Landau Michael S, Pantanowitz Liron
Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.
J Am Soc Cytopathol. 2019 Jul-Aug;8(4):230-241. doi: 10.1016/j.jasc.2019.03.003. Epub 2019 Mar 25.
Artificial intelligence (AI) has made impressive strides recently in interpreting complex images, thanks to improvements in deep learning techniques and increasing computational power. Researchers have started applying these advanced techniques to pathology images, although most efforts have been focused on histopathology. Cytopathology, however, remains the original field of pathology for which AI models for clinical use were successfully commercialized, to assist with automating Papanicolaou test screening. Recent AI efforts have focused on whole slide images of both gynecologic and non-gynecologic cytopathology. This review summarizes the literature and commercial landscape of AI as applied to cytopathology.
得益于深度学习技术的进步和计算能力的提升,人工智能(AI)近来在解读复杂图像方面取得了令人瞩目的进展。研究人员已开始将这些先进技术应用于病理图像,不过大多数工作都集中在组织病理学上。然而,细胞病理学仍是病理学的原始领域,针对该领域的临床用人工智能模型已成功实现商业化,以协助实现巴氏试验筛查的自动化。近期人工智能的工作重点是妇科和非妇科细胞病理学的全玻片图像。本综述总结了应用于细胞病理学的人工智能的文献和商业情况。