PDF(Pubmed)
Abstract：
The development of rapid and accurate Whole Slide Imaging (WSI) has paved the way for the application of Artificial Intelligence (AI) to digital pathology. The availability of WSI in the recent years allowed the rapid development of various AI technologies to blossom. WSI-based digital pathology combined with neural networks can automate arduous and time-consuming tasks of slide evaluation. Machine Learning (ML)-based AI has been demonstrated to outperform pathologists by eliminating inter- and intra-observer subjectivity, obtaining quantitative data from slide images, and extracting hidden image patterns that are relevant to disease subtype and progression. In this review, we outline the functionality of different AI technologies such as neural networks and deep learning and discover how aspects of different diseases make them benefit from the implementation of AI. AI has proven to be valuable in many different organs, with this review focusing on the liver, kidney, and lungs. We also discuss how AI and image analysis not only can grade diseases objectively but also discover aspects of diseases that have prognostic value. In the end, we review the current status of the integration of AI in pathology and share our vision on the future of digital pathology.
摘要：
快速准确的全幻灯片成像（WSI）的发展为人工智能（AI）在数字病理学中的应用铺平了道路。近年来，WSI的可用性使各种AI技术的快速发展蓬勃发展。基于WSI的数字病理学与神经网络的结合可以使幻灯片评估的艰巨且耗时的任务自动化。基于机器学习（ML）的AI已被证明通过消除观察者之间和观察者之间的主观性而胜过病理学家。从幻灯片图像中获取定量数据，并提取与疾病亚型和进展相关的隐藏图像模式。在这次审查中,我们概述了神经网络和深度学习等不同AI技术的功能，并发现不同疾病的各个方面如何使它们从AI的实施中受益。人工智能已经被证明在许多不同的器官中是有价值的，这篇综述集中在肝脏上，肾,还有肺.我们还讨论了AI和图像分析如何不仅可以客观地对疾病进行分级，而且可以发现具有预后价值的疾病方面。最后,我们回顾了人工智能在病理学中的整合现状，并分享了我们对数字病理学未来的愿景。
