The Digital ART Lab of School of Software, Shanghai JiaoTong University, Shanghai 200240, China.
Department of Bone and Joint Surgery, Department of Orthopaedics, Shanghai Jiaotong University, School of Medicine, Renji Hospital, Shanghai 200127, China.
J Healthc Eng. 2021 Apr 20;2021:6662664. doi: 10.1155/2021/6662664. eCollection 2021.
The aim of the present study is to build a software implementation of a previous study and to diagnose discoid lateral menisci on knee joint radiograph images. A total of 160 images from normal individuals and patients who were diagnosed with discoid lateral menisci were included. Our software implementation includes two parts: preprocessing and measurement. In the first phase, the whole radiograph image was analyzed to obtain basic information about the patient. Machine learning was used to segment the knee joint from the original radiograph image. Image enhancement and denoising tools were used to strengthen the image and remove noise. In the second phase, edge detection was used to quantify important features in the image. A specific algorithm was designed to build a model of the knee joint and measure the parameters. Of the test images, 99.65% were segmented correctly. Furthermore, 97.5% of the tested images were segmented correctly and their parameters were measured successfully. There was no significant difference between manual and automatic measurements in the discoid (=0.28) and control groups (=0.15). The mean and standard deviations of the ratio of lateral joint space distance to the height of the lateral tibial spine were compared with the results of manual measurement. The software performed well on raw radiographs, showing a satisfying success rate and robustness. Thus, it is possible to diagnose discoid lateral menisci on radiographs with the help of radiograph-image-analyzing software (BM3D, etc.) and artificial intelligence-related tools (YOLOv3). The results of this study can help build a joint database that contains data from patients and thus can play a role in the diagnosis of discoid lateral menisci and other knee joint diseases in the future.
本研究旨在构建之前研究的软件实现，并对膝关节 X 射线图像中的盘状外侧半月板进行诊断。共纳入了 160 张来自正常个体和被诊断为盘状外侧半月板的患者的图像。我们的软件实现包括两个部分：预处理和测量。在第一阶段，分析整个 X 射线图像以获得有关患者的基本信息。使用机器学习从原始 X 射线图像中分割膝关节。使用图像增强和去噪工具来增强图像并去除噪声。在第二阶段，使用边缘检测来量化图像中的重要特征。设计了一种特定的算法来构建膝关节模型并测量参数。在测试图像中，99.65%的图像被正确分割。此外，97.5%的测试图像被正确分割且其参数被成功测量。在盘状组（=0.28）和对照组（=0.15）中，手动和自动测量之间没有显著差异。比较了外侧关节间隙距离与外侧胫骨棘高度之比的平均值和标准差与手动测量的结果。该软件对原始 X 射线表现良好，具有令人满意的成功率和稳健性。因此，借助 X 射线图像分析软件（如 BM3D 等）和人工智能相关工具（如 YOLOv3 等），有可能在 X 射线图像上诊断盘状外侧半月板。本研究的结果有助于构建一个包含患者数据的联合数据库，从而在未来对盘状外侧半月板和其他膝关节疾病的诊断中发挥作用。
