Key-point based automated diagnosis for alveolar dehiscence in mandibular incisors using convolutional neural network

• Published in: Biomedical signal processing and control Vol. 85; p. 105082
• Main Authors: Liu, Tianyu, Ye, Yingzhi, Liu, Chengcheng, Chen, Jing, Liu, Yuehua, Xing, Wenyu, Ta, Dean
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.08.2023
• Subjects: Alveolar dehiscence; Cone-beam computed tomography; Convolutional neural network; Key-point based method; Key-point based method; Cone-beam computed tomography; Convolutional neural network; Alveolar dehiscence
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2023.105082

•A Key-point based automated diagnosis method (KPM) for alveolar dehiscence in the anterior teeth using Convolutional Neural Network (CNN) was proposed in this study.•Compared with the classical Binary Classification Method, the proposed KPM showed superior diagnostic performance with an accuracy of 90.2%, a sensitivity of 86.2% and a specificity of 92.6%, respectively,•The results proved that the diagnosis of alveolar dehiscence could be realized using convolutional neural network and the proposed KPM have the advantage of high accuracy and real-time performance.•This study suggests that the key-point based convolutional neural network might have the potential for the diagnosis of alveolar dehiscence in the clinic. The aim of this study was to propose an automated diagnosis method for alveolar dehiscence in the anterior teeth using Convolutional Neural Network (CNN). The Cone-Beam Computed Tomography (CBCT) scanning was performed on 387 orthodontic patients at Shanghai Stomatological Hospital. A total of 1017 mandibular incisors with the largest labiolingual sectional images were obtained from the CBCT data. Among the 1017 incisor images, 371 specimens were diagnosed with alveolar dehiscence. We proposed two strategies of automated diagnosis methods for alveolar dehiscence. The first strategy (referred to as the Binary Classification Method (BCM)) was to take the task directly as a classic binary classification, and five classification networks (ResNet50, ResNet101, VGG16, AlexNet, MobileNet) were tested in this task. The second strategy (referred to as the Key-Point based Method (KPM)) was to use the CNN to search two key points (i.e., the Cement-Enamel Junction (CEJ) and the Alveolar Crest (AC)) firstly and then make a diagnosis according to the distance between the two key points. At the same time, we proposed an image preprocessing method for the approximate location of mandibular incisors and an improved key point selection method to avoid the problems of missed detection. In both CNN strategies, 90% of the mandibular incisor images were assigned to the training dataset, and the rest 10% were assigned to the testing dataset. The BCM showed limited performance in the diagnosis of alveolar dehiscence, with diagnostic accuracy below 70% for all the five classification networks. The KPM showed superior diagnostic performance with an accuracy of 90.2%, a sensitivity of 86.2% and a specificity of 92.6%, respectively, in the testing dataset. The proposed image preprocessing procedures also played an essential role in the diagnosis process, significantly improving the diagnostic accuracy by 7.0% in KPM. The results proved that the diagnosis of alveolar dehiscence could be realized using convolutional neural network and the proposed KPM have the advantage of high accuracy and real-time performance. This study suggests that the key-point based convolutional neural network might have the potential for the diagnosis of alveolar dehiscence in the clinic.