基于深度学习的面部照片判断需要正颌手术的软组织轮廓。
Deep learning based discrimination of soft tissue profiles requiring orthognathic surgery by facial photographs.
机构信息
Safety System Research Group, Korea Institute of Industrial Technology (KITECH), Gyeongsan, Korea.
Department of Oral and Maxillofacial Radiology, Daejeon Dental Hospital, Wonkwang University College of Dentistry, Daejeon, Korea.
出版信息
Sci Rep. 2020 Oct 1;10(1):16235. doi: 10.1038/s41598-020-73287-7.
Facial photographs of the subjects are often used in the diagnosis process of orthognathic surgery. The aim of this study was to determine whether convolutional neural networks (CNNs) can judge soft tissue profiles requiring orthognathic surgery using facial photographs alone. 822 subjects with dentofacial dysmorphosis and / or malocclusion were included. Facial photographs of front and right side were taken from all patients. Subjects who did not need orthognathic surgery were classified as Group I (411 subjects). Group II (411 subjects) was set up for cases requiring surgery. CNNs of VGG19 was used for machine learning. 366 of the total 410 data were correctly classified, yielding 89.3% accuracy. The values of accuracy, precision, recall, and F1 scores were 0.893, 0.912, 0.867, and 0.889, respectively. As a result of this study, it was found that CNNs can judge soft tissue profiles requiring orthognathic surgery relatively accurately with the photographs alone.
面部照片通常用于正颌手术的诊断过程。本研究旨在确定卷积神经网络（CNN）是否可以仅通过面部照片判断需要正颌手术的软组织轮廓。共纳入 822 名牙颌面畸形和/或错颌患者。对所有患者拍摄正面和右侧面的面部照片。不需要正颌手术的患者被归类为 I 组（411 例）。II 组（411 例）为需要手术的病例。使用 VGG19 的 CNN 进行机器学习。在总共 410 个数据中，有 366 个被正确分类，准确率为 89.3%。准确率、精确度、召回率和 F1 分数分别为 0.893、0.912、0.867 和 0.889。本研究结果表明，CNN 仅通过照片即可相对准确地判断需要正颌手术的软组织轮廓。
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