ArticleBMC oral health2024
Deep learning-based prediction of indication for cracked tooth extraction using panoramic radiography.
Article in BMC oral health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial Intelligence in Dental Treatment Planning and Diagnostic Decision-Making: A Systematic Review and Meta-Analysis.Clinical and experimental dental research · 2026Pooled it
- Reliability of the QUADAS-2 checklist in AI-based medical imaging: A comparison of human reviewers and ChatGPT-4o.Imaging science in dentistry · 2026Article
- Bi-linguistic performance of large language models in multimodal analysis for differentiating jawbone-destroying malignancy from osteomyelitis.BMC oral health · 2026Article
- Use of Artificial Intelligence in Diagnosing Vertical Root Fractures-A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- Saving the split: An umbrella review on therapeutic approaches for cracked tooth syndrome.Journal of conservative dentistry and endodontics · 2026Review
- A Biomimetic Adhesive Approach for Restoring a Cracked Tooth in a Single Visit: A Case Report.Case reports in dentistry · 2026Article
- Evaluation of Apical Closure in Panoramic Radiographs Using Vision Transformer Architectures ViT-Based Apical Closure Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Segmentation of airways and soft tissues on panoramic radiographs using artificial intelligence technology.BMC oral health · 2025Article
- Deep learning-based prediction of possibility for immediate implant placement using panoramic radiography.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundWe aimed to determine the feasibility of utilizing deep learning-based predictions of the indications for cracked tooth extraction using panoramic radiography.
methodsPanoramic radiographs of 418 teeth (group 1: 209 normal teeth; group 2: 209 cracked teeth) were evaluated for the training and testing of a deep learning model. We evaluated the performance of the cracked diagnosis model for individual teeth using InceptionV3, ResNet50, and EfficientNetB0. The cracked tooth diagnosis model underwent fivefold cross-validation with 418 data instances divided into training, validation, and test sets at a ratio of 3:1:1.
resultsTo evaluate the feasibility, the sensitivity, specificity, accuracy, and F1 score of the deep learning models were calculated, with values of 90.43-94.26%, 52.63-60.77%, 72.01-75.84%, and 76.36-79.00%, respectively.
conclusionWe found that the indications for cracked tooth extraction can be predicted to a certain extent through a deep learning model using panoramic radiography.
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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.