Abstract：
Diagnosing early lumbar spondylolisthesis is challenging for many doctors because of the lack of obvious symptoms. Using deep learning (DL) models to improve the accuracy of X-ray diagnoses can effectively reduce missed and misdiagnoses in clinical practice. This study aimed to use a two-stage deep learning model, the Res-SE-Net model with the YOLOv8 algorithm, to facilitate efficient and reliable diagnosis of early lumbar spondylolisthesis based on lateral X-ray image identification.
A total of 2424 lumbar lateral radiographs of patients treated in the Beijing Tongren Hospital between January 2021 and September 2023 were obtained. The data were labeled and mutually identified by 3 orthopedic surgeons after reshuffling in a random order and divided into a training set, validation set, and test set in a ratio of 7:2:1. We trained 2 models for automatic detection of spondylolisthesis. YOLOv8 model was used to detect the position of lumbar spondylolisthesis, and the Res-SE-Net classification method was designed to classify the clipped area and determine whether it was lumbar spondylolisthesis. The model performance was evaluated using a test set and an external dataset from Beijing Haidian Hospital. Finally, we compared model validation results with professional clinicians\' evaluation.
The model achieved promising results, with a high diagnostic accuracy of 92.3%, precision of 93.5%, and recall of 93.1% for spondylolisthesis detection on the test set, the area under the curve (AUC) value was 0.934.
Our two-stage deep learning model provides doctors with a reference basis for the better diagnosis and treatment of early lumbar spondylolisthesis.
A total of 2424 lumbar lateral radiographs of patients treated in the Beijing Tongren Hospital between January 2021 and September 2023 were obtained. The data were labeled and mutually identified by 3 orthopedic surgeons after reshuffling in a random order and divided into a training set, validation set, and test set in a ratio of 7:2:1. We trained 2 models for automatic detection of spondylolisthesis. YOLOv8 model was used to detect the position of lumbar spondylolisthesis, and the Res-SE-Net classification method was designed to classify the clipped area and determine whether it was lumbar spondylolisthesis. The model performance was evaluated using a test set and an external dataset from Beijing Haidian Hospital. Finally, we compared model validation results with professional clinicians\' evaluation.
The model achieved promising results, with a high diagnostic accuracy of 92.3%, precision of 93.5%, and recall of 93.1% for spondylolisthesis detection on the test set, the area under the curve (AUC) value was 0.934.
Our two-stage deep learning model provides doctors with a reference basis for the better diagnosis and treatment of early lumbar spondylolisthesis.
摘要：
背景：由于缺乏明显的症状，对许多医生来说，诊断早期腰椎滑脱具有挑战性。利用深度学习(DL)模型提高X线诊断的准确性,可有效减少临床漏诊和误诊。
目的：本研究旨在使用两阶段深度学习模型，采用YOLOv8算法的Res-SE-Net模型，基于侧位X线影像识别的早期腰椎滑脱诊断,便于高效可靠的诊断。
方法：收集2021年1月至2023年9月北京同仁医院收治的2424例患者的腰椎侧位片。数据由三位骨科医生以随机顺序重新洗牌后进行标记和相互识别,并分成训练集,验证集,和测试集的比例为7:2:1。我们训练了两个模型来自动检测脊椎滑脱。采用YOLOv8模型检测腰椎滑脱的位置,并设计Res-SE-Net分类方法对夹闭区域进行分类，判断是否为腰椎滑脱。使用测试集和外部数据集评估模型性能。最后,我们将模型验证结果与专业临床医生的评估进行了比较。
结果：该模型取得了有希望的结果，具有92.3%的高诊断准确率，精度为93.5%，在测试装置上检测脊椎滑脱的召回率为93.1%，曲线下面积（AUC）值为0.934。
结论：我们的两阶段深度学习模式为医生更好地诊断和治疗早期腰椎滑脱提供了参考依据。
目的：本研究旨在使用两阶段深度学习模型，采用YOLOv8算法的Res-SE-Net模型，基于侧位X线影像识别的早期腰椎滑脱诊断,便于高效可靠的诊断。
方法：收集2021年1月至2023年9月北京同仁医院收治的2424例患者的腰椎侧位片。数据由三位骨科医生以随机顺序重新洗牌后进行标记和相互识别,并分成训练集,验证集,和测试集的比例为7:2:1。我们训练了两个模型来自动检测脊椎滑脱。采用YOLOv8模型检测腰椎滑脱的位置,并设计Res-SE-Net分类方法对夹闭区域进行分类，判断是否为腰椎滑脱。使用测试集和外部数据集评估模型性能。最后,我们将模型验证结果与专业临床医生的评估进行了比较。
结果：该模型取得了有希望的结果，具有92.3%的高诊断准确率，精度为93.5%，在测试装置上检测脊椎滑脱的召回率为93.1%，曲线下面积（AUC）值为0.934。
结论：我们的两阶段深度学习模式为医生更好地诊断和治疗早期腰椎滑脱提供了参考依据。
