Background: Short dental implants (≤6 mm) are increasingly used for posterior rehabilitation in patients with limited alveolar bone height. This review examines their survival rates, marginal bone loss (MBL), peri-implantitis, and technical/prosthetic complications compared with standard-length implants. Methods: A narrative review of the literature was conducted using PubMed, Scopus, and Cochrane Library databases. Search terms included 'short dental implants', 'posterior edentulism', 'survival rates', 'marginal bone loss', 'peri-implantitis', and 'bone augmentation'. Randomized controlled trials (RCTs), systematic reviews, meta-analyses, and prospective and retrospective clinical studies published between 2018 and 2024 were included. Studies were selected based on implant intrabony length of ≤6 mm, a minimum follow-up of 12 months, and reporting of at least one primary outcome. Results: Evidence from a meta-analysis of 16 RCTs (408 short implants; 475 standard-length implants) demonstrates statistically higher survival rates for longer implants (99.4% vs. 95.1%; 95% CI: 2–5%, p < 0.001), though no significant difference was found in early or late implant failure rates. MBL was marginally but non-significantly lower for short implants (0.23 mm vs. 0.27 mm). Peri-implantitis prevalence (0–6% vs. 0–13%) and technical/prosthetic complication rates were comparable between groups. Crown-to-implant ratios of 1.55–1.86 in short implants did not translate to elevated complication rates when prostheses were splinted. A systematic review and meta-analysis of 13 RCTs confirmed equivalent survival at all follow-up intervals without the need for bone augmentation. Conclusions: Short dental implants represent a valid, minimally invasive alternative to bone augmentation in appropriately selected patients. Optimal outcomes depend on modern nano-structured implant surfaces, adequate prosthetic splinting, bone quality assessment, and rigorous peri-implant maintenance. Further long-term RCTs with standardized peri-implantitis criteria are needed.

Dens invaginatus is a developmental anomaly resulting from an infolding of enamel and dentin during odontogenesis, often leading to complex internal anatomy and early pulpal involvement. The presence of atypical apical morphology further complicates diagnosis and endodontic management. This case report describes the diagnosis and nonsurgical endodontic management of a rare variant of dens invaginatus in a maxillary anterior tooth, characterized by a single root exhibiting two different apical morphologies: one normally formed closed apex and an additional lateral open apex on the palate-distal aspect of the same root. The patient presented with severe anterior pain following recent trauma and was associated with a large periapical lesion. Cone-beam computed tomography (CBCT) played a decisive role in identifying the complex canal configuration and guiding treatment planning. A staged endodontic approach was adopted, including calcium hydroxide intracanal medicament, apical barrier formation using biodentine, and a hybrid obturation technique employing gutta-percha and thermoplasticized gutta-percha. Favorable clinical and radiographic outcomes were observed. This report emphasizes the importance of advanced imaging, careful canal negotiation, and customized obturation strategies in managing rare endodontic anomalies.

Background: Artificial intelligence (AI), particularly convolutional neural networks (CNNs) and vision transformers (ViTs), is transforming dental radiographic diagnosis across multiple clinical domains. Methods: A narrative review searched PubMed, PubMed Central, BMC Oral Health, and Google Scholar for systematic reviews, meta-analyses, and original studies published between 2019 and 2025, covering ten domains: caries detection, periapical lesion identification, alveolar bone loss, peri-implant assessment, tooth detection and segmentation, cone-beam computed tomography (CBCT) analysis, vertical root fracture (VRF) detection, cephalometric landmark analysis, dental anomaly detection, and AI-versus-clinician comparison. Results: Pooled sensitivity was 0.94 and specificity 0.91 for caries detection on bitewing radiographs. Periapical AI sensitivity was 92.9% with specificity of only 58.6%, yielding a positive predictive value of 15.3%. Peri-implant bone loss assessment achieved AUC of 0.94. Vision transformers outperformed CNNs in 58% of dental imaging studies. VRF detection accuracy ranged from 75% to 97.8%. Cephalometric landmark detection rate reached 98.29%. AI diagnosed radiographs in 1.5 seconds versus 53.8 seconds for clinicians. ANSI/ADA Standard 1110-1:2025 provides the first formal dental AI validation framework. Conclusions: AI achieves substantial and reproducible diagnostic accuracy across dental radiographic tasks. High false-positive rates, demographic dataset bias, limited multi-centre external validation, and regulatory gaps must be resolved before broad clinical implementation.