal configuration of maxillary premolars using Cone Beam Computed Tomography in the Pakistani subpopulation.
METHODS: This cross-sectional study utilized CBCT scans from two distinct centres: Aga Khan University in Karachi and Jinnah MRI and Body Scans in Lahore. The CBCT images were visualized using GALAXIS version 1.9 (SICAT GmbH and Co. KG, Bonn, Germany), integrated within the Sirona Dental System (D-64625 Bensheim, Germany). The scanning parameters were standardized at 85 kV, 7 mA, with a 15-s exposure time and a voxel size of 0.16 mm. A total of 707 CBCT scans were collected, encompassing 2180 maxillary premolars. Root canal configurations were classified based on (Ahmed et al. Int Endod J. 2017;50(8):761-70). Statistical analyses were performed using SPSS version 26, employing the Chi-square test with a significance level set at p < 0.05.
RESULTS: The distribution of root canal morphologies varied significantly with age and gender. Among maxillary premolars, 50% exhibited the typical configuration of 2MPMB1 L1 (two roots, single canal in each buccal and lingual root), while 26% of maxillary right second premolars displayed 1MPM1 (one root, one canal). Overall, 1MPM1 accounted for 27.4% of the total cases in the second premolars. There was no statistically significant relationship between age and root canal distribution in either first premolars (p = 0.338) or second premolars (p = 0.833). Regarding gender, a significant difference was observed in the distribution of right maxillary 1st premolars (p = 0.022*), with a higher prevalence among females.
CONCLUSIONS: This study offers significant insights into the anatomical variations of root canals in maxillary premolars across diverse regional subpopulations in Pakistan. While specific root canal configurations were prevalent, the findings indicate no statistically significant correlation between age and root canal morphology in maxillary premolars. However, a notable gender disparity was observed in the distribution of the right maxillary first premolars.

This is a unique case of a single-rooted maxillary premolar with two separate canals in bayonet or S-shaped radicular anatomy undergoing post-endodontic reinforcement prior to crown placement. Bayonet-shaped canals present challenges in canal negotiation, cleaning, shaping, and obturation. The complexity of these canals heightens the risk of accidental file breakage and other iatrogenic errors, posing significant challenges. Post-endodontic restoration in S-shaped canals with double curvature poses challenges in precise placement and material adaptation. A novel technique was implicated to preserve the functionality and structural aesthetics of a decayed maxillary first premolar tooth.

OBJECTIVE: Tooth morphology education is a critical component of dental curricula, providing a foundational understanding of the intricate structural anatomy of teeth. This study evaluates the learning outcomes in relation to tooth morphology of two student cohorts from different academic terms, comparing the traditional \'tooth puzzle\' method to an alternative fully digital approach.
METHODS: Two groups of Master of Dentistry students were retrospectively analyzed. The control group (55 students) was taught via the \'tooth puzzle\' method in 2021, while the experimental group (55 students) underwent a fully digital course in 2020 due to COVID-19 restrictions. Both groups completed a digital examination involving the identification of 40 teeth, presented as images and videos.
RESULTS: In the control group of 55 students, nearly half (49.1%) achieved faultless results, while 65.5% had at most two faults, and 74.5% had no more than four faults. The group had a total of 163 faults, averaging 3.0 per student, with only one student (1.8%) failing the test. In stark contrast, the experimental group had no students without faults, 9.1% had four or fewer faults, and a significant 61.8% made 10 or more faults, with 29.1% failing their first test attempt by exceeding 12 faults. Overall, the experimental group registered 582 faults, averaging 10.6 per student.
CONCLUSIONS: The \'tooth puzzle\' method, with its interactive and tactile elements, proved more effective in teaching tooth morphology than the digital-only approach. The increased number of faults and failed tests in the experimental group suggest that while digital tools offer meaningful support in learning tooth morphology, their main advantage is seen when coupled with traditional hands-on techniques, not unassisted and independently.

Dental anatomy education for dental technology students should be developed in alignment with digital dental laboratory practices. We hypothesized that a virtually assisted sketching-based dental anatomy teaching module could improve students\' acquisition of skills essential for digital restoration design. The second-year dental technology curriculum included a novel virtual technology-assisted sketching-based module for dental anatomy education. Pre- and post-course assessments evaluated students\' skill sets and knowledge bases. Computer-aided design (CAD) scores were analyzed after one year to assess how the skills students developed through this module impacted their subsequent CAD performance. Participants who undertook the dental sketching-based teaching module demonstrated significantly improved theoretical knowledge of dental anatomy, dental aesthetic perception, and spatial reasoning skills. A partial least squares structural equation model indicated that the positive effects of this module on subsequent CAD performance were indirectly mediated by dental aesthetic perception, spatial reasoning, and practice time. A virtually assisted sketching-based dental anatomy teaching module significantly improved students\' acquisition of skills and knowledge and positively mediated dental technology students\' CAD performance.

BACKGROUND: In complex teeth like maxillary premolars, endodontic treatment success depends on a complete comprehension of root canal anatomy. The research on mandibular premolars\' root canal anatomy has been extensive and well-documented in existing literature. However, there appears to be a notable gap in available data concerning the root canal anatomy of maxillary premolars. This study aimed to explore the root canal morphology of maxillary premolars using cone-beam computed tomography (CBCT) imaging, considering age and gender variations.
METHODS: From 500 patient CBCT scans, 787 maxillary premolar teeth were evaluated. The sample was divided by gender and age (10-20, 21-30, 31-40, 41-50, 51-60, and 61 years and older). Ahmed et al. classification system was used to record root canal morphology.
RESULTS: The most frequent classifications for right maxillary 1st premolars were 2MPM1 B1 L1 (39.03%) and 1MPM1 (2.81%), while the most frequent classifications for right maxillary 2nd premolars were 2MPM1 B1 L1 (39.08%) and 1MPM1 (17.85%). Most of the premolars typically had two roots (left maxillary first premolars: 81.5%, left maxillary second premolars: 82.7%, right maxillary first premolars: 74.4%, right maxillary second premolars: 75.7%). Left and right maxillary 1st premolars for classes 1MPM1 and 1MPM1-2-1 showed significant gender differences. For classifications 1MPM1 and 1MPM1-2-1, age-related changes were seen in the left and right maxillary first premolars.
CONCLUSIONS: This study provides novel insights into the root canal anatomy of maxillary premolars within the Saudi population, addressing a notable gap in the literature specific to this demographic. Through CBCT imaging and analysis of large sample sizes, the complex and diverse nature of root canal morphology in these teeth among Saudi individuals is elucidated. The findings underscore the importance of CBCT imaging in precise treatment planning and decision-making tailored to the Saudi population. Consideration of age and gender-related variations further enhances understanding and aids in personalized endodontic interventions within this demographic.

Dental anatomy education is traditionally structured into theoretical and practical modules to foster both cognitive and psychomotor development. The theoretical module typically involves didactic lectures where educators elucidate dental structures using visual aids. In contrast, practical modules utilize three-dimensional illustrations, extracted and plastic teeth, and tooth carving exercises on wax or soap blocks, chosen for their cost, ease of handling, and fidelity in replication. However, the efficacy of these traditional methods is increasingly questioned. The criticism in this concern is that oversized carving materials may distort students\' understanding of anatomical proportions, potentially affecting the development of necessary skills for clinical practice. Lecture-driven instruction, on the other hand, is also criticized for its limitations in fostering interactive learning, resulting in a gap between pre-clinical instruction and practical patient care. In this study, we review the various educational strategies that have emerged to enhance traditional dental anatomy pedagogy by describing the effectiveness of conventional didactic lectures, wax carving exercises, the use of real and artificial teeth, the flipped classroom model, and e-learning tools. Our review aims to assess each method\'s contribution to improving clinical applicability and educational outcomes in dental anatomy, with a focus on developing pedagogical frameworks that align with contemporary educational needs and the evolving landscape of dental practice. We suggest that the optimal approach for teaching tooth morphology would be to integrate the digital benefits of the flipped classroom model with the practical, hands-on experience of using extracted human teeth. To address the challenges presented by this integration, the creation and standardization of three-dimensional tooth morphology educational tools, complemented with concise instructional videos for a flipped classroom setting, appears to be a highly effective strategy.

In this case report, two uncommon fractures of the root located at the apex of the mandibular anterior teeth region were successfully treated and preserved using non-surgical endodontic procedures. These teeth possess a unique size, characterized by the presence of exceptionally elongated lateral incisors measuring approximately 29 mm, central incisors measuring approximately 25 mm, and canines measuring approximately 30 mm. For the lower left central incisor, the root canal was carefully navigated to the coronal fragment, whereas the apical fragment was left undisturbed and preserved. In the case of the remarkably elongated lower right lateral incisor, the process of \'unification\' between the apical and coronal fragments was accomplished through the utilization of Gutta-percha and Ah plus sealer. Subsequently, the mandibular anterior teeth were stabilized and immobilized using dental splints for a duration of one and a half months, with regular follow-ups conducted over a span of six months and one year, during which positive healing outcomes were observed.

In prosthodontic procedures, traditional computer-aided design (CAD) is often time-consuming and lacks accuracy in shape restoration. In this study, we combined implicit template and deep learning (DL) to construct a precise neural network for personalized tooth defect restoration.
Ninety models of right maxillary central incisor (80 for training, 10 for validation) were collected. A DL model named ToothDIT was trained to establish an implicit template and a neural network capable of predicting unique identifications. In the validation stage, teeth in validation set were processed into corner, incisive, and medium defects. The defective teeth were inputted into ToothDIT to predict the unique identification, which actuated the deformation of the implicit template to generate the highly customized template (DIT) for the target tooth. Morphological restorations were executed with templates from template shape library (TSL), average tooth template (ATT), and DIT in Exocad (GmbH, Germany). RMSestimate, width, length, aspect ratio, incisal edge curvature, incisive end retraction, and guiding inclination were introduced to assess the restorative accuracy. Statistical analysis was conducted using two-way ANOVA and paired t-test for overall and detailed differences.
DIT displayed significantly smaller RMSestimate than TSL and ATT. In 2D detailed analysis, DIT exhibited significantly less deviations from the natural teeth compared to TSL and ATT.
The proposed DL model successfully reconstructed the morphology of anterior teeth with various degrees of defects and achieved satisfactory accuracy. This approach provides a more reliable reference for prostheses design, resulting in enhanced accuracy in morphological restoration.
This DL model holds promise in assisting dentists and technicians in obtaining morphology templates that closely resemble the original shape of the defective teeth. These customized templates serve as a foundation for enhancing the efficiency and precision of digital restorative design for defective teeth.