Yang Xuanzhe, Zhang Yuming, Li Yi, Wu Zixiang
Second Clinical Medical College, Shaanxi University of Chinese Medicine, Xianyang, Shaanxi, China.
Department of Orthopedics, Xijing Hospital, The First Affiliated Hospital of Air Force Medical University, Xi'an, Shaanxi, China.
Spine (Phila Pa 1976). 2025 May 15;50(10):E179-E196. doi: 10.1097/BRS.0000000000005174. Epub 2024 Oct 11.
The present study followed the reporting guidelines for systematic reviews and meta-analyses.
We conducted this study to review the diagnostic value of artificial intelligence (AI) for various types of lumbar spinal stenosis (LSS) and the level of stenosis, offering evidence-based support for the development of smart diagnostic tools.
AI is currently being utilized for image processing in clinical practice. Some studies have explored AI techniques for identifying the severity of LSS in recent years. Nevertheless, there remains a shortage of structured data proving its effectiveness.
Four databases (PubMed, Cochrane, Embase, and Web of Science) were searched until March 2024, including original studies that utilized deep learning (DL) and machine learning (ML) models to diagnose LSS. The risk of bias of included studies was assessed using Quality Assessment of Diagnostic Accuracy Studies is a quality evaluation tool for diagnostic research (diagnostic tests). Computed Tomography. PROSPERO is an international database of prospectively registered systematic reviews. Summary Receiver Operating Characteristic. Magnetic Resonance. Central canal stenosis. three-dimensional magnetic resonance myelography. The accuracy in the validation set was extracted for a meta-analysis. The meta-analysis was completed in R4.4.0.
A total of 48 articles were included, with an overall accuracy of 0.885 (95% CI: 0.860-0907) for dichotomous tasks. Among them, the accuracy was 0.892 (95% CI: 0.867-0915) for DL and 0.833 (95% CI: 0.760-0895) for ML. The overall accuracy for LSS was 0.895 (95% CI: 0.858-0927), with an accuracy of 0.912 (95% CI: 0.873-0.944) for DL and 0.843 (95% CI: 0.766-0.907) for ML. The overall accuracy for central canal stenosis was 0.875 (95% CI: 0.821-0920), with an accuracy of 0.881 (95% CI: 0.829-0.925) for DL and 0.733 (95% CI: 0.541-0.877) for ML. The overall accuracy for neural foramen stenosis was 0.893 (95% CI: 0.851-0.928). In polytomous tasks, the accuracy was 0.936 (95% CI: 0.895-0.967) for no LSS, 0.503 (95% CI: 0.391-0.614) for mild LSS, 0.512 (95% CI: 0.336-0.688) for moderate LSS, and 0.860 for severe LSS (95% CI: 0.733-0.954).
AI is highly valuable for diagnosing LSS. However, further external validation is necessary to enhance the analysis of different stenosis categories and improve the diagnostic accuracy for mild to moderate stenosis levels.
本研究遵循系统评价和荟萃分析的报告指南。
我们开展本研究以评估人工智能（AI）对各类腰椎管狭窄症（LSS）及其狭窄程度的诊断价值，为智能诊断工具的开发提供循证支持。
目前AI已用于临床实践中的图像处理。近年来，一些研究探索了AI技术在识别LSS严重程度方面的应用。然而，仍缺乏结构化数据证明其有效性。
检索了四个数据库（PubMed、Cochrane、Embase和Web of Science）直至2024年3月，纳入利用深度学习（DL）和机器学习（ML）模型诊断LSS的原始研究。采用诊断准确性研究的质量评估工具（一种用于诊断研究（诊断试验）的质量评估工具）评估纳入研究的偏倚风险。计算机断层扫描。PROSPERO是一个前瞻性注册系统评价的国际数据库。汇总受试者工作特征曲线。磁共振成像。中央管狭窄。三维磁共振脊髓造影。提取验证集中的准确性进行荟萃分析。荟萃分析在R4.4.0中完成。
共纳入48篇文章，二分任务的总体准确率为0.885（95%CI：0.860 - 0.907）。其中，DL的准确率为0.892（95%CI：0.867 - 0.915），ML的准确率为0.833（95%CI：0.760 - 0.895）。LSS的总体准确率为0.895（95%CI：0.858 - 0.927），DL的准确率为0.912（95%CI：0.873 - 0.944），ML的准确率为0.843（95%CI：0.766 - 0.907）。中央管狭窄的总体准确率为0.875（95%CI：0.821 - 0.920），DL的准确率为0.881（95%CI：0.829 - 0.925），ML的准确率为0.733（95%CI：0.541 -
