Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, King Abdulaziz University, Jeddah, Saudi Arabia..
Acta Histochem. 2022 May;124(4):151890. doi: 10.1016/j.acthis.2022.151890. Epub 2022 Mar 30.
Deep learning algorithms and artificial intelligence (AI) are making great progress in their capacity to evaluate and interpret image data recent advancements in computer vision and machine learning. The first use of AI in a pathology lab was in cytopathology, when a computer-assisted Pap test screening was created. Initially designed to diagnose rather than screen, there was a lot of disagreement concerning their wide use to clinical specimens. However, whole-slide imaging of both gynaecological and non-gynaecological histopathology have been the subject of recent AI work. An overview of the literature on AI in cytopathology is provided in this brief review. To be more precise, it intends to emphasize the relevance of applications of AI algorithms to gynaecological and non-gynaecologic cytology. Between January 2000 and December 2021, a search on artificial intelligence in cytopathology was conducted in several well-known databases, including PubMed, Web of Science, Scopus, Embase, and Google Scholar. Only full-text papers that could be accessed online were evaluated.
深度学习算法和人工智能（AI）在评估和解释图像数据方面取得了重大进展，这得益于计算机视觉和机器学习的最新进展。AI 在病理学实验室中的首次应用是在细胞病理学中，当时创建了计算机辅助的巴氏涂片筛查。最初设计用于诊断而不是筛查，人们对将其广泛应用于临床标本存在很多分歧。然而，全玻片成像在妇科和非妇科组织病理学中一直是最近 AI 工作的主题。本文简要综述了细胞病理学中 AI 的文献。更准确地说，它旨在强调 AI 算法在妇科和非妇科细胞学中的应用的相关性。在 2000 年 1 月至 2021 年 12 月期间，在包括 PubMed、Web of Science、Scopus、Embase 和 Google Scholar 在内的几个知名数据库中对细胞病理学中的人工智能进行了搜索。仅评估了可以在线访问的全文论文。
