ArticleScientific reports2023
Artificial intelligence in diagnosing dens evaginatus on periapical radiography with limited data availability.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 2 of them syntheses that pooled it.
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Who cites it
7 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- Radiographic diagnosis of periodontitis using artificial intelligence: a meta-analysis comparing binary and staging classifications across imaging modalities.BMC oral health · 2025Pooled it
- Deep Learning in Dental Imaging: Advances, Challenges, and Future.Oral radiology · 2026Review
- Management of a Rare 4-Rooted Maxillary First Molar With 5 Canals and an Endo-Periodontal Lesion: A Case Report.The American journal of case reports · 2026Article
- Artificial Intelligence in The Diagnosis, Treatment, and Prognostication in Endodontics: A Comprehensive Literature Review.European endodontic journal · 2025Review
- [An lightweight algorithm for multi-dimensional optimization of intelligent detection of dental abnormalities on panoramic oral X-ray images].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2025Article
- CNN-based remote dental diagnosis model for caries detection with grad-CAM.Scientific reports · 2025Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop an artificial intelligence (AI) model using deep learning techniques to diagnose dens evaginatus (DE) on periapical radiography (PA) and compare its performance with endodontist evaluations. In total, 402 PA images (138 DE and 264 normal cases) were used. A pre-trained ResNet model, which had the highest AUC of 0.878, was selected due to the small number of data. The PA images were handled in both the full (F model) and cropped (C model) models. There were no significant statistical differences between the C and F model in AI, while there were in endodontists (p = 0.753 and 0.04 in AUC, respectively). The AI model exhibited superior AUC in both the F and C models compared to endodontists. Cohen's kappa demonstrated a substantial level of agreement for the AI model (0.774 in the F model and 0.684 in C) and fair agreement for specialists. The AI's judgment was also based on the coronal pulp area on full PA, as shown by the class activation map. Therefore, these findings suggest that the AI model can improve diagnostic accuracy and support clinicians in diagnosing DE on PA, improving the long-term prognosis of the tooth.
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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.