Deep learning based prediction of necessity for orthognathic surgery of skeletal malocclusion using cephalogram in Korean individuals

Abstract

Background Posteroanterior 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. Methods The 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. Results Subsequently, 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. Conclusion It was found that a convolutional neural network can determine the need for orthognathic surgery with relative accuracy when using cephalogram.

Development, Application, and Performance of Artificial Intelligence in Cephalometric Landmark Identification and Diagnosis: A Systematic Review

“…Overall, the AI algorithm architecture (CNN, ANN) showed promising results in cephalometric landmark detection and analysis compared with human subjects, as shown in Table 1 . Moreover, 19 articles [ 5 , 6 , 8 , 9 , 10 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 ] were excluded from this review, as shown in Supplementary Table S1 .…”

Artificial intelligence in orthodontics: Where are we now? A scoping review

“…and 64.8%. 20 Similarly, Shin et al (2021) 21 and Lin et al (2021) 22 concluded, with an accuracy of 95.4% 21 and 87.4%, 22 respectively, that a deep learning program can be used to determine the need for orthognathic surgery. The latter publication also determined that it is possible to predict the future need for surgery to correct sagittal skeletal discrepancy in patients with repaired unilateral cleft lip and palate at the age of 6 years.…”

Three-Dimensional Postoperative Results Prediction for Orthognathic Surgery through Deep Learning-Based Alignment Network

“…Deep learning has developed rapidly in recent years, making it possible to automatically extract information in the medical field from diagnoses using medical imaging and pattern analysis [ 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. Deep neural networks (DNNs), a type of deep learning, have been widely applied to medical images because of their high performance in detection, classification, and segmentation [ 16 , 17 , 18 , 19 , 20 ]. It can reduce the labor of experts while detecting image information that may be missed by humans.…”