• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

病理实验室数字化:经验教训回顾。

Digitization of Pathology Labs: A Review of Lessons Learned.

机构信息

Fraunhofer Institute for Digital Medicine MEVIS, Bremen, Germany.

Charité-Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt Universität zu Berlin, Institute of Pathology, Berlin, Germany.

出版信息

Lab Invest. 2023 Nov;103(11):100244. doi: 10.1016/j.labinv.2023.100244. Epub 2023 Aug 30.

DOI:10.1016/j.labinv.2023.100244
PMID:37657651
Abstract

Pathology laboratories are increasingly using digital workflows. This has the potential of increasing laboratory efficiency, but the digitization process also involves major challenges. Several reports have been published describing the individual experiences of specific laboratories with the digitization process. However, a comprehensive overview of the lessons learned is still lacking. We provide an overview of the lessons learned for different aspects of the digitization process, including digital case management, digital slide reading, and computer-aided slide reading. We also cover metrics used for monitoring performance and pitfalls and corresponding values observed in practice. The overview is intended to help pathologists, information technology decision makers, and administrators to benefit from the experiences of others and to implement the digitization process in an optimal way to make their own laboratory future-proof.

摘要

病理实验室越来越多地采用数字化工作流程。这有可能提高实验室的效率,但数字化过程也涉及重大挑战。已经有几篇报告描述了特定实验室在数字化过程中的个别经验。然而,对于所学到的经验教训仍缺乏全面的概述。我们提供了数字化过程不同方面的经验教训概述,包括数字病例管理、数字切片阅读和计算机辅助切片阅读。我们还涵盖了用于监测性能的指标以及在实践中观察到的陷阱和相应值。该概述旨在帮助病理学家、信息技术决策者和管理人员从他人的经验中受益,并以最佳方式实施数字化过程,使自己的实验室具有前瞻性。

相似文献

1
Digitization of Pathology Labs: A Review of Lessons Learned.病理实验室数字化:经验教训回顾。
Lab Invest. 2023 Nov;103(11):100244. doi: 10.1016/j.labinv.2023.100244. Epub 2023 Aug 30.
2
Whole slide imaging equivalency and efficiency study: experience at a large academic center.全 slides 成像等效性和效率研究:大型学术中心的经验。
Mod Pathol. 2019 Jul;32(7):916-928. doi: 10.1038/s41379-019-0205-0. Epub 2019 Feb 18.
3
Whole Slide Images in Artificial Intelligence Applications in Digital Pathology: Challenges and Pitfalls.人工智能在数字病理学中的全切片图像应用:挑战与陷阱。
Turk Patoloji Derg. 2023;39(2):101-108. doi: 10.5146/tjpath.2023.01601.
4
Validation of a digital pathology system including remote review during the COVID-19 pandemic.验证一种数字病理学系统,包括在 COVID-19 大流行期间进行远程审查。
Mod Pathol. 2020 Nov;33(11):2115-2127. doi: 10.1038/s41379-020-0601-5. Epub 2020 Jun 22.
5
The Pathologist 2.0: An Update on Digital Pathology in Veterinary Medicine.病理学家2.0:兽医学数字病理学的最新进展
Vet Pathol. 2017 Sep;54(5):756-766. doi: 10.1177/0300985817709888. Epub 2017 Jun 5.
6
Revolutionizing Digital Pathology With the Power of Generative Artificial Intelligence and Foundation Models.利用生成式人工智能和基础模型推动数字病理学革命。
Lab Invest. 2023 Nov;103(11):100255. doi: 10.1016/j.labinv.2023.100255. Epub 2023 Sep 26.
7
New Trends of Emerging Technologies in Digital Pathology.数字病理学中新兴技术的新趋势
Pathobiology. 2016;83(2-3):61-9. doi: 10.1159/000443482. Epub 2016 Apr 26.
8
The future of pathology is digital.病理学的未来是数字化的。
Pathol Res Pract. 2020 Sep;216(9):153040. doi: 10.1016/j.prp.2020.153040. Epub 2020 Jun 20.
9
Generative Deep Learning in Digital Pathology Workflows.生成式深度学习在数字病理学工作流程中的应用。
Am J Pathol. 2021 Oct;191(10):1717-1723. doi: 10.1016/j.ajpath.2021.02.024. Epub 2021 Apr 8.
10
Automated complete slide digitization: a medium for simultaneous viewing by multiple pathologists.自动全玻片数字化:一种可供多位病理学家同时查看的媒介。
J Pathol. 2001 Nov;195(4):508-14. doi: 10.1002/path.972.

引用本文的文献

1
From Microscopes to Monitors: Unique Opportunities and Challenges in Digital Pathology Implementation in Remote Canadian Regions.从显微镜到监视器:加拿大偏远地区数字病理学实施中的独特机遇与挑战
Diagnostics (Basel). 2025 Aug 8;15(16):1983. doi: 10.3390/diagnostics15161983.
2
An open-source platform for structured annotation and computational workflows in digital pathology research.一个用于数字病理学研究中结构化注释和计算工作流程的开源平台。
Sci Rep. 2025 Aug 7;15(1):28910. doi: 10.1038/s41598-025-13546-7.
3
Streamlining a Patchwork - Exploring the Challenges of Digital Transformation in Pathology: Ethnographic Study.
简化拼凑之物——探索病理学数字转型的挑战:人种志研究
J Med Internet Res. 2025 Jul 18;27:e63366. doi: 10.2196/63366.
4
Pathologist-Read vs AI-Driven Assessment of Tumor-Infiltrating Lymphocytes in Melanoma.病理学家解读与人工智能驱动的黑色素瘤肿瘤浸润淋巴细胞评估
JAMA Netw Open. 2025 Jul 1;8(7):e2518906. doi: 10.1001/jamanetworkopen.2025.18906.
5
Deep learning predicts the effect of neoadjuvant chemotherapy for patients with triple negative breast cancer.深度学习可预测三阴性乳腺癌患者新辅助化疗的效果。
J Pathol Inform. 2025 May 14;18:100448. doi: 10.1016/j.jpi.2025.100448. eCollection 2025 Aug.
6
Advancements in pathology: Digital transformation, precision medicine, and beyond.病理学的进展:数字转型、精准医学及其他。
J Pathol Inform. 2024 Nov 19;16:100408. doi: 10.1016/j.jpi.2024.100408. eCollection 2025 Jan.
7
Structuring and centralizing breast cancer real-world biomarker data from pathology reports through C-LAB artificial intelligence platform.通过C-LAB人工智能平台构建并集中来自病理报告的乳腺癌真实世界生物标志物数据。
Digit Health. 2025 Feb 25;11:20552076251323110. doi: 10.1177/20552076251323110. eCollection 2025 Jan-Dec.
8
An equivalency and efficiency study for one year digital pathology for clinical routine diagnostics in an accredited tertiary academic center.在一家经认可的三级学术中心进行的为期一年的数字病理学用于临床常规诊断的等效性和效率研究。
Virchows Arch. 2025 Feb 18. doi: 10.1007/s00428-025-04043-3.
9
Artificial intelligence in digital pathology - time for a reality check.数字病理学中的人工智能——是时候进行现实核查了。
Nat Rev Clin Oncol. 2025 Apr;22(4):283-291. doi: 10.1038/s41571-025-00991-6. Epub 2025 Feb 11.
10
Transforming Diagnostics: A Comprehensive Review of Advances in Digital Pathology.变革性诊断:数字病理学进展的全面综述
Cureus. 2024 Oct 19;16(10):e71890. doi: 10.7759/cureus.71890. eCollection 2024 Oct.