血液病理学
数字化病理学
工作流程
计算机科学
心灵感应学
病理
人工智能
人工智能应用
数据科学
医学
远程医疗
生物
医疗保健
生物化学
数据库
细胞遗传学
经济
染色体
基因
经济增长
作者
Elisa Lin,Franklin Fuda,Hung S. Luu,Andrew M. Cox,Fengqi Fang,Junlin Feng,Mingyi Chen
标识
DOI:10.1053/j.semdp.2023.02.001
摘要
Digital pathology has a crucial role in diagnostic pathology and is increasingly a technological requirement in the field. Integration of digital slides into the pathology workflow, advanced algorithms, and computer-aided diagnostic techniques extend the frontiers of the pathologist's view beyond the microscopic slide and enable true integration of knowledge and expertise. There is clear potential for artificial intelligence (AI) breakthroughs in pathology and hematopathology. In this review article, we discuss the approach of using machine learning in the diagnosis, classification, and treatment guidelines of hematolymphoid disease, as well as recent progress of artificial intelligence in flow cytometric analysis of hematolymphoid diseases. We review these topics specifically through the potential clinical applications of CellaVision, an automated digital image analyzer of peripheral blood, and Morphogo, a novel artificial intelligence-based bone marrow analyzing system. Adoption of these new technologies will allow pathologists to streamline workflow and achieve faster turnaround time in diagnosing hematological disease.
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