卷积神经网络
锥束ct
人工智能
牙源性的
深度学习
计算机断层摄影
医学
放射科
计算机科学
模式识别(心理学)
计算机断层摄影术
病理
作者
Jae‐Hong Lee,Do-Hyung Kim,Seong‐Nyum Jeong
出处
期刊:Oral Diseases
[Wiley]
日期:2019-11-18
卷期号:26 (1): 152-158
被引量:109
摘要
Abstract Objectives The aim of the current study was to evaluate the detection and diagnosis of three types of odontogenic cystic lesions (OCLs)—odontogenic keratocysts, dentigerous cysts, and periapical cysts—using dental panoramic radiography and cone beam computed tomographic (CBCT) images based on a deep convolutional neural network (CNN). Methods The GoogLeNet Inception‐v3 architecture was used to enhance the overall performance of the detection and diagnosis of OCLs based on transfer learning. Diagnostic indices (area under the ROC curve [AUC], sensitivity, specificity, and confusion matrix with and without normalization) were calculated and compared between pretrained models using panoramic and CBCT images. Results The pretrained model using CBCT images showed good diagnostic performance (AUC = 0.914, sensitivity = 96.1%, specificity = 77.1%), which was significantly greater than that achieved by other models using panoramic images (AUC = 0.847, sensitivity = 88.2%, specificity = 77.0%) ( p = .014). Conclusions This study demonstrated that panoramic and CBCT image datasets, comprising three types of odontogenic OCLs, are effectively detected and diagnosed based on the deep CNN architecture. In particular, we found that the deep CNN architecture trained with CBCT images achieved higher diagnostic performance than that trained with panoramic images.
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