Deep Learning in Cobb Angle Automated Measurement on X-rays: a Systematic Review and Meta-analysis

Overview

Journal Spine Deform

Publisher Springer Nature

Date 2024 Sep 25

PMID 39320698

Authors

Yuanpeng Zhu

Xiangjie Yin

Zefu Chen

Haoran Zhang

Kexin Xu

Jianguo Zhang

Nan Wu

Affiliations

Soon will be listed here.

Abstract

Purpose: This study aims to provide an overview of different deep learning algorithms (DLAs), identify the limitations, and summarize potential solutions to improve the performance of DLAs.

Methods: We reviewed eligible studies on DLAs for automated Cobb angle estimation on X-rays and conducted a meta-analysis. A systematic literature search was conducted in six databases up until September 2023. Our meta-analysis included an evaluation of reported circular mean absolute error (CMAE) from the studies, as well as a subgroup analysis of implementation strategies. Risk of bias was assessed using the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). This study was registered in PROSPERO prior to initiation (CRD42023403057).

Results: We identified 120 articles from our systematic search (n = 3022), eventually including 50 studies in the systematic review and 17 studies in the meta-analysis. The overall estimate for CMAE was 2.99 (95% CI 2.61-3.38), with high heterogeneity (94%, p < 0.01). Segmentation-based methods showed greater accuracy (p < 0.01), with a CMAE of 2.40 (95% CI 1.85-2.95), compared to landmark-based methods, which had a CMAE of 3.31 (95% CI 2.89-3.72).

Conclusions: According to our limited meta-analysis results, DLAs have shown relatively high accuracy for automated Cobb angle measurement. In terms of CMAE, segmentation-based methods may perform better than landmark-based methods. We also summarized potential ways to improve model design in future studies. It is important to follow quality guidelines when reporting on DLAs.

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