Deep learning-driven diagnosis of multi-type vertebra diseases based on computed tomography images

作者：Yongjie Wang, Feng Su, Lu Qian, Wenkai Zhang, Tao Liu, Yining Tao, Shuai Fu, Libin Cui, Shibao Lu, Xueming Chen, Zhenyun Shi · 发表于：Quantitative Imaging in Medicine and Surgery · 年份：2023 · DOI：10.21037/qims-23-685 · 被引用次数：6 · 研究领域：Medical Imaging and Analysis、Spinal Fractures and Fixation Techniques、Bone and Joint Diseases

Background: Osteoporotic vertebral compression fractures (OVCFs) are the most common type of fragility fracture. Distinguishing between OVCFs and other types of vertebra diseases, such as old fractures (OFs), Schmorl's node (SN), Kummell's disease (KD), and previous surgery (PS), is critical for subsequent surgery and treatment. Combining with advanced deep learning (DL) technologies, this study plans to develop a DL-driven diagnostic system for diagnosing multi-type vertebra diseases. Methods: We established a large-scale dataset based on the computed tomography (CT) images of 1,051 patients with OVCFs from Luhe Hospital and used data of 46 patients from Xuanwu Hospital as alternative hospital validation dataset. Each patient underwent one examination. The dataset contained 11,417 CT slices and 19,718 manually annotated vertebrae with diseases. A two-stage DL-based system was developed to diagnose five vertebra diseases. The proposed system consisted of a vertebra detection module (VDModule) and a vertebra classification module (VCModule). Results: The training and testing dataset for the VDModule consisted of 9,135 and 3,212 vertebrae, respectively. The VDModule using the ResNet18-based Faster region-based convolutional neural network (R-CNN) model achieved an area under the curve (AUC), false-positive (FP) rate, and false-negative (FN) rate of 0.982, 1.52%, and 1.33%, respectively, in the testing dataset. The training dataset for VCModule consisted of 14,584 and 47,604 dis...