Safety System Research Group, Korea Institute of Industrial Technology (KITECH), Gyeongsan, Korea.
School of Electronics Engineering College of IT Engineering, Kyungpook National University, Daegu, Korea.
BMC Oral Health. 2021 Mar 18;21(1):130. doi: 10.1186/s12903-021-01513-3.
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.
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.
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.
It was found that a convolutional neural network can determine the need for orthognathic surgery with relative accuracy when using cephalogram.
前后位和侧位头颅侧位片已广泛用于评估正颌手术的必要性。本研究旨在开发一种深度学习网络，通过头颅侧位片自动预测正畸手术的必要性。
纳入 840 名因牙颌面畸形和/或错牙合抱怨就诊的患者的头颅侧位片（Ⅱ类：244 例，Ⅲ类：447 例，面部不对称：149 例）。未行正颌手术的患者被分为 I 组（622 例-Ⅱ类：221 例，Ⅲ类：312 例，面部不对称：89 例）。Ⅱ组（218 例-Ⅱ类：23 例，Ⅲ类：135 例，面部不对称：60 例）为需要手术的病例。通过随机抽样提取数据集，由训练集、验证集和测试集组成。数据集的比例为 4:1:5。实验采用 PyTorch 作为框架。
在总共 413 个测试数据中，有 394 个被正确分类。准确率、敏感度和特异度分别为 0.954、0.844 和 0.993。
研究发现，使用头颅侧位片时，卷积神经网络可以相对准确地判断是否需要正颌手术。
