Paweł Łajczak, Jakub Matyja, Kamil Jóźwik, Zbigniew Nawrat
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The primary metric was the Dice score, supplemented by relative volume error (RVE) and average symmetric surface distance (ASSD).</p><p><strong>Results: </strong>The search process identified 752 articles, leading to 11 studies for meta-analysis. A QUADAS- 2 analysis revealed varying biases. The overall Dice score for 56 models was 0.89 (CI: 0.88-0.90), with high heterogeneity (I2 = 95.9%). Subgroup analyses based on DL architecture, MRI inputs, and testing set sizes revealed performance variations. 2.5D DL networks demonstrated comparable efficacy to 3D networks. Imaging input analyses highlighted the superiority of contrast-enhanced T1-weighted imaging and mixed MRI inputs.</p><p><strong>Discussion: </strong>This study fills a gap in systematic review in the automated segmentation of VS using DL techniques. Despite promising results, limitations include publication bias and high heterogeneity. Future research should focus on standardized designs, larger testing sets, and addressing biases for more reliable results. DL have promising efficacy in VS diagnosis, however further validation and standardization is needed.</p><p><strong>Conclusion: </strong>In conclusion, this meta-analysis provides comprehensive review into the current landscape of automated VS segmentation using DL. The high Dice score indicates promising agreement in segmentation, yet challenges like bias and heterogeneity must be addressed in the future research.</p>","PeriodicalId":19422,"journal":{"name":"Neuroradiology","volume":" ","pages":""},"PeriodicalIF":2.4000,"publicationDate":"2024-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Accuracy of vestibular schwannoma segmentation using deep learning models - a systematic review & meta-analysis.\",\"authors\":\"Paweł Łajczak, Jakub Matyja, Kamil Jóźwik, Zbigniew Nawrat\",\"doi\":\"10.1007/s00234-024-03449-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Vestibular Schwannoma (VS) is a rare tumor with varied incidence rates, predominantly affecting the 60-69 age group. In the era of artificial intelligence (AI), deep learning (DL) algorithms show promise in automating diagnosis. 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引用次数: 0
摘要
前庭许旺瘤(VS)是一种罕见肿瘤,发病率不一,主要影响 60-69 岁年龄组的人群。在人工智能(AI)时代,深度学习(DL)算法在自动诊断方面大有可为。然而,在使用 DL 自动分割 VS 方面还存在知识空白。为了弥补这一空白,本荟萃分析旨在深入了解应用于 VS MR 图像的 DL 算法的现状:按照 2020 年 PRISMA 指南,对四个数据库进行了检索。纳入标准侧重于使用 DL 进行 VS MR 图像分割的文章。主要指标是 Dice 分数,辅以相对容积误差 (RVE) 和平均对称面距离 (ASSD):搜索过程中发现了 752 篇文章,最终有 11 项研究进行了荟萃分析。QUADAS- 2 分析显示了不同的偏差。56 个模型的总体 Dice 得分为 0.89(CI:0.88-0.90),异质性较高(I2 = 95.9%)。基于 DL 架构、磁共振成像输入和测试集大小的分组分析显示了性能差异。2.5D DL 网络的疗效与 3D 网络相当。成像输入分析强调了对比增强 T1 加权成像和混合 MRI 输入的优越性:本研究填补了使用 DL 技术自动分割 VS 的系统性综述空白。尽管结果令人鼓舞,但也存在发表偏倚和高度异质性等局限性。未来的研究应侧重于标准化设计、更大的测试集以及解决偏倚问题,以获得更可靠的结果。DL在VS诊断中具有良好的疗效,但仍需进一步验证和标准化:总之,这项荟萃分析全面回顾了目前使用 DL 进行 VS 自动分割的情况。高 Dice 分数表明分割的一致性很好,但在未来的研究中必须解决偏倚和异质性等挑战。
Accuracy of vestibular schwannoma segmentation using deep learning models - a systematic review & meta-analysis.
Vestibular Schwannoma (VS) is a rare tumor with varied incidence rates, predominantly affecting the 60-69 age group. In the era of artificial intelligence (AI), deep learning (DL) algorithms show promise in automating diagnosis. However, a knowledge gap exists in the automated segmentation of VS using DL. To address this gap, this meta-analysis aims to provide insights into the current state of DL algorithms applied to MR images of VS.
Methodology: Following 2020 PRISMA guidelines, a search across four databases was conducted. Inclusion criteria focused on articles using DL for VS MR image segmentation. The primary metric was the Dice score, supplemented by relative volume error (RVE) and average symmetric surface distance (ASSD).
Results: The search process identified 752 articles, leading to 11 studies for meta-analysis. A QUADAS- 2 analysis revealed varying biases. The overall Dice score for 56 models was 0.89 (CI: 0.88-0.90), with high heterogeneity (I2 = 95.9%). Subgroup analyses based on DL architecture, MRI inputs, and testing set sizes revealed performance variations. 2.5D DL networks demonstrated comparable efficacy to 3D networks. Imaging input analyses highlighted the superiority of contrast-enhanced T1-weighted imaging and mixed MRI inputs.
Discussion: This study fills a gap in systematic review in the automated segmentation of VS using DL techniques. Despite promising results, limitations include publication bias and high heterogeneity. Future research should focus on standardized designs, larger testing sets, and addressing biases for more reliable results. DL have promising efficacy in VS diagnosis, however further validation and standardization is needed.
Conclusion: In conclusion, this meta-analysis provides comprehensive review into the current landscape of automated VS segmentation using DL. The high Dice score indicates promising agreement in segmentation, yet challenges like bias and heterogeneity must be addressed in the future research.
期刊介绍:
Neuroradiology aims to provide state-of-the-art medical and scientific information in the fields of Neuroradiology, Neurosciences, Neurology, Psychiatry, Neurosurgery, and related medical specialities. Neuroradiology as the official Journal of the European Society of Neuroradiology receives submissions from all parts of the world and publishes peer-reviewed original research, comprehensive reviews, educational papers, opinion papers, and short reports on exceptional clinical observations and new technical developments in the field of Neuroimaging and Neurointervention. The journal has subsections for Diagnostic and Interventional Neuroradiology, Advanced Neuroimaging, Paediatric Neuroradiology, Head-Neck-ENT Radiology, Spine Neuroradiology, and for submissions from Japan. Neuroradiology aims to provide new knowledge about and insights into the function and pathology of the human nervous system that may help to better diagnose and treat nervous system diseases. Neuroradiology is a member of the Committee on Publication Ethics (COPE) and follows the COPE core practices. Neuroradiology prefers articles that are free of bias, self-critical regarding limitations, transparent and clear in describing study participants, methods, and statistics, and short in presenting results. Before peer-review all submissions are automatically checked by iThenticate to assess for potential overlap in prior publication.