Dental Radiography and Imaging

Lightweight neural networks analyze salivary metabolics via mass spectrometry to screen for periodontitis and diabetes, achieving 91.9% accuracy with minimal computational resources for clinical.

Deep learning framework with quantum-inspired feature fusion achieves 99.33% accuracy for automated enamel caries classification in intraoral photographs with visual explainability.

Multimodal imaging fusion enhances orthodontic diagnostics and enables seamless collaboration among dental specialists through standardized data integration.

Prospective study comparing crestal bone loss around dental implants placed with conventional drilling, bone expansion, and ridge split techniques over 12-month follow-up.

Split-mouth trial shows 3D interlocking miniplates produce faster bone healing and better nerve recovery than conventional plates for mandibular fractures near the mental foramen.

Supervised machine learning models detect morphometric patterns of temporomandibular joint disc displacement on 3T MRI, potentially supporting radiologic assessment.

Explore a competency framework for integrating AI education into dental curricula, from foundational knowledge to clinical implementation and innovation across preclinical and clinical training.

Systematic review confirms periodontitis increases acute myocardial infarction risk by 84%, with radiographic bone loss showing strongest association independent of traditional cardiovascular factors.

YOLOv12-based automatic detection of cephalometric landmarks on lateral skull X-rays achieves 80.57% accuracy within 2 mm, matching human variability standards.

Case report of odontogenic myxoma in a 5-year-old presenting with trismus, demonstrating persistent functional impairment despite successful tumor removal and bone regeneration.

Sagittal cone-beam CT imaging enables reliable measurement of facial recess size for preoperative assessment in cochlear implantation, aligning with surgical perspective.

AI-based image processing maintained diagnostic quality in dental CBCT scans at 20% standard radiation dose, but not at 10% dose, with implications for reducing cumulative radiation exposure.

Global meta-analysis of 768,263 individuals reveals first permanent molar dental caries prevalence at 33%, with higher rates in Africa, Europe, and Latin America compared to Asia.

Deep learning framework quantifies craniofacial growth patterns and sexual dimorphism from cephalometric radiographs using automated feature extraction and saliency mapping without manual annotation.

CBCT analysis of 800 maxillary premolars in Jordanian patients reveals diverse root and canal configurations, with Ahmed's classification capturing variations missed by Vertucci's system.

Study examines how titanium osteosynthesis plates alter radiation scatter in mandibular reconstruction, affecting cellular responses and treatment planning outcomes.

Deep learning vision-language model for diagnosing pediatric dental diseases in panoramic radiographs, combining visual and textual information with 90% accuracy for caries and periapical.

Study assesses periodontal ligament visibility in third molars for forensic age estimation at the 21-year legal threshold in Turkish population using ROC analysis.

Theoretical perfusion model using intercapillary distance to regulate tissue denitrogenation rates across compartments, with applications to decompression protocols and DCS risk assessment.

Deep learning model using ResNet-50 classifies fibro-osseous jaw lesions from histology images with 86% accuracy, outperforming pathologists in differentiating fibrous dysplasia, cemento-ossifying.