  Stroke is the second most frequent cause of death worldwide with a
considerable economic burden on the health systems. In about 15% of strokes,
atherosclerotic carotid plaques (ACPs) constitute the main etiological factor.
Early detection of ACPs may have a key-role for preventing strokes by managing
the patient a-priory to the occurrence of the damage. ACPs can be detected on
panoramic images. As these are one of the most common images performed for
routine dental practice, they can be used as a source of available data for
computerized methods of automatic detection in order to significantly increase
timely diagnosis of ACPs. Recently, there has been a definite breakthrough in
the field of analysis of medical images due to the use of deep learning based
on neural networks. These methods, however have been barely used in dentistry.
In this study we used the Faster Region-based Convolutional Network (Faster
R-CNN) for deep learning. We aimed to assess the operation of the algorithm on
a small database of 65 panoramic images. Due to a small amount of available
training data, we had to use data augmentation by changing the brightness and
randomly flipping and rotating cropped regions of interest in multiple angles.
Receiver Operating Characteristic (ROC) analysis was performed to calculate the
accuracy of detection. ACP was detected with a sensitivity of 75%, specificity
of 80% and an accuracy of 83%. The ROC analysis showed a significant Area Under
Curve (AUC) difference from 0.5. Our novelty lies in that we have showed the
efficiency of the Faster R-CNN algorithm in detecting ACPs on routine panoramic
images based on a small database. There is a need to further improve the
application of the algorithm to the level of introducing this methodology in
routine dental practice in order to enable us to prevent stroke events.
