背景:近年来,深度学习(DL)技术越来越多地用于腰椎间盘退变的诊断和治疗。本研究旨在评估DL技术在磁共振(MR)图像中的IVD分割性能,并探索改进策略。
方法:我们开发了PRISMA系统审查协议,并系统地审查了使用DL算法框架基于截至2024年4月10日发布的MR图像进行IVD分割的研究。诊断准确性研究质量评估-2工具用于评估方法学质量,并计算合并骰子相似系数(DSC)得分和联合交集(IoU)以评估分割性能。
结果:本系统综述包括45项研究,其中16项提供了完整的分割性能数据,并纳入了定量荟萃分析.结果表明,DL模型显示出令人满意的IVD分割性能,合并DSC为0.900(95%置信区间[CI]:0.887-0.914),IoU为0.863(95%CI:0.730-0.995)。然而,亚组分析没有显示因素对IVD分割性能的显著影响,包括网络维度,算法类型,出版年份,患者数量,扫描方向,数据增强,和交叉验证。
结论:本研究强调了DL技术在IVD分割中的潜力及其进一步应用。然而,由于算法框架和纳入研究结果报告的异质性,结论应谨慎解释。未来的研究应该集中在大规模数据集上训练广义模型,以增强其临床应用。
BACKGROUND: In recent years, deep learning (DL) technology has been increasingly used for the diagnosis and treatment of lumbar intervertebral disc (IVD) degeneration. This study aims to evaluate the performance of DL technology for IVD segmentation in magnetic resonance (MR) images and explore improvement strategies.
METHODS: We developed a PRISMA systematic review protocol and systematically reviewed studies that used DL algorithm frameworks to perform IVD segmentation based on MR images published up to April 10, 2024. The Quality Assessment of Diagnostic Accuracy Studies-2 tool was used to assess methodological quality, and the pooled dice similarity coefficient (DSC) score and Intersection over Union (IoU) were calculated to evaluate segmentation performance.
RESULTS: 45 studies were included in this systematic review, of which 16 provided complete segmentation performance data and were included in the quantitative meta-analysis. The results indicated that DL models showed satisfactory IVD segmentation performance, with a pooled DSC of 0.900 (95% confidence interval [CI]: 0.887-0.914) and IoU of 0.863 (95% CI: 0.730-0.995). However, the subgroup analysis did not show significant effects of factors on IVD segmentation performance, including network dimensionality, algorithm type, publication year, number of patients, scanning direction, data augmentation, and cross-validation.
CONCLUSIONS: This study highlights the potential of DL technology in IVD segmentation and its further applications. However, due to the heterogeneity in algorithm frameworks and result reporting of the included studies, the conclusions should be interpreted with caution. Future research should focus on training generalized models on large-scale datasets to enhance their clinical application.