This narrative review explores the role of Artificial Intelligence (AI) in advancing the synthesis, optimization, and clinical translation of dental biomaterials. It critically evaluates how AI-driven approaches address existing challenges related to material performance, biocompatibility, and durability, while identifying current research gaps and outlining future perspectives. A comprehensive literature synthesis was conducted through electronic searches in PubMed, Scopus, Web of Science, and Google Scholar. Selected articles encompassed both experimental and computational studies. Case studies were analyzed to illustrate the application of AI techniques, including machine learning (ML) for predicting mechanical properties, deep learning (DL) for microstructural and imaging analysis, and optimization algorithms ( e.g. , genetic algorithms) for materials discovery and formulation enhancement. The findings indicate that ML models can accurately predict mechanical properties ( e.g. , flexural modulus), DL enables automated detection of material degradation, and optimization algorithms accelerate the discovery of advanced biomaterials such as high-entropy ceramics. AI also facilitates the development of personalized, adaptive materials, including pH-responsive and self-healing resin composites. Nonetheless, barriers remain, including data scarcity, limited generalizability, high computational demands, and lack of standardization. AI is poised to redefine the landscape of dental biomaterials by enabling smarter material design, improved performance prediction, and accelerated clinical translation. This review advances the field by integrating emerging computational approaches with practical needs and identifying unresolved challenges that hinder real-world adoption. Ultimately, leveraging AI alongside technologies such as 3D printing and digital twin systems can drive the development of next-generation biomaterials tailored to patient-specific clinical outcomes.

This study aimed to evaluate the influence of various sterilization techniques on the cyclic fatigue resistance, bending strength, and torsional fatigue resistance of Tornado Blue and Race Evo rotary endodontic file systems. Both the file systems underwent a series of 15 cycles of sterilization according to the following study groups: control group (G1; i.e., without treatment), chemical sterilization with 5.25% sodium hypochlorite (G2), dry heat sterilization (G3), glass beads sterilization (G4), and steam autoclaving (G5). Each group consisted of 8 samples (n = 8/group). Statistical analysis was performed using a two-way ANOVA and Tukey’s tests (p ≤ 0.05). Mean values for the number of cycles between both files were 1291 ± 52.79 rpm and 2095 ± 52.91 rpm for Tornado Blue and Race Evo, respectively. The torsional fatigue resistance was 8.00 ± 0.85 NCF and 10.70 ± 0.85 NCF and the bending strength was 486.94 ± 20.97 gf and 564.17 ± 20.97 gf for Tornado Blue and Race Evo, respectively. The results showed no significant difference in the mechanical properties followed by different sterilization protocols. However, a significant difference was observed in tested properties between the two file systems (p ≤ 0.05). The outcome is reassuring for clinicians that the essential process of routine sterilization at clinics seems safe with regards to mechanical integrity of the endodontic files.

Background/Objectives: Vertical root fractures (VRFs) present significant diagnostic challenges due to their subtle radiographic features and variability across imaging modalities. Artificial intelligence (AI) offers potential to improve detection accuracy, yet evidence regarding its performance across different imaging systems remains fragmented. To critically evaluate current evidence on AI-assisted detection of VRFs across periapical radiography, panoramic radiography, and cone-beam computed tomography (CBCT) and to compare diagnostic performance, methodological strengths, and limitations. Methods: A systematic review of literature up to January 2025 was carried out using databases such as PubMed, Scopus, Web of Science, and the Cochrane Library. The studies included in this review utilized AI-based techniques for detecting VRF through periapical, panoramic, or CBCT imaging. Extracted data encompassed study design, AI models, dataset sizes, preprocessing methods, imaging parameters, validation techniques, and diagnostic metrics. The risk of bias in these studies was evaluated using the QUADAS-2 tool. Results: Ten studies met inclusion criteria; CNN-based models predominated, with performance highly dependent on imaging modality. CBCT-based AI systems achieved the highest diagnostic accuracy (91.4–97.8%) and specificity (90.7–100%), followed by periapical radiography models with accuracies up to 95.7% in controlled settings. Panoramic radiography models demonstrated lower sensitivity (0.45–0.75) but maintained high precision (0.93) in certain contexts. Most studies reported improvements over human performance, yet limitations included small datasets, heterogeneous methodologies, and risk of overfitting. Conclusions: AI-assisted VRF detection shows promising accuracy, particularly with CBCT imaging, but current evidence is constrained by methodological variability and limited clinical validation.

This study aimed to assess and evaluate the quality of root canal fillings (RCFs) conducted on mandibular first molars performed by endodontists in the Asir region, Saudi Arabia. A cross-sectional radiographical study was conducted among patients over 18 years old from both genders treated by endodontists with different qualifications and experiences. The treatments were carried out under similar operating field, materials, and equipment. The quality of the X-rays was evaluated according to the international standards in the form of length, density, and taper. Those radiographs were assessed by two evaluators with similar certificates. Intraexaminer agreements were calculated. Chi-square or Fisher's exact tests were used to test the significant difference between groups and the quality parameters. p < 0.05 was considered a cutoff point for statistical significance. A total of 74 mandibular molars with 224 root canals treated were assessed in this radiographic study. The recorded adequate percentages were 77, 93, and 91% for the quality of RCF taper, density, and taper, respectively, with acceptable overall RCF quality of 87%. Significant differences were recorded between the right and left sides in terms of taper (p = 0.035), as well as between different years of experiences and different locations of the canals in terms of density (p = 0.040 for both variables). The quality of RCFs performed by endodontist with different type of certificates performed in mandibular first molars were high in terms of density and taper but moderate in length. The overall RCF quality was acceptable. Evaluation of the postoperative preapical X-rays for the endodontically treated teeth is a positive point in maintaining high standard services of patients. It should be followed as mentioned in the recommended textbooks in relation to length, taper, and density. How to cite this article: Almnea RA. Radiographic Assessment of the Quality of Root Canal Filling of Mandibular First Molars Performed by Endodontists in Asir Region, Saudi Arabia. J Contemp Dent Pract 2024;25(7):624-630.

The knowledge of Dentists and Endodontists in Saudi Arabia regarding antibiotic prescriptions during and after endodontic treatment is of interest. A self-designed questionnaire survey was utilized to assess the knowledge of dentists across various cities in Saudi Arabia concerning antibiotic usage guidelines for endodontic purposes. A total of 391 participants were included in the study, and the questionnaire was distributed through social platforms like WhatsApp, Instagram, Facebook Messenger, and email. Results revealed that over 80% of participants acknowledged the need for antibiotics in cases of acute apical abscess with cellulitis, with amoxicillin being the most recommended antibiotic by dentists. Interestingly, there was no statistically significant difference in knowledge based on experience or graduation group. In conclusion, while participants demonstrated adequate knowledge about antibiotic prescriptions in endodontic cases, continued awareness of updated guidelines, including the WHO's Essential Medicines List (EML), guidelines by the European Society of Endodontology (ESE) and American Association Endodontists (AAE) is essential for optimal clinical practice.