基于口内数码照图像深度学习的牙周病早期筛查研究

doi:10.13701/j.cnki.kqyxyj.2022.11.018

口腔医学研究 ›› 2022, Vol. 38 ›› Issue (11): 1092-1095.DOI: 10.13701/j.cnki.kqyxyj.2022.11.018

基于口内数码照图像深度学习的牙周病早期筛查研究

朱红标^1,2, 刘强冬^1,2, 曾子强^3,4, 娄伟明^3,4, 戴芳^1,2, 吴婧婷^1,2, 邓恬^1,2, 邓立彬^2,3,4, 宋莉^1,2*

1.南昌大学第二附属医院口腔医学诊疗中心江西南昌 330006;
2.南昌大学牙周病研究所江西南昌 330006;
3.南昌大学公共卫生学院江西南昌 330006;
4.江西省预防医学重点实验室江西南昌 330006

收稿日期:2022-04-26 出版日期:2022-11-25 发布日期:2022-11-22
通讯作者: *宋莉,E-mail:ndefy91009@ncu.edu.cn
作者简介:朱红标(1994~ ),男,江西九江人,硕士,医师,主要从事牙周病学工作。
基金资助:
江西省科技厅重点研发一般项目(编号:20212BBG73006)南昌大学交叉基金项目(编号:9166-27060003-ZD04)院内临床研究重大项目(编号:2021efyA04)

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

ZHU Hongbiao^1,2, LIU Qiangdong^1,2, ZENG Ziqiang^3,4, LOU Weiming^3,4, DAI Fang^1,2, WU Jingting^1,2, DENG Tian^1,2, DENG Libin^2,3,4, SONG Li^1,2*

1. Center of Stomatology, The Second Affiliated Hospital of Nanchang University, Nanchang 330006, China;
2. Institute of Periodontology of Nanchang University, Nanchang 330006, China;
3. School of Public Health, Nanchang University, Nanchang 330006, China;
4. Jiangxi Provincial Key Laboratory of Preventive Medicine, Nanchang University, Nanchang 330006, China

Received:2022-04-26 Online:2022-11-25 Published:2022-11-22

摘要/Abstract

摘要： 目的: 基于卷积神经网络(convolutional neural network,CNN)的深度学习技术构建人工智能(artificial intelligence,AI)牙周病早筛模型,辅助非牙周医生对牙周病进行早期筛查。方法: 收集南昌大学第二附属医院口腔医学诊疗中心就诊的牙周非健康人群以及牙周健康人群的口内数码照和临床资料。基于VGG-16结构对口内数码照图像进行训练和测试,建立口腔九宫格、正位咬合、正位咬合(剔除无效背景)3种训练集模型。结果: 共收集到578位研究对象的3869张口内数码照图像,其中牙周健康图像2230张,牙周非健康图像1639张。采用VGG-16结构建立3种训练集模型,对九宫格口内数码照、正位咬合口内数码照、正位咬合(剔除无效背景)口内数码照预测的准确度分别为66.62%、64.66%、77.44%,曲线下面积(area under curve,AUC)值分别为0.651、0.767、0.784。结论: 本研究构建的VGG-16模型能有效通过对口内数码照图像识别,辅助非牙周医生对牙周病进行早筛。

关键词: 卷积神经网络, 牙周病, 深度学习, 人工智能

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

朱红标, 刘强冬, 曾子强, 娄伟明, 戴芳, 吴婧婷, 邓恬, 邓立彬, 宋莉. 基于口内数码照图像深度学习的牙周病早期筛查研究[J]. 口腔医学研究, 2022, 38(11): 1092-1095.

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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基于口内数码照图像深度学习的牙周病早期筛查研究

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

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