Early Screening of Periodontal Disease Based on Deep Learning of Intra Oral Digital Image

doi:10.13701/j.cnki.kqyxyj.2022.11.018

Abstract

Abstract: Objective: To construct an artificial intelligence (AI) early screening model of periodontal disease based on convolutional neural network (CNN) deep learning technology, and to assist non-periodontal doctors in early screening of periodontal disease. Methods: The oral digital photos and clinical data of periodontal non-healthy people and periodontal healthy people were collected from the Second Affiliated Hospital of Nanchang University. Vgg-16 was used to train and test intra oral digital images. Three training models, i.e. nine grid mouth, orthotopic occlusal, and orthotopic occlusal excluding invalid background, were established. Results: A total of 3869 oral digital images of 578 subjects were collected, including 2230 periodontal healthy images and 1639 periodontal unhealthy images. Vgg-16 was used to establish three kinds of training set models. The accuracy of prediction of digital image in nine grid mouth, digital image in orthotopic occlusal mouth, and digital image in orthotopic occlusal mouth excluding invalid background were 66.62%, 64.66%, and 77.44%, respectively. AUC values were 0.651, 0.767, and 0.784, respectively. Conclusion: The VGG-16 model constructed in this study can effectively assist non-periodontal doctors in early screening of periodontal disease through intra-oral digital image recognition.

Key words: convolutional neural network, periodontal disease, deep learning, artificial intelligence

ZHU Hongbiao, LIU Qiangdong, ZENG Ziqiang, LOU Weiming, DAI Fang, WU Jingting, DENG Tian, DENG Libin, SONG Li. Early Screening of Periodontal Disease Based on Deep Learning of Intra Oral Digital Image[J]. Journal of Oral Science Research, 2022, 38(11): 1092-1095.

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