El Achi Hanadi, Khoury Joseph D
Department of Pathology and Laboratory Medicine, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Department of Hematopathology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Cancers (Basel). 2020 Mar 26;12(4):797. doi: 10.3390/cancers12040797.
Digital Pathology is the process of converting histology glass slides to digital images using sophisticated computerized technology to facilitate acquisition, evaluation, storage, and portability of histologic information. By its nature, digitization of analog histology data renders it amenable to analysis using deep learning/artificial intelligence (DL/AI) techniques. The application of DL/AI to digital pathology data holds promise, even if the scope of use cases and regulatory framework for deploying such applications in the clinical environment remains in the early stages. Recent studies using whole-slide images and DL/AI to detect histologic abnormalities in general and cancer in particular have shown encouraging results. In this review, we focus on these emerging technologies intended for use in diagnostic hematology and the evaluation of lymphoproliferative diseases.
数字病理学是利用先进的计算机技术将组织学玻璃切片转换为数字图像的过程，以促进组织学信息的采集、评估、存储和便携性。就其本质而言，模拟组织学数据的数字化使其适合使用深度学习/人工智能（DL/AI）技术进行分析。将DL/AI应用于数字病理学数据具有前景，尽管在临床环境中部署此类应用的用例范围和监管框架仍处于早期阶段。最近使用全切片图像和DL/AI检测一般组织学异常尤其是癌症的研究已显示出令人鼓舞的结果。在本综述中，我们重点关注这些旨在用于诊断血液学和淋巴增殖性疾病评估的新兴技术。
