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Deep Learning-Based Detection of Carotid Artery Atheromas in Panoramic Radiographs
Thais Martins Jajah Carlos1, Márcio José da Cunha1, Aniel Silva Morais1
1Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38408-100, Brazil.
Bioengineering (Basel, Switzerland)
|January 28, 2026
Summary
Panoramic dental radiographs can detect carotid artery calcifications, a stroke risk marker. A deep learning model (MobileNetV2) achieved high accuracy in identifying these calcifications, supporting opportunistic screening for vascular risk.
Area of Science:
- Radiology
- Artificial Intelligence
- Cardiovascular Health
Background:
- Carotid artery calcifications, often visible on panoramic dental radiographs at C3-C4 vertebrae level, are linked to increased stroke risk.
- Early identification of these calcifications is crucial for assessing systemic vascular risk.
Purpose of the Study:
- To develop and evaluate a deep learning method using MobileNetV2 for automatic identification of carotid atheromas on panoramic radiographs.
- To assess the potential of AI-assisted panoramic radiography for opportunistic stroke risk screening.
Main Methods:
- A dataset of 378 region-of-interest (ROI) images was preprocessed and augmented.
- A two-stage deep learning training scheme with class-weighting and early stopping was employed.
- The MobileNetV2 model was trained and validated, with performance assessed on an independent test set.
Main Results:
- The AI model achieved high performance metrics on the test set: 94.7% accuracy, 95.7% sensitivity, 94.1% specificity, and an AUC of 0.963.
- A sensitivity-targeted threshold (82.6%) yielded 91.2% accuracy and 97.1% specificity.
- The model demonstrated strong performance in identifying carotid atheromas.
Conclusions:
- Panoramic radiographs can serve as an opportunistic tool for screening systemic vascular risk.
- AI-assisted methods show significant potential for enabling earlier identification of carotid atheromas within preventive healthcare strategies.
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