Periodontitis Auxiliary Diagnosis Based on Deep Learning and Oral Dental X-ray Images

Abstract The alveolar bone edge line is an important basis for the dentist to diagnose periodontitis. In view of the problem of difficulty in accurately identifying the marginal lines of alveolar bone in oral X-ray images, a periodontitis-assisted diagnosis method based deep learning is proposed. In the proposed method, the image segmentation model (PSPNet, U-Net, and Dense U-Net) was applied to segmenting oral dental X-ray images. PSPNet, U-Net, and Dense U-Net are prominent image segmentation models that have gained traction in the field of medical X-ray image segmentation. These models have demonstrated their effectiveness in accurately segmenting regions of interest in medical images, and have been extensively employed in various medical imaging segmentations. Based on this, the Canny algorithm was used to extract the alveolar bone edge line from the segmentation image. According to the experiment results, the Dense U-Net achieves the best oral dental X-ray image segmentation results. The Dice, Jc, HD95, and ASD of Dense U-Net for the segmentation of 140 periodontitis oral dental X-ray images in the test set are 95.21, 90.85, 34.53, and 34.67%, respectively. Compared with PSPNet and U-Net, the oral dental X-ray image segmentation results of the Dense U-Net model have significant advantages in four metrics: Dice, Jc, HD95, and ASD. Moreover, the alveolar bone edge lines extracted by the Canny algorithm from the oral dental X-ray images segmented by Dense U-Net are closer to the manually labeled results. Experimental results fully demonstrate the effectiveness of using deep learning techniques to extract the alveolar bone edge line. This study has provided a useful exploration in assisting dentists to better diagnose periodontitis.