Deep Convolutional Neural Networks Based Analysis of Cephalometric Radiographs for Differential Diagnosis of Orthognathic Surgery Indications
Source abstract
Abstract
The aim of this study was to evaluate the deep convolutional neural networks (DCNNs) based on analysis of cephalometric radiographs for the differential diagnosis of the indications of orthognathic surgery. Among the DCNNs, Modified-Alexnet, MobileNet, and Resnet50 were used, and the accuracy of the models was evaluated by performing 4-fold cross validation. Additionally, gradient-weighted class activation mapping (Grad-CAM) was used to perform visualized interpretation to determine which region affected the DCNNs’ class classification. The prediction accuracy of the models was 96.4% for Modified-Alexnet, 95.4% for MobileNet, and 95.6% for Resnet50. According to the Grad-CAM analysis, the most influential regions for the DCNNs’ class classification were the maxillary and mandibular teeth, mandible, and mandibular symphysis. This study suggests that DCNNs-based analysis of cephalometric radiograph images can be successfully applied for differential diagnosis of the indications of orthognathic surgery.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Dental Radiography and Imaging · Medical Imaging and Analysis · Artificial Intelligence in Healthcare and Education
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Biomedical Engineering, College of Medicine, Seoul National University, Seoul 03080, Korea
Department of Clinical Dentistry, College of Medicine, Korea University, Seoul 02841, Korea
Department of Prosthodontics, Korea University Ansan Hospital, Gyung-gi do 15355, Korea
Department of Prosthodontics, Korea University Anam Hospital, Seoul 02841, Korea
Department of Oral and Maxillofacial Surgery, Korea University Ansan Hospital, Gyung-gi do 15355, Korea
Department of Orthodontics, Korea University Guro Hospital, Seoul 08308, Korea
Department of Orthodontics, Korea University Ansan Hospital, Gyung-gi do 15355, Korea
References (33)
Show source-provided references
1. Proffit, W.R., Fields, H.W., and Sarver, D.M. (2013). Contemporary Orthodontics, Mosby. [5th ed.].
2. Virtual surgical planning: Balancing esthetics, practicality, and anticipated stability in a complex Class III patient10.1016/j.ajodo.2018.06.021 ↗
3. Anthropometric Facial Changes After Orthognathic Surgery and Their Relation With Oral Health Related Quality of Life10.1097/SCS.0000000000005214 ↗
4. Orthognathic Surgery and Orthodontics: Inadequate Planning Leading to Complications or Unfavorable Results10.1016/j.coms.2019.08.008 ↗
5. The orthodontic examination
6. Evaluation of two-dimensional lateral cephalometric radiographs and three-dimensional cone beam computed tomography superimpositions: A comparative study10.1016/j.ijom.2018.10.002 ↗
7. Diagnosis and treatment planning of orthodontic patients with 3-dimensional dentofacial records10.1016/j.ajodo.2016.10.037 ↗
8. Automated 2-D cephalometric analysis on X-ray images by a model-based approach10.1109/TBME.2006.876638 ↗
9. Effect of landmark identification on cephalometric measurements: Guidelines for cephalometric analyses10.1034/j.1600-0544.2000.030106.x ↗
10. Variation of the sella-nasion plane and its effect on SNA and SNB
11. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning10.1109/TMI.2016.2528162 ↗
12. A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals10.1016/j.jelekin.2018.07.005 ↗
13. NetVLAD: CNN Architecture for Weakly Supervised Place Recognition10.1109/TPAMI.2017.2711011 ↗
14. Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning
15. Deep Convolution Neural Network (DCNN) Multiplane Approach to Synthetic CT Generation From MR images-Application in Brain Proton Therapy10.1016/j.ijrobp.2019.06.2535 ↗
16. Runtime Programmable and Memory Bandwidth Optimized FPGA-Based Coprocessor for Deep Convolutional Neural Network10.1109/TNNLS.2018.2815085 ↗
17. He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep residual learning for image recognition. arXiv.10.1109/CVPR.2016.90 ↗
18. History and application of artificial neural networks in dentistry10.4103/ejd.ejd_325_18 ↗
19. Deep learning for healthcare: Review, opportunities and challenges10.1093/bib/bbx044 ↗
20. Detection and diagnosis of dental caries using a deep learning-based convolutional neural network algorithm10.1016/j.jdent.2018.07.015 ↗
21. Evaluation and Comparison of Anatomical Landmark Detection Methods for Cephalometric X-Ray Images: A Grand Challenge10.1109/TMI.2015.2412951 ↗
22. Cheng, E., Chen, J., Yang, J., Deng, H., Wu, Y., Megalooikonomou, V., Gable, B., and Ling, H. (September, January 30). Automatic Dent-landmark detection in 3-D CBCT dental volumes. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Boston, MA, USA.
23. New approach for the diagnosis of extractions with neural network machine learning10.1016/j.ajodo.2015.07.030 ↗
24. Orthodontic Treatment Planning based on Artificial Neural Networks10.1038/s41598-018-38439-w ↗
25. Imagenet classification with deep convolutional neural networks
26. Alom, M.Z., Taha, T.M., Yakopcic, C., Westberg, S., Sidike, P., Nasrin, M.S., Esesn, B.C., Awwal, A.A., and Asari, V.K. (2018). The history began from alexnet: A comprehensive survey on deep learning approaches. arXiv.
27. Bottou, L. (2010, January 22–27). Large-scale machine learning with stochastic gradient descent. Proceedings of the COMPSTAT’2010, Paris, France.10.1007/978-3-7908-2604-3_16 ↗
28. A K-fold Averaging Cross-validation Procedure10.1080/10485252.2015.1010532 ↗
29. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2016). Grad-CAM: Visual explanations from deep networks via gradient-based localization. arXiv.10.1109/ICCV.2017.74 ↗
30. Lee, K.-S., Jung, S.-K., Ryu, J.-J., Shin, S.-W., and Choi, J. (2020). Evaluation of Transfer Learning with Deep Convolutional Neural Networks for Screening Osteoporosis in Dental Panoramic Radiographs. J. Clin. Med., 9.10.3390/jcm9020392 ↗
31. Automated identification of cephalometric landmarks: Part 2-Might it be better than human?10.2319/022019-129.1 ↗
32. Mobilenet convolutional neural networks and support vector machines for palmprint recognition10.1016/j.procs.2019.08.147 ↗
33. Artificial Intelligent Model With Neural Network Machine Learning for the Diagnosis of Orthognathic Surgery10.1097/SCS.0000000000005650 ↗