Deep Learning-Based Detection of Carotid Artery Atheromas in Panoramic Radiographs

Radiographically visible carotid artery calcifications are typically seen at the level of the C3-C4 cervical vertebrae and can be detected on panoramic dental radiographs. Their early identification is clinically relevant, as they represent a potential marker for increased risk of stroke. In this context, the present study proposes a deep learning method for automatic identification of carotid atheromas using MobileNetV2. From a publicly available dataset, 378 region-of-interest (ROI) images (640 × 320) were prepared and split into train/val/test = 264/57/57 with class counts train 157/107, val 34/23, test 34/23 (negatives/positives). Images underwent standardized preprocessing and on-the-fly augmentation; training used a two-stage scheme (backbone frozen "head" training followed by partial fine-tuning of the top layers), class-weighting, dropout = 0.3, batch normalization (BN) head, early stopping, and partial unfreezing (~70% of the backbone). The decision threshold was selected on validation by Youden's J. On the independent test set, the model achieved an accuracy (<i>ACC</i>) of 94.7%, sensitivity (<i>SEN</i>) of 95,7%, specificity (<i>SPE</i>) of 0.941, area under the receiver operating characteristic curve (AUC) 0.963, and area under the precision-recall curve (AUPRC) of 0.968. Using a sensitivity-targeted threshold (<i>SEN</i> ≈ 0.80), the model yielded <i>ACC</i> = 91.2%, <i>SEN</i> = 82.6%, and <i>SPE</i> = 97.1%. These results support panoramic radiographs as an opportunistic screening modality for systemic vascular risk and highlight the potential of artificial intelligence (AI)-assisted methods to enable earlier identification within preventive healthcare.