Deep learning-assisted classification of calcaneofibular ligament injuries in the ankle joint.
Deep learning-assisted classification of calcaneofibular ligament injuries in the ankle joint.
复制标题
深度学习辅助踝关节跟腓韧带损伤分类
DOI：
10.21037/qims-22-470
复制
发表时间：
2023-01-01
影响因子：
2.8
通讯作者：
中科院分区：
文献类型：
--
作者：
在磁共振成像（MRI）上对跟腓韧带（CFL）损伤进行分类是一项耗时的工作，而且阅片者之间存在很大的差异。本研究探讨了使用深度学习方法对CFL损伤进行分类的可行性，将其与肌肉骨骼（MSK）放射科医生的分类进行比较，并进一步检查图像裁剪筛选和校准方法。方法回顾性分析1,074例踝关节镜及MRI检查患者的影像学资料。根据关节镜检查结果，将患者分为正常（0级，n=475）;变性、劳损和部分撕裂（1级，n=217）;和完全撕裂（2级，n=382）组。所有患者以8：1：1的比例分为训练集、验证集和测试集。预处理后，使用基于掩码区域的卷积神经网络（R-CNN）裁剪图像，然后应用注意力算法进行图像筛选和校准，并实施LeNet-5进行CFL损伤分类。比较轴向、冠状和组合模型的诊断效果，并选择最佳方法进行外组验证。将模型在组内和组外测试集中的诊断结果与4名不同资历的MSK放射科医生的诊断结果进行比较。结果使用注意算法的Mask R-CNN对轴向和冠状序列的左右图像裁剪的平均精度（mAP）为0.90-0.96。LeNet-5分类0-2类的准确性分别为0.92，0.93和0.92，轴向序列和0.89，0.92和0.90，分别为冠状序列。序列组合后，分类精度为0-2类分别为0.95，0.97和0.96。4名MSK放射科医师将组内测试集分类为0-2类的平均准确度为0.94、0.91、0.86和0.85，所有这些均与模型有显著差异。MSK放射科医师将外组测试集分类为0-2类的平均准确度为0.92、0.91、0.87和0.85，2名高级MSK放射科医师表现出与模型相似的诊断性能，而初级MSK放射科医师表现出更差的准确度。结论深度学习可用于对CFL损伤进行分类，其级别与MSK放射科医生的级别相似。裁剪后加入注意力算法有助于CFL图像的精确裁剪。
Background The classification of calcaneofibular ligament (CFL) injuries on magnetic resonance imaging (MRI) is time-consuming and subject to substantial interreader variability. This study explores the feasibility of classifying CFL injuries using deep learning methods by comparing them with the classifications of musculoskeletal (MSK) radiologists and further examines image cropping screening and calibration methods. Methods The imaging data of 1,074 patients who underwent ankle arthroscopy and MRI examinations in our hospital were retrospectively analyzed. According to the arthroscopic findings, patients were divided into normal (class 0, n=475); degeneration, strain, and partial tear (class 1, n=217); and complete tear (class 2, n=382) groups. All patients were divided into training, validation, and test sets at a ratio of 8:1:1. After preprocessing, the images were cropped using Mask region-based convolutional neural network (R-CNN), followed by the application of an attention algorithm for image screening and calibration and the implementation of LeNet-5 for CFL injury classification. The diagnostic effects of the axial, coronal, and combined models were compared, and the best method was selected for outgroup validation. The diagnostic results of the models in the intragroup and outgroup test sets were compared with those results of 4 MSK radiologists of different seniorities. Results The mean average precision (mAP) of the Mask R-CNN using the attention algorithm for the left and right image cropping of axial and coronal sequences was 0.90–0.96. The accuracy of LeNet-5 for classifying classes 0–2 was 0.92, 0.93, and 0.92, respectively, for the axial sequences and 0.89, 0.92, and 0.90, respectively, for the coronal sequences. After sequence combination, the classification accuracy for classes 0–2 was 0.95, 0.97, and 0.96, respectively. The mean accuracies of the 4 MSK radiologists in classifying the intragroup test set as classes 0–2 were 0.94, 0.91, 0.86, and 0.85, all of which were significantly different from the model. The mean accuracies of the MSK radiologists in classifying the outgroup test set as classes 0–2 were 0.92, 0.91, 0.87, and 0.85, with the 2 senior MSK radiologists demonstrating similar diagnostic performance to the model and the junior MSK radiologists demonstrating worse accuracy. Conclusions Deep learning can be used to classify CFL injuries at similar levels to those of MSK radiologists. Adding an attention algorithm after cropping is helpful for accurately cropping CFL images.
登录
查看更多内容
DOI：
10.1002/jor.24926
发表时间：
2021-04
期刊：
Journal of orthopaedic research : official publication of the Orthopaedic Research Society
影响因子：
--
作者：
Flannery SW;Kiapour AM;Edgar DJ;Murray MM;Fleming BC
通讯作者：
Fleming BC
影响因子：
3.8
作者：
Hattori, Soichi;Nimura, Akimoto;Akita, Keiichi
通讯作者：
Akita, Keiichi
影响因子：
2.6
作者：
Park, H. J.;Lee, S. Y.;Lee, J. W.
通讯作者：
Lee, J. W.
影响因子：
1.3
作者：
Currie, Geoff
通讯作者：
Currie, Geoff
DOI：
10.1109/tpami.2018.2844175
发表时间：
2020-02-01
影响因子：
23.6
作者：
He, Kaiming;Gkioxari, Georgia;Girshick, Ross
通讯作者：
Girshick, Ross