ArticleDiagnostics (Basel, Switzerland)2024
The Detection of Pulp Stones with Automatic Deep Learning in Panoramic Radiographies: An AI Pilot Study.
Article in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Gonial Angle Estimation on Dental Panoramic X-Rays Using Deep Learning: A Transfer Learning Approach for Keypoint Detection.Journal of imaging informatics in medicine · 2026Article
- Deep‑learning‑based detection of open‑apex teeth on panoramic radiographs using YOLO models.Oral radiology · 2026Article
- Artificial intelligence in the detection of dental pulp calcifications: a scoping review.BMC oral health · 2026Article
- Revolutionizing endodontics: the impact and innovations of artificial intelligence.BMC oral health · 2026Review
- Artificial Intelligence in the Study of Root and Canal Anatomy: A Comprehensive Review on Applications, Advantages, Challenges and Future Directions.European endodontic journal · 2025Review
- Artificial intelligence-assisted identification of condensing osteitis and idiopathic osteosclerosis on panoramic radiographs.Scientific reports · 2025Article
- Segmentation of Pulp and Pulp Stones with Automatic Deep Learning in Panoramic Radiographs: An Artificial Intelligence Study.Dentistry journal · 2025Article
- Artificial intelligence-based detection of dens invaginatus in panoramic radiographs.BMC oral health · 2025Article
- Closing Editorial: Advancements in Artificial Intelligence for Dentomaxillofacial Radiology-Current Trends and Future Directions.Diagnostics (Basel, Switzerland) · 2025Article
- A novel deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs.Oral radiology · 2025Article
- Comparative evaluation of CNN architectures for detection of dental pulp calcifications on CBCT slices.Science progressArticle
- Transfer learning models in the detection of pulp calcifications- A preliminary study.Journal of oral biology and craniofacial researchArticle
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aims to evaluate the effectiveness of employing a deep learning approach for the automated detection of pulp stones in panoramic imaging. A comprehensive dataset comprising 2409 panoramic radiography images (7564 labels) underwent labeling using the CranioCatch labeling program, developed in Eskişehir, Turkey. The dataset was stratified into three distinct subsets: training (
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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.