Tooth

BackgroundThe surgical extraction of impacted mandibular third molars present significant clinical challenges, where accurate preoperative assessment is crucial to mitigate risks such as Inferior Alveolar Nerve injury. Although artificial intelligence shows promise in dental radiology, existing approaches are often limited to binary classification, affected by class imbalance, and lack standardized evaluation protocols, thereby restricting their clinical applicability.MethodsThis study proposes two independent deep learning frameworks for comprehensive analysis of third molar impactions. The first framework is an end-to-end object detection pipeline employing modified YOLOv10 and YOLOv11n architectures enhanced with multihead self-attention. The second framework is a feature-based classification approach, where deep features extracted using ResNet50 and InceptionNetV3 are classified using traditional machine learning algorithms.ResultsValidated on a multinational dataset of 5796 expertly annotated orthopantomograms with high inter-rater agreement (κ = 0.92), the proposed frameworks demonstrated competitive performance. The Fine KNN classifier using ResNet50 features achieved the best classification performance, yielding 97.56% accuracy, 96.07% precision, 96.21% recall, and an F1-score of 96.10%, while InceptionNetV3-based classification achieved 97.33% accuracy with an F1-score of 95.30%. For object detection, YOLOv11n attained a mean average precision of 88.9% (mAP@0.5) and 85.7% (mAP@0.5:0.95), while maintaining substantially lower computational complexity (19.7 vs 28.4 GFLOPs). Ablation experiments confirmed that the integration of multihead self-attention modules and generative adversarial network-based augmentation improved detection performance by 6.4% mean average precision.ConclusionsThe proposed frameworks enable accurate and automated multiclass assessment of third molar impactions, achieving high diagnostic performance while preserving computational efficiency suitable for clinical deployment. This work advances artificial intelligence-assisted surgical planning by providing reliable F1-score-based evaluation, reliable real-time detection, and enhanced preoperative risk stratification in oral and maxillofacial surgery.

PurposeThis narrative review aims to assimilate the current state of evidence for considering pulpotomy as a treatment option to manage vital primary teeth clinically diagnosed with irreversible pulpitis.MethodsA structured literature search was undertaken on four electronic databases (MEDLINE, Embase, Web of Science, and Cochrane Library) to compile evidence for this narrative review. Eligible studies included clinical investigations and systematic reviews assessing outcomes of pulpotomy for treating primary teeth with clinical signs and symptoms of irreversible pulpitis.ResultsSeven clinical studies and two meta-analyses met the inclusion criteria for data synthesis. Across the clinical studies,clinical success of pulpotomy for management of irreversible pulpitis-affected primary teeth ranged from 95-100%, while radiographic success ranged from 90-100% at the 6-month to 1-year post-treatment follow-up. The meta-analyses of these clinical studies concluded that pulpotomy may represent a viable alternative to conventional pulpectomy for the management of irreversible pulpitis in primary teeth. Based on current evidence, new diagnostic considerations and treatment procedural guidelines have been proposed for primary tooth pulpitis.ConclusionsAccumulating histological and clinical evidence supports pulpotomy as a successful, minimally invasive treatment option for management of primary teeth with clinical symptoms of irreversible pulpitis. The paper calls for revision of international guidelines to incorporate these developments and reduce unnecessary invasive interventions.

Dental tissues development involves two distinct cell lineages: mesenchymal cells (derived from the cranial neural crest) and epithelial cells (derived from oral ectoderm and pharyngeal epithelium). Emerging evidence highlights the remarkable functional heterogeneity of cranial neural crest-derived dental mesenchymal stem cells (DMSCs), exhibiting pluripotency, self-renewal, and differentiation capacities. This heterogeneity enables a single DMSC population to generate specialized subpopulations with unique roles in teeth and periodontal tissues formation. Significant progress has been made in characterizing six major types of DMSCs and two populations of closely related cells: Tooth germ progenitor cells (TGPCs) and dental follicle stem cells (DFSCs), critical during early morphogenesis; Stem cells from human exfoliated deciduous teeth (SHEDs) and apical papilla stem cells (SCAPs), pivotal for root development; Dental pulp stem cells (DPSCs), periodontal ligament stem cells (PDLSCs), gingival mesenchymal stem cells (GMSCs) and alveolar bone mesenchymal stem cells (ABMSCs), essential for maintaining and regenerating mature dental tissues. A key breakthrough has unveiled the development and hierarchy of DMSCs by applying new techniques like single-cell RNA sequencing (scRNA-seq). To integrate insights into the development of teeth and periodontal tissues, this review synthesizes current knowledge on both developmental heterogeneity and subpopulation heterogeneity within DMSCs and related cells. These insights not only advance fundamental understanding of the developmental mechanisms of teeth and periodontal tissues, but also establish a promising framework for achieving more efficient tissue regeneration and repair engineering.