实时病理辅助工具OnSightPathology，让普通电脑也能运行AI病理分析。
OnSight Pathology: A real-time platform-agnostic computational pathology companion for histopathology
- 通过连续截屏实现无侵入式实时AI推理，无需复杂安装
- 在2500+全切片图像上验证，支持脑肿瘤分类、有丝分裂检测等任务
- 兼容手机摄像头，适合手术中、远程会诊等真实场景
显微镜下组织切片检查仍是疾病分类的核心手段，但依赖主观判断且需专业专家，影响准确性和临床诊疗。尽管人工智能在自动化组织学分析方面展现出潜力，但日益增多的专有数字病理解决方案阻碍了实际应用。为此，我们提出OnSight Pathology——一种平台无关的计算机视觉软件，利用持续自定义屏幕截图，为用户在浏览数字切片时提供实时AI推理。该软件以单个可执行文件形式发布（https://onsightpathology.github.io/），可在消费级个人电脑上本地运行，无需复杂集成，实现低成本、高安全性的研究与临床部署。我们在超过2,500张公开可用的全切片图像及临床数字病理系统案例中展示了其有效性。软件在常规病理任务中表现稳健，包括常见脑肿瘤类型分类、有丝分裂检测和免疫组化染色定量。内置多模态聊天助手可提供可验证的图像描述，不依赖固定类别标签，提升质量控制。最后，我们证明其可兼容实时显微镜摄像机流，包括个人智能手机，具备在更传统、术中及远程病理场景中的应用潜力。综上，OnSight Pathology能跨多种病理流程提供实时AI推理，打破人工智能工具在病理学中应用的关键障碍。
原文摘要 · Abstract (English)
The microscopic examination of surgical tissue remains a cornerstone of disease classification but relies on subjective interpretations and access to highly specialized experts, which can compromise accuracy and clinical care. While emerging breakthroughs in artificial intelligence (AI) offer promise for automated histological analysis, the growing number of proprietary digital pathology solutions has created barriers to real-world deployment. To address these challenges, we introduce OnSight Pathology, a platform-agnostic computer vision software that uses continuous custom screen captures to provide real-time AI inferences to users as they review digital slide images. Accessible as a single, self-contained executable file (https://onsightpathology.github.io/ ), OnSight Pathology operates locally on consumer-grade personal computers without complex software integration, enabling cost-effective and secure deployment in research and clinical workflows. Here we demonstrate the utility of OnSight Pathology using over 2,500 publicly available whole slide images across different slide viewers, as well as cases from our clinical digital pathology setup. The software's robustness is highlighted across routine histopathological tasks, including the classification of common brain tumor types, mitosis detection, and the quantification of immunohistochemical stains. A built-in multi-modal chat assistant provides verifiable descriptions of images, free of rigid class labels, for added quality control. Lastly, we show compatibility with live microscope camera feeds, including from personal smartphones, offering potential for deployment in more analog, inter-operative, and telepathology settings. Together, we highlight how OnSight Pathology can deliver real-time AI inferences across a broad range of pathology pipelines, removing key barriers to the adoption of AI tools in histopathology.
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