ArticleBMC oral health2023
Artificial intelligence in the diagnosis of dental diseases on panoramic radiographs: a preliminary study.
Article in BMC oral health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
45 citing papers in PubMed, 1 synthesis or guideline pooled it, 78 citations in OpenAlex.
- Artificial Intelligence in Dental Treatment Planning and Diagnostic Decision-Making: A Systematic Review and Meta-Analysis.Clinical and experimental dental research · 2026Pooled it
- Deep Learning-Based Automated Diagnostic Charting on Panoramic Radiography: Comparison of YOLOv11 and YOLOv12.Odontology · 2026Article
- Accuracy of large language models in the Turkish dental specialization examination (DUS): a multidimensional evaluation across disciplines and question formats.BMC oral health · 2026Article
- AI-Driven Dentistry and Public Health Surveillance: Opportunities and Challenges.Public health challenges · 2026Review
- Diagnostic Accuracy of Artificial Intelligence Models for Detecting Odontogenic Keratocytes on CBCT Imaging: A Systematic Review and Meta-Analysis.Health science reports · 2026Review
- A multimodal vision-language model for comprehensive dental diagnosis and enhanced clinical practice.Nature communications · 2026Article
- Deep‑learning‑based detection of open‑apex teeth on panoramic radiographs using YOLO models.Oral radiology · 2026Article
- U-net-based segmentation of foreign bodies and ghost images in panoramic radiographs.Oral radiology · 2026Article
- Artificial intelligence in diagnostic imaging: collaborative asset or looming replacement?Oral radiology · 2026Review
- Prediction of preterm and low birth weight risk using a physiology based artificial neural network integrating hematological, dental, and periodontal index markers: a cross sectional study based on machine learning.BMC pregnancy and childbirth · 2026Article
- AI-Based Detection of Periapical Lesions in Endodontic Radiographs: a Diagnostic Accuracy Study.Maedica · 2026Article
- CBCT-Based Orthodontic Classification Using Commercial AI: Completeness and Accuracy in Independent Validation.Journal of clinical medicine · 2026Article
- AI Integration in Brazilian Master's Curricula and Its Implications in Dental Education.International journal of dentistry · 2026Article
- Artificial intelligence for dental caries diagnosis: translating algorithms to clinical practice.Frontiers in medicine · 2026Review
- Deep learning-based dental age estimation in adolescents using panoramic radiographs: a comparative study with traditional methods.Frontiers in radiology · 2026Article
- Evaluation of the diagnostic reliability of an AI prototype in detecting clinical features from dental photographs-original research.Frontiers in dental medicine · 2026Article
- Anticipated challenges facing the dental profession: A narrative review.Journal of public health research · 2026Review
- Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x.BMC oral health · 2025Article
- Diagnostic Accuracy and Efficiency of AI-Assisted Radiographic Interpretation Compared to Conventional Methods in Early Detection of Dental Caries.Journal of pharmacy & bioallied sciences · 2025Article
- Glandular odontogenic cyst: a ten-case series and literature review with emphasis on radiological diagnostic challenges.BMC oral health · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) has been introduced to interpret the panoramic radiographs (PRs). The aim of this study was to develop an AI framework to diagnose multiple dental diseases on PRs, and to initially evaluate its performance.
methodsThe AI framework was developed based on 2 deep convolutional neural networks (CNNs), BDU-Net and nnU-Net. 1996 PRs were used for training. Diagnostic evaluation was performed on a separate evaluation dataset including 282 PRs. Sensitivity, specificity, Youden's index, the area under the curve (AUC), and diagnostic time were calculated. Dentists with 3 different levels of seniority (H: high, M: medium, L: low) diagnosed the same evaluation dataset independently. Mann-Whitney U test and Delong test were conducted for statistical analysis (ɑ=0.05).
resultsSensitivity, specificity, and Youden's index of the framework for diagnosing 5 diseases were 0.964, 0.996, 0.960 (impacted teeth), 0.953, 0.998, 0.951 (full crowns), 0.871, 0.999, 0.870 (residual roots), 0.885, 0.994, 0.879 (missing teeth), and 0.554, 0.990, 0.544 (caries), respectively. AUC of the framework for the diseases were 0.980 (95%CI: 0.976-0.983, impacted teeth), 0.975 (95%CI: 0.972-0.978, full crowns), and 0.935 (95%CI: 0.929-0.940, residual roots), 0.939 (95%CI: 0.934-0.944, missing teeth), and 0.772 (95%CI: 0.764-0.781, caries), respectively. AUC of the AI framework was comparable to that of all dentists in diagnosing residual roots (p > 0.05), and its AUC values were similar to (p > 0.05) or better than (p < 0.05) that of M-level dentists for diagnosing 5 diseases. But AUC of the framework was statistically lower than some of H-level dentists for diagnosing impacted teeth, missing teeth, and caries (p < 0.05). The mean diagnostic time of the framework was significantly shorter than that of all dentists (p < 0.001).
conclusionsThe AI framework based on BDU-Net and nnU-Net demonstrated high specificity on diagnosing impacted teeth, full crowns, missing teeth, residual roots, and caries with high efficiency. The clinical feasibility of AI framework was preliminary verified since its performance was similar to or even better than the dentists with 3-10 years of experience. However, the AI framework for caries diagnosis should be improved.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.