利用锥形束 CT 图像上的深度学习技术诊断活体中牙根纵裂。
Diagnosis of in vivo vertical root fracture using deep learning on cone-beam CT images.
机构信息
Department of Dentomaxillofacial Radiology, Nanjing Stomatological Hospital, Medical School of Nanjing University, Zhong Yang Road 30, Nanjing City, 210008, Jiangsu, People's Republic of China.
Department of Stomatology, Guangdong Medical University Affiliated Longhua Central Hospital, Shenzhen, China.
出版信息
BMC Oral Health. 2022 Sep 5;22(1):382. doi: 10.1186/s12903-022-02422-9.
OBJECTIVES
Evaluating the diagnostic efficiency of deep learning models to diagnose vertical root fracture in vivo on cone-beam CT (CBCT) images.
MATERIALS AND METHODS
The CBCT images of 276 teeth (138 VRF teeth and 138 non-VRF teeth) were enrolled and analyzed retrospectively. The diagnostic results of these teeth were confirmed by two chief radiologists. There were two experimental groups: auto-selection group and manual selection group. A total of 552 regions of interest of teeth were cropped in manual selection group and 1118 regions of interest of teeth were cropped in auto-selection group. Three deep learning networks (ResNet50, VGG19 and DenseNet169) were used for diagnosis (3:1 for training and testing). The diagnostic efficiencies (accuracy, sensitivity, specificity, and area under the curve (AUC)) of three networks were calculated in two experiment groups. Meanwhile, 552 teeth images in manual selection group were diagnosed by a radiologist. The diagnostic efficiencies of the three deep learning network models in two experiment groups and the radiologist were calculated.
RESULTS
In manual selection group, ResNet50 presented highest accuracy and sensitivity for diagnosing VRF teeth. The accuracy, sensitivity, specificity and AUC was 97.8%, 97.0%, 98.5%, and 0.99, the radiologist presented accuracy, sensitivity, and specificity as 95.3%, 96.4 and 94.2%. In auto-selection group, ResNet50 presented highest accuracy and sensitivity for diagnosing VRF teeth, the accuracy, sensitivity, specificity and AUC was 91.4%, 92.1%, 90.7% and 0.96.
CONCLUSION
In manual selection group, ResNet50 presented higher diagnostic efficiency in diagnosis of in vivo VRF teeth than VGG19, DensenNet169 and radiologist with 2 years of experience. In auto-selection group, Resnet50 also presented higher diagnostic efficiency in diagnosis of in vivo VRF teeth than VGG19 and DensenNet169. This makes it a promising auxiliary diagnostic technique to screen for VRF teeth.
目的
评估深度学习模型在活体锥形束 CT（CBCT）图像上诊断垂直根折的诊断效率。
材料和方法
回顾性纳入 276 颗牙齿（138 颗垂直根折牙齿和 138 颗非垂直根折牙齿）的 CBCT 图像。由两位首席放射科医生确认这些牙齿的诊断结果。实验分为两组：自动选择组和手动选择组。手动选择组共裁剪 552 个牙齿感兴趣区，自动选择组共裁剪 1118 个牙齿感兴趣区。使用 3 种深度学习网络（ResNet50、VGG19 和 DenseNet169）进行诊断（3：1 用于训练和测试）。计算两组中三种网络的诊断效率（准确性、敏感度、特异性和曲线下面积（AUC））。同时，由一位放射科医生对手动选择组中的 552 个牙齿图像进行诊断。计算两组和放射科医生中三种深度学习网络模型的诊断效率。
结果
在手动选择组中，ResNet50 对诊断垂直根折牙齿的准确性和敏感度最高。准确性、敏感度、特异性和 AUC 分别为 97.8%、97.0%、98.5%和 0.99，放射科医生的准确性、敏感度和特异性分别为 95.3%、96.4%和 94.2%。在自动选择组中，ResNet50 对诊断垂直根折牙齿的准确性和敏感度最高，准确性、敏感度、特异性和 AUC 分别为 91.4%、92.1%、90.7%和 0.96。
结论
在手动选择组中，ResNet50 对诊断活体垂直根折牙齿的诊断效率高于具有 2 年经验的放射科医生和 VGG19、DensenNet169。在自动选择组中，Resnet50 对诊断活体垂直根折牙齿的诊断效率也高于 VGG19 和 DensenNet169。这使其成为一种有前途的辅助诊断技术，可用于筛查垂直根折。
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