Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 2 appraisals
Clinical oral investigations
Deep learning-based automated assessment of alveolar bone loss in CBCT for periodontitis
OBJECTIVES: This study aimed to develop and validate a CBCT-based automated system for assessing radiographic alveolar bone loss (RBL) to improve the accuracy and efficiency of periodontitis diagnosis. METHODS: A total of 110 patients (2,796 teeth) with Stage I-IV periodontitis from four center were included. The nnU-Net framework was used to segment teeth, alveolar bone, and the cemento-enamel junction (CEJ). RBL was calculated automatically using an edge-constrained shortest path algorithm. The model was trained on data from Center A and externally validated with datasets from Centers B-D. Linear periodontal measurements from 11 CBCT scans were compared between manual and CAD-based segmentation. An independent validation set was used to assess automated staging accuracy and time efficiency. RESULTS: The CAD system achieved Dice similarity coefficients (DSC) of 95.85% for teeth, 95.75% for alveolar bone, and 86.18% for the CEJ. External validation showed alveolar bone and tooth DSC values both above 95% and CEJ DSC values above 77%. Linear measurements showed strong agreement with manual segmentation (Spearman's ρ = 0.9187; ICC = 0.9266). For staging, the CAD system reached an overall accuracy of 87.31%, with 97.01% for Stage Ⅰ, 88.06% for Stage Ⅱ and 89.55% for Stage Ⅲ/Ⅳ. The CAD system represented a 13.42-fold acceleration compared with the manual workflow. CONCLUSION: The CAD system enables accurate automated segmentation and RBL quantification on CBCT images, with robust multicenter performance and substantial gains in efficiency. CLINICAL SIGNIFICANCE: This system offers a fast and reliable method for RBL assessment, supporting consistent diagnosis and monitoring of periodontitis.
19 July 2026
Read appraisal →Journal of dental research
AI in Oral Health Surveillance: Critical Review
Artificial intelligence (AI) holds transformative potential for advancing oral health surveillance by streamlining data collection, integration, and dissemination. This review critically synthesizes AI applications in oral health surveillance, highlighting its roles in 1) mapping population-level trends and oral health inequities using machine learning on epidemiological data; 2) enabling remote screening of oral diseases/conditions, including caries, oral hygiene, gingivitis, oral cancer, and malocclusion from intraoral images via computer vision models; and 3) integrating multimodal data through emerging large language models (LLMs) to enhance precision public health. We clarify the comparative strengths of distinct AI modeling for processing the primary data types in surveillance: structured clinical records, unstructured images, and integrated multimodal data. Traditional machine learning methods have been effectively applied to map population-level oral health disparities and identify risk factors but are constrained to structured data. Computer vision methods excel in individual-level diagnostics using intraoral photographs. To translate such capability into scalable surveillance, it is recommended to establish standardized imaging protocols for nonclinical settings, develop scalable models for fine-grained feature extraction, and implement reliable evaluation. These steps are essential to address pervasive challenges, including inconsistent image quality, domain shift, prevalence imbalance, and cost-effectiveness constraints. The future of AI-driven oral health surveillance lies in developing dental-adapted multimodal LLMs (MLLMs). Such MLLMs are uniquely capable of synthesizing disparate data streams, from structured clinical data to heterogeneous imaging modalities (e.g., intraoral photographs and radiographs), or even biomolecular data. This integration capacity facilitates a paradigm shift, moving current applications in dental consultation and clinical decision support toward a novel, tiered system for population-level monitoring. Such a system would provide actionable insights for public health policymaking via spatiotemporal analysis and causal inference. Next-generation AI-driven oral health surveillance systems can only succeed when built on a strong foundation of rigorous ethical principles and safeguards.
2 July 2026
Read appraisal →