ArticleScientific reports2022
Detecting 17 fine-grained dental anomalies from panoramic dental radiography using artificial intelligence.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 3 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 3 syntheses or guidelines pooled it, 35 citations in OpenAlex.
- Artificial Intelligence-assisted Diagnosis of Carotid Artery Calcifications on Panoramic Radiographs: A Meta-analysis.International dental journal · 2026Pooled it
- Detection of carotid artery calcifications using artificial intelligence in dental radiographs: a systematic review and meta-analysis.BMC medical imaging · 2025Pooled it
- Panoramic imaging errors in machine learning model development: a systematic review.Dento maxillo facial radiology · 2024Pooled it
- Artificial Intelligence-Assisted Detection of the Elongated Styloid Process on Dental Radiographic Images: A Systematic Review and Literature Update.Journal of clinical medicine · 2026Review
- Artificial Intelligence in the Diagnosis of Odontogenous Cysts and Ameloblastomas-A Systematic Review and Meta-Analysis.Journal of clinical medicine · 2026Review
- Deep Learning-Based Detection of Carotid Artery Atheromas in Panoramic Radiographs.Bioengineering (Basel, Switzerland) · 2026Article
- A visualization system for intelligent diagnosis and statistical analysis of oral diseases based on panoramic radiography.Scientific reports · 2025Article
- A novel deep learning-based pipeline architecture for pulp stone detection on panoramic radiographs.Oral radiology · 2025Article
- Advanced AI-driven detection of interproximal caries in bitewing radiographs using YOLOv8.Scientific reports · 2025Article
- Assessment of using transfer learning with different classifiers in hypodontia diagnosis.BMC oral health · 2025Article
- Detection of periodontal bone loss patterns and furcation defects from panoramic radiographs using deep learning algorithm: a retrospective study.BMC oral health · 2024Article
- Deep Learning in Diagnosis of Dental Anomalies and Diseases: A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Patients' perspectives on the use of artificial intelligence in dentistry: a regional survey.Head & face medicine · 2023Article
- Dental image enhancement network for early diagnosis of oral dental disease.Scientific reports · 2023Article
- Carbonic Anhydrase II Activators in Osteopetrosis Treatment: A Review.Current issues in molecular biology · 2023Review
- A Novel Deep Learning-Based Approach for Segmentation of Different Type Caries Lesions on Panoramic Radiographs.Diagnostics (Basel, Switzerland) · 2023Article
- A study on the diagnosis of theFrontiers in microbiology · 2022Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Panoramic dental radiography is one of the most common examinations performed in dental clinics. Compared with other dental images, it covers a wide area from individual teeth to the maxilla and mandibular area. Dental clinicians can get much information about patients' health. However, it is time-consuming and laborious to detect all signs of anomalies because these regions are very complicated. So it is needed to filter out healthy images to save clinicians' time to examine. For this, we applied modern artificial intelligence-based computer vision techniques. In this study, we built a model to detect 17 fine-grained dental anomalies which are critical to patients' dental health and quality of life. We used about 23,000 anonymized panoramic dental images taken from local dental clinics from July 2020 to July 2021. Our model can detect these abnormal signs and filter out normal images with high sensitivity of about 0.99. The result indicates that our model can be used in real clinical practice to alleviate the burden of clinicians.
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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.