Deep learning in Cobb angle automated measurement on X-rays: a systematic review and meta-analysis.

IF 1.6 Q3 CLINICAL NEUROLOGY

Spine deformity Pub Date : 2024-09-25 DOI:10.1007/s43390-024-00954-4

Yuanpeng Zhu, Xiangjie Yin, Zefu Chen, Haoran Zhang, Kexin Xu, Jianguo Zhang, Nan Wu

{"title":"Deep learning in Cobb angle automated measurement on X-rays: a systematic review and meta-analysis.","authors":"Yuanpeng Zhu, Xiangjie Yin, Zefu Chen, Haoran Zhang, Kexin Xu, Jianguo Zhang, Nan Wu","doi":"10.1007/s43390-024-00954-4","DOIUrl":null,"url":null,"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.","PeriodicalId":21796,"journal":{"name":"Spine deformity","volume":" ","pages":""},"PeriodicalIF":1.6000,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Spine deformity","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1007/s43390-024-00954-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CLINICAL NEUROLOGY","Score":null,"Total":0}

引用次数: 0

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.

查看原文本刊更多论文

深度学习在 X 射线柯布角自动测量中的应用：系统综述与荟萃分析。

目的：本研究旨在概述不同的深度学习算法（DLA），找出其局限性，并总结提高 DLA 性能的潜在解决方案：我们审查了符合条件的关于在 X 光片上自动估算 Cobb 角度的 DLA 的研究，并进行了荟萃分析。截至 2023 年 9 月，我们在六个数据库中进行了系统的文献检索。我们的荟萃分析包括对研究报告的圆平均绝对误差（CMAE）进行评估，以及对实施策略进行分组分析。偏倚风险采用修订后的《诊断准确性研究质量评估》（QUADAS-2）进行评估。本研究在启动前已在 PROSPERO 注册（CRD42023403057）：我们从系统检索中确定了 120 篇文章（n = 3022），最终将 50 项研究纳入系统综述，17 项研究纳入荟萃分析。CMAE的总体估计值为2.99（95% CI为2.61-3.38），异质性较高（94%，P 结论：CMAE的总体估计值为2.99（95% CI为2.61-3.38）：根据我们有限的荟萃分析结果，DLA 对自动 Cobb 角测量的准确性相对较高。就 CMAE 而言，基于分割的方法可能比基于地标的方法表现更好。我们还总结了在未来研究中改进模型设计的潜在方法。在报告 DLA 时，遵循质量指南非常重要。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Spine deformity

CiteScore

3.20

自引率

18.80%

发文量

167

期刊介绍： Spine Deformity the official journal of the?Scoliosis Research Society is a peer-refereed publication to disseminate knowledge on basic science and clinical research into the?etiology?biomechanics?treatment?methods and outcomes of all types of?spinal deformities. The international members of the Editorial Board provide a worldwide perspective for the journal's area of interest.The?journal?will enhance the mission of the Society which is to foster the optimal care of all patients with?spine?deformities worldwide. Articles published in?Spine Deformity?are Medline indexed in PubMed.? The journal publishes original articles in the form of clinical and basic research. Spine Deformity will only publish studies that have institutional review board (IRB) or similar ethics committee approval for human and animal studies and have strictly observed these guidelines. The minimum follow-up period for follow-up clinical studies is 24 months.