Evaluation of Deep Learning-Based Segmentation Models for Carotid Artery Calcification Detection in Panoramic Radiographs

Abstract

Objectives: The primary objective of this study is to evaluate the effectiveness of artificial intelligence-assisted segmentation methods in detecting carotid artery calcification (CAC) in panoramic radiographs and to compare the performance of different YOLO models: YOLOv5x-seg, YOLOv8x-seg, and YOLOv11x-seg. Additionally, the study aims to investigate the association between patient gender and the presence of CAC, as part of a broader epidemiological analysis.

Methods: In this study, 30,883 panoramic radiographs were scanned. Annotations were made on 652 radiographs exhibiting features consistent with CAC, totaling 1,086 annotations. Deep learning-based analysis was conducted using three distinct YOLO segmentation models. The performance of these models was assessed using metrics such as precision, accuracy, and F1 score.

Results: The YOLOv5x-seg model exhibited a balanced performance with a precision, sensitivity, and F1 score of 84.62% each. The YOLOv8x-seg model demonstrated higher sensitivity at 88.46%, albeit with a slightly higher false positive rate, evidenced by a precision of 78.63%. The YOLOv11x-seg model achieved the highest precision at 93.41%, an F1 score of 87.18%, and a sensitivity of 81.73%.

Conclusions: AI-based segmentation models utilizing YOLO algorithms can be considered reliable tools for detecting CAC in panoramic radiographs. These models display promising performance appropriate for clinical applications; however, further research with larger and more diverse datasets is required to verify their generalizability and efficacy.