ArticlePLOS digital health2023
Deep convolution neural network for screening carotid calcification in dental panoramic radiographs.
Article in PLOS digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 3 of them syntheses that pooled it.
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Who cites it
12 citing papers in PubMed, 3 syntheses or guidelines pooled it, 14 citations in OpenAlex.
- Artificial Intelligence-assisted Diagnosis of Carotid Artery Calcifications on Panoramic Radiographs: A Meta-analysis.International dental journal · 2026Pooled it
- Diagnostic Performance of Deep Learning and Radiomics in Extracranial Carotid Plaque Detection: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- Detection of carotid artery calcifications using artificial intelligence in dental radiographs: a systematic review and meta-analysis.BMC medical imaging · 2025Pooled it
- Accuracy of deep learning in the detection of carotid calcifications on cone-beam computed tomography: A systematic review.Imaging science in dentistry · 2026Review
- 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
- Evaluation of deep learning-based segmentation models for carotid artery calcification detection in panoramic radiographs.Oral radiology · 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
- Dental Panoramic Radiographs as Opportunistic Screening for Carotid Calcifications: A Case-Based Review.Cureus · 2025Article
- Applying deep learning techniques to identify tonsilloliths in panoramic radiography.Scientific reports · 2025Article
- CACSNet for automatic robust classification and segmentation of carotid artery calcification on panoramic radiographs using a cascaded deep learning network.Scientific reports · 2024Article
- Automatic foot ulcer segmentation using conditional generative adversarial network (AFSegGAN): A wound management system.PLOS digital health · 2023Article
Corrections and comments
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Authors and funding
12 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ischemic stroke, a leading global cause of death and disability, is commonly caused by carotid arteries atherosclerosis. Carotid artery calcification (CAC) is a well-known marker of atherosclerosis. Such calcifications are classically detected by ultrasound screening. In recent years it was shown that these calcifications can also be inferred from routine panoramic dental radiographs. In this work, we focused on panoramic dental radiographs taken from 500 patients, manually labelling each of the patients' sides (each radiograph was treated as two sides), which were used to develop an artificial intelligence (AI)-based algorithm to automatically detect carotid calcifications. The algorithm uses deep learning convolutional neural networks (CNN), with transfer learning (TL) approach that achieved true labels for each corner, and reached a sensitivity (recall) of 0.82 and a specificity of 0.97 for individual arteries, and a recall of 0.87 and specificity of 0.97 for individual patients. Applying and integrating the algorithm in healthcare units and dental clinics has the potential of reducing stroke events and their mortality and morbidity consequences.
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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.