Background Dental education is undergoing continuous development to align with evolving healthcare demands. Understanding students’ academic experiences, curriculum perceptions, research involvement, and career expectations is essential for improving training quality. This study aimed to assess these factors among dental students. Methods This cross-sectional descriptive study included 194 dental students from the second year to the internship level. Participants were recruited using convenience sampling. Data were collected through a structured, self-administered questionnaire assessing academic experiences, curriculum perceptions, research involvement, community engagement, and career expectations. Descriptive statistics and chi-square tests were performed, with significance set at p < .05. Results Of the participants, 56.7% were female, and 43.3% reported having considered leaving dental school. Research involvement was reported by 40.7% of students, with lack of knowledge and time constraints identified as key barriers. Time-management difficulties were the most commonly reported academic challenge (33.0%). Perceptions of the curriculum were divided, with significant differences observed across academic levels (p = .020). Differences between preclinical and clinical training were reported by 48.5% of students (p < .001). Career expectations were generally high (65.5%), although they varied significantly across academic levels (p = .001). Conclusion Dental students reported varied academic experiences, with challenges in time management, research engagement, and clinical transition. These exploratory findings highlight the need for targeted educational support and further validation.
Abdullah F. Alshammari, Sarah A. Alenezi, Abdulkarim A. Alnasrallah et al.· Journal of Medical Education...· 0 citations
Background The quality of cavity preparation critically influences the longevity and success of restorative dental treatments. Current assessment methods remain largely subjective, relying on visual inspection and examiner judgment, which are prone to variability and limited reproducibility. Although three-dimensional (3D) imaging enables quantitative evaluation, its routine use in clinical and educational settings is limited by cost, accessibility, and workflow complexity. Objective This study aimed to develop an automated, objective, and clinically interpretable framework for evaluating dental cavity preparation quality using standard two-dimensional (2D) images, with optional integration of 3D depth information. Methods A deep learning pipeline based on enhanced U-Net architectures was developed to automatically segment cavity and cusp regions from 2D molar photographs. Anatomically informed geometric analyses were applied to quantify cavity-shape similarity, intercuspal distance, isthmus width, and cavity proportionality. Global cavity-shape conformity was assessed using Elliptic Fourier Descriptors (EFDs), enabling scale-, rotation-, and translation-invariant comparisons with reference preparations. When 3D STL data were available, cavity depth and cavity-bed smoothness were additionally quantified. These measurements were integrated into a transparent Cavity Quality Score (CQS) ranging from 1 to 10. Results The cavity segmentation model achieved an internal validation Dice coefficient of 0.81 and an Intersection-over-Union of 0.74, while cusp segmentation achieved a Dice coefficient of 0.83. External validation using measurements from three independent experts demonstrated close agreement between automated predictions and expert consensus for EFD cavity-shape similarity (MAE = 1.32 percentage points; r = 0.981), pooled isthmus-width measurements (MAE = 0.03 mm; r = 0.995), pooled cusp-pair distances (MAE = 0.08 mm; r = 0.999), and cavity depth estimation (absolute error ≈ 0.01 mm). Conclusion This study presents a hybrid, explainable artificial intelligence framework for objective assessment of dental cavity preparation using widely available 2D images. By integrating deep learning with anatomically informed geometric analysis, the proposed CQS offers a transparent and scalable tool for formative feedback in clinical and competency-based dental education. Further validation against expert summative grading is required before high-stakes implementation.
Abdullah F Alshammari, B. A. Anazi, M. Khan et al.· Frontiers in Artificial Inte...· 0 citations
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