ArticleClinical oral investigations2024
Insights into Predicting Tooth Extraction from Panoramic Dental Images: Artificial Intelligence vs. Dentists.
Article in Clinical oral investigations, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers, 4 of them syntheses that pooled it.
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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.
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Who cites it
11 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Explainable Artificial Intelligence in Dentistry: A Systematic Review of Its Trust and Translation.International dental journal · 2026Pooled it
- Explainability, Bias and Generalizability of AI Models in Dentistry: A Systematic Review of Model Interpretability and Equity.Clinical and experimental dental research · 2026Pooled it
- Explainable artificial intelligence in dental imaging: a systematic review of interpretability and the current state of trust evidence.Frontiers in dental medicine · 2026Pooled it
- Accuracy of artificial intelligence in orthodontic extraction treatment planning: a systematic review and meta analysis.BMC oral health · 2025Pooled 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
- Deep learning-based dental age estimation in adolescents using panoramic radiographs: a comparative study with traditional methods.Frontiers in radiology · 2026Article
- Article
- Evaluation of postoperative bleeding risk after dental extractions in patients on antithrombotic medication: A comparison of machine learning and clinical experience.Clinical oral investigations · 2025Article
- Shaping Orthodontics of the Future: Concepts and Implications from a Cellular and Molecular Perspective.International journal of molecular sciences · 2025Review
- Clinical Applications of Artificial Intelligence in Periodontology: A Scoping Review.Medicina (Kaunas, Lithuania) · 2025Article
- Diagnostic accuracy of artificial intelligence in determining extraction protocol in orthodontic patients: A systematic review.Journal of orthodontic science · 2025Review
Corrections and comments
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Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTooth extraction is one of the most frequently performed medical procedures. The indication is based on the combination of clinical and radiological examination and individual patient parameters and should be made with great care. However, determining whether a tooth should be extracted is not always a straightforward decision. Moreover, visual and cognitive pitfalls in the analysis of radiographs may lead to incorrect decisions. Artificial intelligence (AI) could be used as a decision support tool to provide a score of tooth extractability. MATERIAL AND
methodsUsing 26,956 single teeth images from 1,184 panoramic radiographs (PANs), we trained a ResNet50 network to classify teeth as either extraction-worthy or preservable. For this purpose, teeth were cropped with different margins from PANs and annotated. The usefulness of the AI-based classification as well that of dentists was evaluated on a test dataset. In addition, the explainability of the best AI model was visualized via a class activation mapping using CAMERAS.
resultsThe ROC-AUC for the best AI model to discriminate teeth worthy of preservation was 0.901 with 2% margin on dental images. In contrast, the average ROC-AUC for dentists was only 0.797. With a 19.1% tooth extractions prevalence, the AI model's PR-AUC was 0.749, while the dentist evaluation only reached 0.589.
conclusionAI models outperform dentists/specialists in predicting tooth extraction based solely on X-ray images, while the AI performance improves with increasing contextual information. CLINICAL RELEVANCE: AI could help monitor at-risk teeth and reduce errors in indications for extractions.
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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.