Performance of Artificial Intelligence in Diagnosing Lumbar Spinal Stenosis: A Systematic Review and Meta-Analysis.

Abstract

Study design: The present study followed the reporting guidelines for systematic reviews and meta-analyses.

Objective: 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.

Background: 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.

Materials and methods: 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.

Results: 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).

Conclusions: 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.