ArticleScientific reports2022
Comparison of detection performance of soft tissue calcifications using artificial intelligence in panoramic radiography.
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 12 papers, 2 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 2 syntheses or guidelines pooled it, 24 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
- Side- and patient-based performance of a deep learning system based on the results of individual detection of carotid artery calcifications on panoramic radiographs.Imaging science in dentistry · 2026Article
- Deep Learning-Based Detection of Carotid Artery Atheromas in Panoramic Radiographs.Bioengineering (Basel, Switzerland) · 2026Article
- Area detection improves the person-based performance of a deep learning system for classifying the presence of carotid artery calcifications on panoramic radiographs.Oral radiology · 2026Article
- Integrating artificial intelligence in healthcare: applications, challenges, and future directions.Future science OA · 2025Review
- Examination of the Frequency of Soft Tissue Ossification and Calcifications in Panoramic Radiographs: A Retrospective Study.Diagnostics (Basel, Switzerland) · 2025Article
- Applying deep learning techniques to identify tonsilloliths in panoramic radiography.Scientific reports · 2025Article
- Artificial Intelligence in Dentistry: A Descriptive Review.Bioengineering (Basel, Switzerland) · 2024Review
- Impact of human and artificial intelligence collaboration on workload reduction in medical image interpretation.NPJ digital medicine · 2024Article
- Influence of clinical experience in detecting calcifications of the head and neck region on panoramic radiographs: an app-based evaluation.Annals of medicine and surgery (2012) · 2024Article
- Deep convolution neural network for screening carotid calcification in dental panoramic radiographs.PLOS digital health · 2023Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
Artificial intelligence (AI) is limited to teeth and periodontal disease in the dental field, and is used for diagnosis assistance or data analysis, and there has been no research conducted in actual clinical situations. So, we created an environment similar to actual clinical practice and conducted research by selecting three of the soft tissue diseases (carotid artery calcification, lymph node calcification, and sialolith) that are difficult for general dentists to see. Therefore, in this study, the accuracy and reading time are evaluated using panoramic images and AI. A total of 20,000 panoramic images including three diseases were used to develop and train a fast R-CNN model. To compare the performance of the developed model, two oral and maxillofacial radiologists (OMRs) and two general dentists (GDs) read 352 images, excluding the panoramic images used in development for soft tissue calcification diagnosis. On the first visit, the observers read images without AI; on the second visit, the same observers used AI to read the same image. The diagnostic accuracy and specificity for soft tissue calcification of AI were high from 0.727 to 0.926 and from 0.171 to 1.000, whereas the sensitivity for lymph node calcification and sialolith were low at 0.250 and 0.188, respectively. The reading time of AI increased in the GD group (619 to 1049) and decreased in the OMR group (1347 to 1372). In addition, reading scores increased in both groups (GD from 11.4 to 39.8 and OMR from 3.4 to 10.8). Using AI, although the detection sensitivity of sialolith and lymph node calcification was lower than that of carotid artery calcification, the total reading time of the OMR specialists was reduced and the GDs reading accuracy was improved. The AI used in this study helped to improve the diagnostic accuracy of the GD group, who were not familiar with the soft tissue calcification diagnosis, but more data sets are needed to improve the detection performance of the two diseases with low sensitivity of AI.
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