中国科学院机构知识库网格
Chinese Academy of Sciences Institutional Repositories Grid
首页 机构 成果 学者
  
  1. CAS IR Grid
  2. 计算技术研究所
  3. 中国科学院计算技术研究所
  4. 中国科学院计算技术研究所期刊论文
A deep learning model using convolutional neural networks for caries detection and recognition with endoscopes

文献类型：期刊论文

作者Zang, Xiaoyi4,5; Luo, Chunlong2,3; Qiao, Bo4,5; Jin, Nenghao4,5; Zhao, Yi3; Zhang, Haizhong1,4
刊名ANNALS OF TRANSLATIONAL MEDICINE
出版日期2022-12-19
页码11
关键词Artificial intelligence (AI) caries lesions endoscopes deep learning
ISSN号2305-5839
DOI10.21037/atm-22-5816
英文摘要Background: Caries are common, especially in economically undeveloped countries with limited access to medical resources. Sometimes patient cannot even realize that they have oral problems until they feel obvious pain. Deep convolutional neural networks (CNNs) have been widely adopted for medical image analysis and management and have yielded some progress in stomatology while the endoscopes are cheap and easily used in daily life for families or other non-medical situations. Therefore, we created a deep learning model to detect and recognize caries using endoscopic images. Methods: We used 194 images of non-caries and 1,059 images of permanent molar and premolar caries to build a classification and a segmentation model in patients of endoscope images from the Department of Stomatology of People's Liberation Army General Hospital (PLAGH). A classification model combined with an end-to-end semantic segmentation model, DeepLabv3+ was used for segmenting the caries, then we evaluated with a 5-fold cross-validation protocol whereby each fold was used once. Results: In the classification model, the mean area under the curve (AUC) [90% confidence interval (CI)] was 0.9897 (0.9821-0.9956) (P<0.01) In the segmentation model, the mean accuracy was 0.9843 (0.9820-0.9871), the recall was 0.6996 (0.6810-0.7194), the specificity was 0.9943 (0.9937-0.9954), the Dice coefficient was 0.7099 (0.6948-0.7343), and the intersection over union (IoU) was 0.5779 (0.5646-0.6006). Conclusions: We used a deep learning model to monitor caries and encourage their early diagnosis and treatment.
资助项目National Key R&D Program of China ; [2020YFC2008900]
WOS研究方向Oncology ; Research & Experimental Medicine
语种英语
WOS记录号WOS:000905672900001
出版者AME PUBLISHING COMPANY
源URL[http://119.78.100.204/handle/2XEOYT63/20143]  
专题中国科学院计算技术研究所期刊论文
通讯作者Zhang, Haizhong
作者单位1.Chinese Peoples Liberat Army Gen Hosp, Med Ctr 1, Dept Stomatol, 28 Fuxing Rd, Beijing 100853, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Res Ctr Ubiquitous Comp Syst, Beijing, Peoples R China
4.Chinese Peoples Liberat Army Gen Hosp, Med Ctr 1, Dept Stomatol, Beijing, Peoples R China
5.Med Sch Chinese PLA, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zang, Xiaoyi,Luo, Chunlong,Qiao, Bo,et al. A deep learning model using convolutional neural networks for caries detection and recognition with endoscopes[J]. ANNALS OF TRANSLATIONAL MEDICINE,2022:11.
APA Zang, Xiaoyi,Luo, Chunlong,Qiao, Bo,Jin, Nenghao,Zhao, Yi,&Zhang, Haizhong.(2022).A deep learning model using convolutional neural networks for caries detection and recognition with endoscopes.ANNALS OF TRANSLATIONAL MEDICINE,11.
MLA Zang, Xiaoyi,et al."A deep learning model using convolutional neural networks for caries detection and recognition with endoscopes".ANNALS OF TRANSLATIONAL MEDICINE (2022):11.

入库方式： OAI收割

来源：计算技术研究所

浏览0
下载0
收藏0
其他版本

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。