Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images

Ziyang Hu; Dantong Cao; Yanni Hu; Baixin Wang; Yifan Zhang; Rong Tang; Jia Zhuang; Antian Gao; Ying Chen; Zitong Lin

doi:10.1186/s12903-022-02422-9

Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images

BMC Oral Health. 2022 Sep 5;22(1):382. doi: 10.1186/s12903-022-02422-9.

Authors

Ziyang Hu^#^{1

2}, Dantong Cao^#¹, Yanni Hu¹, Baixin Wang³, Yifan Zhang³, Rong Tang¹, Jia Zhuang¹, Antian Gao¹, Ying Chen⁴, Zitong Lin⁵

Affiliations

¹ Department of Dentomaxillofacial Radiology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Zhong Yang Road 30, Nanjing City, 210008, Jiangsu, People's Republic of China.
² Department of Stomatology, Guangdong Medical University Affiliated Longhua Central Hospital, Shenzhen, China.
³ School of Electronic Science and Engineering, Nanjing University, Nanjing, China.
⁴ School of Electronic Science and Engineering, Nanjing University, Nanjing, China. yingchen@nju.edu.cn.
⁵ Department of Dentomaxillofacial Radiology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Zhong Yang Road 30, Nanjing City, 210008, Jiangsu, People's Republic of China. linzitong_710@163.com.

^# Contributed equally.

Abstract

Objectives: Evaluating the diagnostic efficiency of deep learning models to diagnose vertical root fracture in vivo on cone-beam CT (CBCT) images.

Materials and methods: The CBCT images of 276 teeth (138 VRF teeth and 138 non-VRF teeth) were enrolled and analyzed retrospectively. The diagnostic results of these teeth were confirmed by two chief radiologists. There were two experimental groups: auto-selection group and manual selection group. A total of 552 regions of interest of teeth were cropped in manual selection group and 1118 regions of interest of teeth were cropped in auto-selection group. Three deep learning networks (ResNet50, VGG19 and DenseNet169) were used for diagnosis (3:1 for training and testing). The diagnostic efficiencies (accuracy, sensitivity, specificity, and area under the curve (AUC)) of three networks were calculated in two experiment groups. Meanwhile, 552 teeth images in manual selection group were diagnosed by a radiologist. The diagnostic efficiencies of the three deep learning network models in two experiment groups and the radiologist were calculated.

Results: In manual selection group, ResNet50 presented highest accuracy and sensitivity for diagnosing VRF teeth. The accuracy, sensitivity, specificity and AUC was 97.8%, 97.0%, 98.5%, and 0.99, the radiologist presented accuracy, sensitivity, and specificity as 95.3%, 96.4 and 94.2%. In auto-selection group, ResNet50 presented highest accuracy and sensitivity for diagnosing VRF teeth, the accuracy, sensitivity, specificity and AUC was 91.4%, 92.1%, 90.7% and 0.96.

Conclusion: In manual selection group, ResNet50 presented higher diagnostic efficiency in diagnosis of in vivo VRF teeth than VGG19, DensenNet169 and radiologist with 2 years of experience. In auto-selection group, Resnet50 also presented higher diagnostic efficiency in diagnosis of in vivo VRF teeth than VGG19 and DensenNet169. This makes it a promising auxiliary diagnostic technique to screen for VRF teeth.

Keywords: Artificial intelligence, cone-beam computed tomography, deep learning; Neural networks (computer); Root fractures.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Cone-Beam Computed Tomography / methods
Deep Learning*
Humans
Retrospective Studies
Tooth Fractures* / diagnostic imaging
Tooth Root / diagnostic imaging