Zhang Jiayao, Lin Heng, Wang Honglin, Xue Mingdi, Fang Ying, Liu Songxiang, Huo Tongtong, Zhou Hong, Yang Jiaming, Xie Yi, Xie Mao, Cheng Liangli, Lu Lin, Liu Pengran, Ye Zhewei
Department of Orthopedics, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Intelligent Medical Laboratory, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Front Bioeng Biotechnol. 2023 Jul 19;11:1194009. doi: 10.3389/fbioe.2023.1194009. eCollection 2023.
Explore a new deep learning (DL) object detection algorithm for clinical auxiliary diagnosis of lumbar spondylolisthesis and compare it with doctors' evaluation to verify the effectiveness and feasibility of the DL algorithm in the diagnosis of lumbar spondylolisthesis. Lumbar lateral radiographs of 1,596 patients with lumbar spondylolisthesis from three medical institutions were collected, and senior orthopedic surgeons and radiologists jointly diagnosed and marked them to establish a database. These radiographs were randomly divided into a training set ( = 1,117), a validation set ( = 240), and a test set ( = 239) in a ratio of 0.7 : 0.15: 0.15. We trained two DL models for automatic detection of spondylolisthesis and evaluated their diagnostic performance by PR curves, areas under the curve, precision, recall, F1-score. Then we chose the model with better performance and compared its results with professionals' evaluation. A total of 1,780 annotations were marked for training (1,242), validation (263), and test (275). The Faster Region-based Convolutional Neural Network (R-CNN) showed better precision (0.935), recall (0.935), and F1-score (0.935) in the detection of spondylolisthesis, which outperformed the doctor group with precision (0.927), recall (0.892), f1-score (0.910). In addition, with the assistance of the DL model, the precision of the doctor group increased by 4.8%, the recall by 8.2%, the F1-score by 6.4%, and the average diagnosis time per plain X-ray was shortened by 7.139 s. The DL detection algorithm is an effective method for clinical diagnosis of lumbar spondylolisthesis. It can be used as an assistant expert to improve the accuracy of lumbar spondylolisthesis diagnosis and reduce the clinical workloads.
探索一种用于腰椎滑脱临床辅助诊断的新型深度学习（DL）目标检测算法，并将其与医生的评估进行比较，以验证DL算法在腰椎滑脱诊断中的有效性和可行性。收集了来自三家医疗机构的1596例腰椎滑脱患者的腰椎侧位X线片，由资深骨科医生和放射科医生共同进行诊断和标记，建立数据库。这些X线片按照0.7:0.15:0.15的比例随机分为训练集（=1117）、验证集（=240）和测试集（=239）。我们训练了两个用于自动检测腰椎滑脱的DL模型，并通过PR曲线、曲线下面积、精度、召回率、F1分数评估它们的诊断性能。然后我们选择性能更好的模型，并将其结果与专业人员的评估进行比较。总共标记了1780个注释用于训练（1242个）、验证（263个）和测试（275个）。基于区域的快速卷积神经网络（R-CNN）在腰椎滑脱检测中表现出更好的精度（0.935）、召回率（0.935）和F1分数（0.935），优于医生组的精度（0.927）、召回率（0.892）、F1分数（0.910）。此外，在DL模型的辅助下，医生组的精度提高了4.8%，召回率提高了8.2%，F1分数提高了6.4%，每张普通X线片的平均诊断时间缩短了7.139秒。DL检测算法是腰椎滑脱临床诊断的有效方法。它可以作为辅助专家，提高腰椎滑脱诊断的准确性，减少临床工作量。
