Using Real-Time Artificial Intelligence to Detect Upper Gastrointestinal Cancer by Endoscopy

Background: Upper gastrointestinal (UGI) cancers (including esophageal cancer, gastric cancer, etc.) are the most common cancers worldwide. Detection at early stage is the key for increasing the overall survival. Artificial intelligence (AI) using deep learning algorithms have made remarkable progress in medical imaging. Here we constructed a Gastrointestinal Artificial Intelligence Diagnostic System (GRAIDS) to improve the diagnostic accuracy of upper gastrointestinal (UGI) cancer by analyzing imaging data from clinical endoscopies. Methods: A multi-institutional, diagnostic study from six hospitals of different tiers across China was initiated. We included a total of 1,036,496 endoscopy images from 84,424 individuals to develop and validate the GRAIDS. We evaluated the performance of the GRAIDS using an internal validation set and a prospective validation set from a national hospital and five external validation sets from primary care hospitals. We also compared the diagnostic performance of the GRAIDS with that of endoscopists with three degrees of expertise (expert, competent, and trainee). Findings: The diagnostic accuracy in identifying UGI cancer was 0.955 in the internal validation set and 0.927 in the prospective set, and ranged from 0.915 to 0.977 in the five external validation sets. Compared with the expert endoscopists, the GRAIDS achieved a comparable sensitivity to experts (0.942 versus 0.945; P=0.692) and a superior sensitivity to competent (0.858, P<0.001) and trainee endoscopists (0.722, P<0.001). Interpretation: The GRAIDS achieved high diagnostic accuracy in detecting UGI cancer, with a sensitivity comparable to experts and was superior to nonexpert endoscopists. This system could support nonexpert endoscopists from primary basic hospitals by improving their diagnostic accuracy to the level of the experts, thus providing an opportunity for community-based hospitals to improve their effectiveness in UGI cancer diagnosis. Funding Statement: This research was supported by the National Key R&D Program of China (2018YFC1313300), the Natural Science Foundation of Guangdong Province (2017A030313485, 2014A030312015), the Science and Technology Program of Guangdong (2015B020232008), the Science and Technology Program of Guangzhou (201508020250, 201604020003, 2019B020227002). Declaration of Interests: No competing interests are declared. Ethics Approval Statement: This study was approved by all the relevant independent institutional review boards (IRB) and was performed according to the Helsinki declaration.