Abstract
BackgroundPosteroanterior and lateral cephalogram have been widely used for evaluating the necessity of orthognathic surgery. The purpose of this study was to develop a deep learning network to automatically predict the need for orthodontic surgery using cephalogram.MethodsThe cephalograms of 840 patients (Class ll: 244, Class lll: 447, Facial asymmetry: 149) complaining about dentofacial dysmorphosis and/or a malocclusion were included. Patients who did not require orthognathic surgery were classified as Group I (622 patients—Class ll: 221, Class lll: 312, Facial asymmetry: 89). Group II (218 patients—Class ll: 23, Class lll: 135, Facial asymmetry: 60) was set for cases requiring surgery. A dataset was extracted using random sampling and was composed of training, validation, and test sets. The ratio of the sets was 4:1:5. PyTorch was used as the framework for the experiment.ResultsSubsequently, 394 out of a total of 413 test data were properly classified. The accuracy, sensitivity, and specificity were 0.954, 0.844, and 0.993, respectively.ConclusionIt was found that a convolutional neural network can determine the need for orthognathic surgery with relative accuracy when using cephalogram.
Highlights
Posteroanterior and lateral cephalogram have been widely used for evaluating the necessity of orthog‐ nathic surgery
Deep learning was applied to the diagnosis of dentofacial dysmorphosis using photographs of the subjects [11]
Datasets In this study, transverse and longitudinal cephalograms of 840 patients (Class ll: 244, Class lll: 447, Facial asymmetry: 149), who visited Daejeon Dental Hospital, Wonkwang University between January 2007 and December 2019 complaining about dentofacial dysmorphosis and/ or a malocclusion, were used for the training and testing of a deep learning model (461 males and 379 females with a mean age of 23.2 years and an age range of 19–29 years, SD: 3.15)
Summary
Posteroanterior and lateral cephalogram have been widely used for evaluating the necessity of orthog‐ nathic surgery. The purpose of this study was to develop a deep learning network to automatically predict the need for orthodontic surgery using cephalogram. A maxillofacial skeletal analysis is an important part of diagnosis and treatment planning [13], and it is used to assess the vertical, lateral, and anteroposterior positions of the jaws using posteroanterior (PA) and lateral (Lat) cephalogram. Accurate diagnosis has been based on proper landmark identification by cephalogram and it is essential to a successful treatment [13, 14]. The application of deep learning algorithms to cephalometric analysis has been studied, and many approaches have focused on the detection of cephalometric landmarks [15, 16]. A CNN-based deep learning system was proposed for skeletal classification without the need for landmark detection steps [17]. The network exhibited > 90% sensitivity, specificity, and accuracy for vertical and sagittal skeletal diagnosis
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
