Predicting the risk of dental implant loss using deep learning

作者全名："Huang, Nannan; Liu, Peng; Yan, Youlong; Xu, Ling; Huang, Yuanding; Fu, Gang; Lan, Yiqing; Yang, Sheng; Song, Jinlin; Li, Yuzhou"

作者地址："[Huang, Nannan; Xu, Ling; Yang, Sheng; Li, Yuzhou] Chongqing Med Univ, Stomatol Hosp, Dept Prosthodont, Chongqing, Peoples R China; [Huang, Nannan; Xu, Ling; Huang, Yuanding; Fu, Gang; Lan, Yiqing; Yang, Sheng; Song, Jinlin; Li, Yuzhou] Chongqing Key Lab Oral Dis & Biomed Sci, Chongqing, Peoples R China; [Huang, Nannan; Xu, Ling; Huang, Yuanding; Fu, Gang; Lan, Yiqing; Yang, Sheng; Song, Jinlin; Li, Yuzhou] Chongqing Municipal Key Lab Oral Biomed Engn High, Chongqing, Peoples R China; [Liu, Peng] Chongqing Med Univ, Dept Radiol, Stomatol Hosp, Chongqing, Peoples R China; [Yan, Youlong] Chongqing Med Univ, Stomatol Hosp, Dept Informat Ctr, Chongqing, Peoples R China; [Huang, Yuanding; Fu, Gang] Chongqing Med Univ, Dept Implantol, Stomatol Hosp, Chongqing, Peoples R China"

通信作者："Yang, S; Song, JL; Li, YZ (通讯作者)，Chongqing Med Univ, Stomatol Hosp, Chongqing 401147, Peoples R China."

来源：JOURNAL OF CLINICAL PERIODONTOLOGY

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000822260200001

JCR分区：Q1

影响因子：6.7

年份：2022

卷号：49

期号：9

开始页：872

结束页：883

文献类型：Article

关键词：cone-beam computed tomography; deep learning; dental implant loss; risk prediction model

摘要："Aim To investigate the feasibility of predicting dental implant loss risk with deep learning (DL) based on preoperative cone-beam computed tomography. Materials and Methods Six hundred and three patients who underwent implant surgery (279 high-risk patients who did and 324 low-risk patients who did not experience implant loss within 5 years) between January 2012 and January 2020 were enrolled. Three models, a logistic regression clinical model (CM) based on clinical features, a DL model based on radiography features, and an integrated model (IM) developed by combining CM with DL, were developed to predict the 5-year implant loss risk. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model performance. Time to implant loss was considered for both groups, and Kaplan-Meier curves were created and compared by the log-rank test. Results The IM exhibited the best performance in predicting implant loss risk (AUC = 0.90, 95% confidence interval [CI] 0.84-0.95), followed by the DL model (AUC = 0.87, 95% CI 0.80-0.92) and the CM (AUC = 0.72, 95% CI 0.63-0.79). Conclusions Our study offers preliminary evidence that both the DL model and the IM performed well in predicting implant fate within 5 years and thus may greatly facilitate implant practitioners in assessing preoperative risks."

基金机构："Chongqing Medical University Chongqing Postgraduate Tutor Team Construction Project [dstd201903]; National Natural Science Foundation of China [81771082, 31971282, 82001103, 82171010]; Natural Science Foundation of Chongqing, China [cstc2019jcyj-bshX0005, cstc2019jcyj-msxmX0366, cstc2021jcyj-jqX0028]"

基金资助正文："Chongqing Medical University Chongqing Postgraduate Tutor Team Construction Project, Grant/Award Number: dstd201903; National Natural Science Foundation of China, Grant/Award Numbers: 81771082, 31971282, 82001103, 82171010; Natural Science Foundation of Chongqing, China, Grant/Award Numbers: cstc2019jcyj-bshX0005, cstc2019jcyj-msxmX0366, cstc2021jcyj-jqX0028"