标题： Z-Stack扫描可提高人工智能对有丝分裂的检测：脑膜瘤案例研究 显示英文标题

标题： Z-Stack Scanning can Improve AI Detection of Mitosis: A Case Study of Meningiomas

摘要： Z-stack扫描是一种新兴的全切片成像技术，它沿着载玻片的z轴捕获多个焦平面。 由于与单层全切片成像相比，z-stack可以提供增强的深度信息，因此该技术在分析小规模的组织病理学模式时可能特别有用。 然而，其实际临床影响仍存在争议，结果不一。 为了澄清这一点，我们研究了Z-stack扫描对脑膜瘤人工智能（AI）分裂检测的影响。 使用由三种不同的数字病理扫描仪扫描的22张苏木精和伊红脑膜瘤载玻片，我们在单层和Z-stack全切片图像（WSIs）上测试了三个AI流程的性能。 结果显示，在所有扫描仪-AI组合中，Z-stack WSIs显著提高了AI在分裂检测中的敏感性（+17.14%），而对精确度的影响很小。 我们的研究结果提供了定量证据，强调Z-stack扫描作为AI分裂检测有前景的技术，为更可靠的AI辅助病理工作流程铺平了道路，最终有助于患者管理。 显示英文摘要

摘要： Z-stack scanning is an emerging whole slide imaging technology that captures multiple focal planes alongside the z-axis of a glass slide. Because z-stacking can offer enhanced depth information compared to the single-layer whole slide imaging, this technology can be particularly useful in analyzing small-scaled histopathological patterns. However, its actual clinical impact remains debated with mixed results. To clarify this, we investigate the effect of z-stack scanning on artificial intelligence (AI) mitosis detection of meningiomas. With the same set of 22 Hematoxylin and Eosin meningioma glass slides scanned by three different digital pathology scanners, we tested the performance of three AI pipelines on both single-layer and z-stacked whole slide images (WSIs). Results showed that in all scanner-AI combinations, z-stacked WSIs significantly increased AI's sensitivity (+17.14%) on the mitosis detection with only a marginal impact on precision. Our findings provide quantitative evidence that highlights z-stack scanning as a promising technique for AI mitosis detection, paving the way for more reliable AI-assisted pathology workflows, which can ultimately benefit patient management.