ArticleClinical oral investigations2021
Artificial intelligence-driven novel tool for tooth detection and segmentation on panoramic radiographs.
Article in Clinical oral investigations, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 65 papers, 3 of them syntheses that pooled it.
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Who cites it
65 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Deep learning for tooth detection and segmentation in panoramic radiographs: a systematic review and meta-analysis.BMC oral health · 2025Pooled it
- Panoramic imaging errors in machine learning model development: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Deep learning for tooth identification and numbering on dental radiography: a systematic review and meta-analysis.Dento maxillo facial radiology · 2024Pooled it
- Clinical Readiness of Artificial Intelligence Models for MB2 Canal Detection in Maxillary Molars: A Scoping Review.International dental journal · 2026Article
- The Image Behind the Algorithm.European journal of dentistry · 2026Article
- Tooth Detection and Numbering Performance of Two AI-Based Systems on Panoramic Radiographs: A Comparative Study.Dentistry journal · 2026Article
- Comparative Analysis of Third Molar Segmentation Performance Across Dental Developmental Stages and the 18-Year Age Threshold Using Deep Learning Models.Diagnostics (Basel, Switzerland) · 2026Article
- Context-Guided Mixture-of-Experts with Multimodal LLMs for Lesion Detection in Buccal Mucosa Images.International dental journal · 2026Article
- The Influence of Panoramic Radiograph Quality on the Accuracy of AI-Based Tooth Detection.Diagnostics (Basel, Switzerland) · 2026Article
- Teeth identification and numbering in mixed dentition: evaluating deep learning models for pediatric panoramic radiographs.BMC oral health · 2026Article
- AKUDENTAL teeth instance segmentation dataset: a cross-dataset analysis.BMC oral health · 2026Article
- Genetic and epidemiologic assessment of mandibular cortical indices and bone mineral density in peripubertal children: the Generation R study.Clinical oral investigations · 2025Article
- The Role of Artificial Intelligence in Orthodontics for Determining Skeletal Age Based on Cervical Vertebra Maturation Degree: A Comprehensive Review.Health science reports · 2025Article
- U-Net-Based Deep Learning for Simultaneous Segmentation and Agenesis Detection of Primary and Permanent Teeth in Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Detection and dual-label classification of tooth number and condition in dental panoramic radiographs including deciduous teeth.Radiological physics and technology · 2025Article
- The Validation of an Artificial Intelligence-Based Software for the Detection and Numbering of Primary Teeth on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2025Article
- Applications of Artificial Intelligence in Dental Medicine: A Critical Review.International dental journal · 2025Review
- Evaluation of the Performance of a YOLOv10-Based Deep Learning Model for Tooth Detection and Numbering on Panoramic Radiographs of Patients in the Mixed Dentition Period.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial intelligence application in endodontics: A narrative review.Imaging science in dentistry · 2024Review
- Evaluation of Dental Panoramic Radiographs by Artificial Intelligence Compared to Human Reference: A Diagnostic Accuracy Study.Journal of clinical medicine · 2024Article
5 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
objectiveTo evaluate the performance of a new artificial intelligence (AI)-driven tool for tooth detection and segmentation on panoramic radiographs. MATERIALS AND
methodsIn total, 153 radiographs were collected. A dentomaxillofacial radiologist labeled and segmented each tooth, serving as the ground truth. Class-agnostic crops with one tooth resulted in 3576 training teeth. The AI-driven tool combined two deep convolutional neural networks with expert refinement. Accuracy of the system to detect and segment teeth was the primary outcome, time analysis secondary. The Kruskal-Wallis test was used to evaluate differences of performance metrics among teeth groups and different devices and chi-square test to verify associations among the amount of corrections, presence of false positive and false negative, and crown and root parts of teeth with potential AI misinterpretations.
resultsThe system achieved a sensitivity of 98.9% and a precision of 99.6% for tooth detection. For segmenting teeth, lower canines presented best results with the following values for intersection over union, precision, recall, F1-score, and Hausdorff distances: 95.3%, 96.9%, 98.3%, 97.5%, and 7.9, respectively. Although still above 90%, segmentation results for both upper and lower molars were somewhat lower. The method showed a clinically significant reduction of 67% of the time consumed for the manual.
conclusionsThe AI tool yielded a highly accurate and fast performance for detecting and segmenting teeth, faster than the ground truth alone. CLINICAL SIGNIFICANCE: An innovative clinical AI-driven tool showed a faster and more accurate performance to detect and segment teeth on panoramic radiographs compared with manual segmentation.
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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.