数字化病理学
病理
数字图像分析
癌症
工作量
医学
组织病理学
内科学
计算机科学
人工智能
胃肠道癌
结直肠癌
计算机视觉
操作系统
作者
Julien Caldéraro,Jakob Nikolas Kather
出处
期刊:Gut
[BMJ]
日期:2020-11-19
卷期号:70 (6): 1183-1193
被引量:59
标识
DOI:10.1136/gutjnl-2020-322880
摘要
Artificial intelligence (AI) can extract complex information from visual data. Histopathology images of gastrointestinal (GI) and liver cancer contain a very high amount of information which human observers can only partially make sense of. Complementing human observers, AI allows an in-depth analysis of digitised histological slides of GI and liver cancer and offers a wide range of clinically relevant applications. First, AI can automatically detect tumour tissue, easing the exponentially increasing workload on pathologists. In addition, and possibly exceeding pathologist's capacities, AI can capture prognostically relevant tissue features and thus predict clinical outcome across GI and liver cancer types. Finally, AI has demonstrated its capacity to infer molecular and genetic alterations of cancer tissues from histological digital slides. These are likely only the first of many AI applications that will have important clinical implications. Thus, pathologists and clinicians alike should be aware of the principles of AI-based pathology and its ability to solve clinically relevant problems, along with its limitations and biases.
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