Feng Xiaochen, Zhang Yaying, Lu Minming, Ma Chao, Miao Xiaoqiang, Yang Jiacheng, Lin Lina, Zhang Yueyi, Zhang Kai, Zhang Ning, Kang Yan, Luo Yu, Cao Kai
Department of Diagnostic Radiology, Changhai Hospital, Shanghai, China.
Department of Diagnostic Radiology, the Fourth People's Hospital of Shanghai, Shanghai, China.
Quant Imaging Med Surg. 2025 Sep 1;15(9):8457-8470. doi: 10.21037/qims-2025-67. Epub 2025 Aug 19.
Currently, there is no fully automated tool available for evaluating the degree of cervical spinal stenosis. The aim of this study was to develop and validate the use of artificial intelligence (AI) algorithms for the assessment of cervical spinal stenosis.
In this retrospective multi-center study, cervical spine magnetic resonance imaging (MRI) scans obtained from July 2020 to June 2023 were included. Studies of patients with spinal instrumentation or studies with suboptimal image quality were excluded. Sagittal T2-weighted images were used. The training data from the Fourth People's Hospital of Shanghai (Hos. 1) and Shanghai Changzheng Hospital (Hos. 2) were annotated by two musculoskeletal (MSK) radiologists following Kang's system as the standard reference. First, a convolutional neural network (CNN) was trained to detect the region of interest (ROI), with a second Transformer for classification. The performance of the deep learning (DL) model was assessed on an internal test set from Hos. 2 and an external test set from Shanghai Changhai Hospital (Hos. 3), and compared among six readers. Metrics such as detection precision, interrater agreement, sensitivity (SEN), and specificity (SPE) were calculated.
Overall, 795 patients were analyzed (mean age ± standard deviation, 55±14 years; 346 female), with 589 in the training (75%) and validation (25%) sets, 206 in the internal test set, and 95 in the external test set. Four tasks with different clinical application scenarios were trained, and their accuracy (ACC) ranged from 0.8993 to 0.9532. When using a Kang system score of ≥2 as a threshold for diagnosing central cervical canal stenosis in the internal test set, both the algorithm and six readers achieved similar areas under the receiver operating characteristic curve (AUCs) of 0.936 [95% confidence interval (CI): 0.916-0.955], with a SEN of 90.3% and SPE of 93.8%; the AUC of the DL model was 0.931 (95% CI: 0.917-0.946), with a SEN in the external test set of 100%, and a SPE of 86.3%. Correlation analysis comparing the DL method, the six readers, and MRI reports between the reference standard showed a moderate correlation, with R values ranging from 0.589 to 0.668. The DL model produced approximately the same upgrades (9.2%) and downgrades (5.1%) as the six readers.
The DL model could fully automatically and reliably assess cervical canal stenosis using MRI scans.
目前，尚无用于评估颈椎管狭窄程度的全自动工具。本研究的目的是开发并验证使用人工智能（AI）算法评估颈椎管狭窄。
在这项回顾性多中心研究中，纳入了2020年7月至2023年6月期间获得的颈椎磁共振成像（MRI）扫描。排除了脊柱内固定患者的研究或图像质量欠佳的研究。使用矢状面T2加权图像。来自上海市第四人民医院（医院1）和上海长征医院（医院2）的训练数据由两名肌肉骨骼（MSK）放射科医生按照康氏系统进行标注，作为标准参考。首先，训练一个卷积神经网络（CNN）来检测感兴趣区域（ROI），然后使用第二个Transformer进行分类。在医院2的内部测试集和上海长海医院（医院3）的外部测试集中评估深度学习（DL）模型的性能，并在六位读者之间进行比较。计算检测精度、阅片者间一致性、敏感性（SEN）和特异性（SPE）等指标。
总体而言，共分析了795例患者（平均年龄±标准差，55±14岁；女性346例），其中589例在训练集（75%）和验证集（25%）中，206例在内部测试集中，95例在外部测试集中。训练了四个具有不同临床应用场景的任务，其准确率（ACC）范围为0.8993至0.9532。在内部测试集中，当以康氏系统评分≥2作为诊断中央椎管狭窄的阈值时，算法和六位读者在受试者操作特征曲线（AUC）下的面积相似，均为0.936[95%置信区间（CI）：0.916 - 0.955]，SEN为90.3%，SPE为93.8%；DL模型的AUC为0.931（95%CI：0.917 - 0.946），在外部测试集中SEN为100%，SPE为86.3%。将DL方法、六位读者和MRI报告与参考标准进行相关性分析，结果显示具有中等相关性，R值范围为0.589至0.668。DL模型产生的升级（9.2%）和降级（5.1%）与六位读者大致相同。
DL模型可以使用MRI扫描完全自动且可靠地评估椎管狭窄。
