Sample size sufficiency analysis.

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.</div

The probabilities and quantities of prediction CAC or “Clean” on three types of patients based on the performance of a single side.

The cells in the table consist of the probabilities and the patient numbers are presented in italic in parentheses.</p

MapGrad of calcified corners.

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.</div

The region of interest is located near the C3-C4 spinals.

Therefore, we cut out each of the lower corners of the panoramic image in 500 X 500 pixels size (depicted with light blue rectangles). This size ensured that the corners encompassed the calcification.</p

Gradient-weighted class activation mapping (Grad-CAM) of a correct prediction of CAC and “clean” classes.

The colors indicate the region that has the greatest impact on the CAC prediction. The color range is presented below (ranging from red, representing the regions with the greatest impact on the CAC prediction, to blue, representing the regions with the lowest impact). It can be noticed that the classifier indeed focused on the region of the calcification signs. Other Grad-CAM of CAC and clean predications can be found in the S3 Fig and S4 Fig.</p

Sample size assessment: F1 score as a function of the sample size, based on random subsampling of 40%-80% of the original data.

Sample size assessment: F1 score as a function of the sample size, based on random subsampling of 40%-80% of the original data.</p

Summary—performances ‘per corner ‘and ‘per patient’.

Summary—performances ‘per corner ‘and ‘per patient’.</p

Recall-Precision (RP) curves of all the cross-validation folds (each fold is shown here in a different curve); the random classifier is depicted with a dashed line.

Recall-Precision (RP) curves of all the cross-validation folds (each fold is shown here in a different curve); the random classifier is depicted with a dashed line.</p

Probability calculations of the three patient types.

Probability calculations of the three patient types.</p

Figures E and F are panoramic corners with Triticeous Cartilage calcification.

The blue arrows point toward the calcification. Figures G and H are clean normal corners. (TIF)</p