Identifying Medically-compromised Patients with Periodontitis-Associated Cardiovascular Diseases Using Convolutional Neural Network-facilitated Multilabel Classification of Panoramic Radiographs
Journal
2021 International Conference on Applied Artificial Intelligence, ICAPAI 2021
Date Issued
2021
Author(s)
Abstract
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. ? 2021 IEEE.
Subjects
Atherosclerotic cardiovascular disease
convolutional neural network
multi-label classification
panoramic radiograph
periodontitis
Cardiology
Convolution
Convolutional neural networks
Diseases
Image segmentation
Luminance
Radiography
Cardio-vascular disease
Cohort studies
Domain knowledge
Histogram analysis
Multi label classification
Multi-label classifications
Original images
Panoramic radiograph
Classification (of information)
SDGs
Type
conference paper