Faa Gavino, Fraschini Matteo, Barberini Luigi
Dipartimento di Scienze mediche e sanità pubblica, Università degli Studi di Cagliari, Cagliari, Italia.
Dipartimento di Ingegneria Elettrica ed Elettronica, Università degli Studi di Cagliari, Cagliari, Italia.
J Public Health Res. 2024 Oct 29;13(4):22799036241284898. doi: 10.1177/22799036241284898. eCollection 2024 Oct.
Artificial intelligence (AI), and more specifically Machine Learning (ML) and Deep learning (DL), has permeated the digital pathology field in recent years, with many algorithms successfully applied as new advanced tools to analyze pathological tissues. The introduction of high-resolution scanners in histopathology services has represented a real revolution for pathologists, allowing the analysis of digital whole-slide images (WSI) on a screen without a microscope at hand. However, it means a transition from microscope to algorithms in the absence of specific training for most pathologists involved in clinical practice. The WSI approach represents a major transformation, even from a computational point of view. The multiple ML and DL tools specifically developed for WSI analysis may enhance the diagnostic process in many fields of human pathology. AI-driven models allow the achievement of more consistent results, providing valid support for detecting, from H&E-stained sections, multiple biomarkers, including microsatellite instability, that are missed by expert pathologists.
近年来，人工智能（AI），更具体地说是机器学习（ML）和深度学习（DL），已渗透到数字病理学领域，许多算法已成功作为新的先进工具应用于分析病理组织。组织病理学服务中高分辨率扫描仪的引入对病理学家来说是一场真正的革命，使得在手头没有显微镜的情况下也能在屏幕上分析数字全切片图像（WSI）。然而，对于大多数参与临床实践的病理学家而言，这意味着在缺乏特定培训的情况下从显微镜过渡到算法。即使从计算的角度来看，WSI方法也代表着一项重大变革。专门为WSI分析开发的多种ML和DL工具可能会在人类病理学的许多领域中增强诊断过程。人工智能驱动的模型能够实现更一致的结果，为从苏木精-伊红（H&E）染色切片中检测包括微卫星不稳定性在内的多种生物标志物提供有效支持，而这些生物标志物是专家病理学家所遗漏的。
