数字病理学在过去几年中得到了发展，提出了重要的工作流优势，促进了其在专业环境中的应用。患者的临床和图像数据可以通过标准通信技术高效地在远程数据库中获取。新的影像技术和先进的人工智能算法的出现显著减轻了医疗专业人员的负担，加速了筛查过程。尽管取得了这些进展，数字病理学在专业环境中的应用受到了与服务之间的互操作性差、缺乏标准接口和集成解决方案的限制。本研究通过提出一个基于标准和开放接口的云端数字病理学平台来解决这个问题。研究提出并描述了一个供管理数字切片、医学报告和集成数字图像分析服务的厂商中性平台，与现有标准兼容。该解决方案集成了开源插件式Dicoogle PACS以实现互操作性和可扩展性，从而为所提出的解决方案提供了强大的功能定制能力。该解决方案是与iPATH研究项目合作伙伴及医学病理学家的验证共同开发的。其结果是一个纯粹的Web协作框架，支持研究和生产环境。总共成功上传了来自不同病理学的566张数字切片到该平台上。使用集成接口，成功将一个有丝分裂检测算法安装到了该平台上，并用来训练了2400个从乳腺癌图像中收集的注释。互操作性是讨论数字病理学解决方案时的一个关键因素，它有助于将其整合到现有机构的信息系统中。此外，它还改善了数据共享和第三方服务（如图像分析服务）的集成，这在当今数字病理学工作流中变得越来越重要。所提出的解决方案充分采用DICOM标准，并提供一个互操作性的云端解决方案，凭借其可扩展的架构提供了强大的功能定制能力。 版权所有 © 2023 年作者。由 Elsevier B.V.出版。保留所有权利。

Digital pathology has been evolving over the last years, proposing significant workflow advantages that have fostered its adoption in professional environments. Patient clinical and image data are readily available in remote data banks that can be consumed efficiently over standard communication technologies. The appearance of new imaging techniques and advanced artificial intelligence algorithms has significantly reduced the burden on medical professionals by speeding up the screening process. Despite these advancements, the usage of digital pathology in professional environments has been slowed down by poor interoperability between services resulting from a lack of standard interfaces and integrative solutions. This work addresses this issue by proposing a cloud-based digital pathology platform built on standard and open interfaces.The work proposes and describes a vendor-neutral platform that provides interfaces for managing digital slides, and medical reports, and integrating digital image analysis services compatible with existing standards. The solution integrates the open-source plugin-based Dicoogle PACS for interoperability and extensibility, which grants the proposed solution great feature customization.The solution was developed in collaboration with iPATH research project partners, including the validation by medical pathologists. The result is a pure Web collaborative framework that supports both research and production environments. A total of 566 digital slides from different pathologies were successfully uploaded to the platform. Using the integration interfaces, a mitosis detection algorithm was successfully installed into the platform, and it was trained with 2400 annotations collected from breast carcinoma images.Interoperability is a key factor when discussing digital pathology solutions, as it facilitates their integration into existing institutions' information systems. Moreover, it improves data sharing and integration of third-party services such as image analysis services, which have become relevant in today's digital pathology workflow. The proposed solution fully embraces the DICOM standard for digital pathology, presenting an interoperable cloud-based solution that provides great feature customization thanks to its extensible architecture.Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.