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 415. 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.