Angeloni Miriam, Rizzi Davide, Schoen Simon, Caputo Alessandro, Merolla Francesco, Hartmann Arndt, Ferrazzi Fulvia, Fraggetta Filippo
Institute of Pathology, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
Comprehensive Cancer Center Erlangen-EMN (CCC ER-EMN), Erlangen, Germany.
Genome Med. 2025 May 26;17(1):60. doi: 10.1186/s13073-025-01484-y.
Digital pathology (DP) has revolutionized cancer diagnostics and enabled the development of deep-learning (DL) models aimed at supporting pathologists in their daily work and improving patient care. However, the clinical adoption of such models remains challenging. Here, we describe a proof-of-concept framework that, leveraging Health Level 7 (HL7) standard and open-source DP resources, allows a seamless integration of both publicly available and custom developed DL models in the clinical workflow.
Development and testing of the framework were carried out in a fully digitized Italian pathology department. A Python-based server-client architecture was implemented to interconnect through HL7 messaging the anatomic pathology laboratory information system (AP-LIS) with an external artificial intelligence-based decision support system (AI-DSS) containing 16 pre-trained DL models. Open-source toolboxes for DL model deployment were used to run DL model inference, and QuPath was used to provide an intuitive visualization of model predictions as colored heatmaps.
A default deployment mode runs continuously in the background as each new slide is digitized, choosing the correct DL model(s) on the basis of the tissue type and staining. In addition, pathologists can initiate the analysis on-demand by selecting a specific DL model from the virtual slide tray. In both cases, the AP-LIS transmits an HL7 message to the AI-DSS, which processes the message, runs DL model inference, and creates the appropriate visualization style for the employed classification model. The AI-DSS transmits model inference results to the AP-LIS, where pathologists can visualize the output in QuPath and/or directly as slide description in the virtual slide tray.
Taken together, the developed integration framework through the use of the HL7 standard and freely available DP resources offers a standardized, portable, and open-source solution that lays the groundwork for the future widespread adoption of DL models in pathology diagnostics.
数字病理学（DP）彻底改变了癌症诊断方式，并推动了旨在辅助病理学家日常工作及改善患者护理的深度学习（DL）模型的发展。然而，此类模型在临床中的应用仍面临挑战。在此，我们描述了一个概念验证框架，该框架利用健康级别7（HL7）标准和开源DP资源，可在临床工作流程中无缝集成公开可用的和定制开发的DL模型。
该框架的开发与测试在一家完全数字化的意大利病理科进行。实施了基于Python的服务器-客户端架构，以通过HL7消息传递将解剖病理实验室信息系统（AP-LIS）与一个包含16个预训练DL模型的外部基于人工智能的决策支持系统（AI-DSS）互连。使用用于DL模型部署的开源工具箱运行DL模型推理，并使用QuPath将模型预测直观地可视化为彩色热图。
默认部署模式在每张新玻片数字化时在后台持续运行，根据组织类型和染色选择正确的DL模型。此外，病理学家可通过从虚拟玻片托盘中选择特定的DL模型按需启动分析。在这两种情况下，AP-LIS都会向AI-DSS发送HL7消息，AI-DSS处理该消息，运行DL模型推理，并为所采用的分类模型创建适当的可视化样式。AI-DSS将模型推理结果发送到AP-LIS，病理学家可在QuPath中可视化输出，和/或直接在虚拟玻片托盘中将其作为玻片描述进行查看。
总体而言，通过使用HL7标准和免费可用的DP资源开发的集成框架提供了一种标准化、可移植且开源的解决方案，为未来DL模型在病理诊断中的广泛应用奠定了基础。
