Atherosclerotic carotid plaques on panoramic imaging: an automatic detection using deep learning with small dataset

TL;DR

This study demonstrates the effectiveness of a deep learning model, Faster R-CNN, in automatically detecting atherosclerotic carotid plaques on panoramic dental images using a small dataset, potentially aiding stroke prevention.

Contribution

First application of Faster R-CNN for ACP detection on panoramic images with limited data, showing promising accuracy and highlighting potential for routine dental screening.

Findings

Achieved 83% accuracy in ACP detection

Sensitivity of 75% and specificity of 80% in identifying plaques

Significant ROC AUC difference from random chance

Abstract

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.