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Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study
Huiyan Luo ,
Guoliang Xu ,
Chaofeng Li ,
Longjun He ,
Linna Luo ,
Zixian Wang ,
Bingzhong Jing ,
Yishu Deng ,
Ying Jin ,
Yin Li ,
Bin Li ,
Wencheng Tan ,
Caisheng He ,
Sharvesh Raj Seeruttun ,
Qiubao Wu ,
Jun Huang ,
De-wang Huang ,
Bin Chen ,
Shao-bin Lin ,
Qin-ming Chen ,
Chu-ming Yuan ,
Hai-xin Chen ,
Heng-ying Pu ,
Feng Zhou ,
Yun He ,
Rui-hua Xu
October 2019
October 2019
There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
<p class="first" id="d14795795e336">Upper gastrointestinal cancers (including oesophageal
cancer and gastric cancer) are
the most common cancers worldwide. Artificial intelligence platforms using deep learning
algorithms have made remarkable progress in medical imaging but their application
in upper gastrointestinal cancers has been limited. We aimed to develop and validate
the Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) for the diagnosis
of upper gastrointestinal cancers through analysis of imaging data from clinical endoscopies.
</p>