Identifying Medically-compromised Patients with Periodontitis-Associated Cardiovascular Diseases Using Convolutional Neural Network-facilitated Multilabel Classification of Panoramic Radiographs

摘要

The bidirectional relationship between periodontitis and atherosclerotic cardiovascular disease (ASCVD) has been demonstrated in cohort studies. In this study, we applied computer vision (CV)-based algorithms and convolutional neural networks (CNNs) to identify periodontitis-associated ASCVD through panoramic radiographs. 432 radiographs were balancedly collected at a medical center, from patients with both ASCVD and periodontitis, with only periodontitis, with only ASCVD, and without either ASCVD or periodontitis. The panoramic radiographs were first segmented with U-Net as original images without any segmentation, images with only the maxilla, images without teeth, images with only the mandible, and images with only teeth. Then, CV-based algorithms for average brightness histogram analysis and CNN-based multi-label classification were parallelly used to recognize two labels, ASCVD and periodontitis. The multi-label classification task was executed with hyperparemeters including adam and binary cross-entropy. Compared to average brightness analysis, the accuracy of multi-label classification for the two labels was satisfying, with the F2 score and recall being 0.90 and 0.93 for original images, respectively. In conclusion, multi-label classification incorporating CNN could better recognize not only periodontitis but ASCVD. Moreover, maxilla played a key role in providing information for classification, which was in line with domain knowledge regarding how ASCVD may involve the head and neck area.