Real-time artificial intelligence for detection of upper gastrointestinal cancer by endoscopy: a multicentre, case-control, diagnostic study

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 l...

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Published in:	The lancet oncology 2019-12, Vol.20 (12), p.1645-1654
Main Authors:	Luo, Huiyan, Xu, Guoliang, Li, Chaofeng, He, Longjun, Luo, Linna, Wang, Zixian, Jing, Bingzhong, Deng, Yishu, Jin, Ying, Li, Yin, Li, Bin, Tan, Wencheng, He, Caisheng, Seeruttun, Sharvesh Raj, Wu, Qiubao, Huang, Jun, Huang, De-wang, Chen, Bin, Lin, Shao-bin, Chen, Qin-ming, Yuan, Chu-ming, Chen, Hai-xin, Pu, Heng-ying, Zhou, Feng, He, Yun, Xu, Rui-hua
Format:	Article
Language:	eng
Subjects:	Accuracy Artificial intelligence Clinical medicine Endoscopy Esophageal cancer Esophagus Gastric cancer Gastrointestinal cancer Hospitals Learning algorithms Medical diagnosis Medical prognosis Stomach cancer Studies Tumors
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Summary:	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. This multicentre, case-control, diagnostic study was done in six hospitals of different tiers (ie, municipal, provincial, and national) in China. The images of consecutive participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals. All patients with upper gastrointestinal cancer lesions (including oesophageal cancer and gastric cancer) that were histologically proven malignancies were eligible for this study. Only images with standard white light were deemed eligible. The images from Sun Yat-sen University Cancer Center were randomly assigned (8:1:1) to the training and intrinsic verification datasets for developing GRAIDS, and the internal validation dataset for evaluating the performance of GRAIDS. Its diagnostic performance was evaluated using an internal and prospective validation set from Sun Yat-sen University Cancer Center (a national hospital) and additional external validation sets from five primary care hospitals. The performance of GRAIDS was also compared with endoscopists with three degrees of expertise: expert, competent, and trainee. The diagnostic accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of GRAIDS and endoscopists for the identification of cancerous lesions were evaluated by calculating the 95% CIs using the Clopper-Pearson method. 1 036 496 endoscopy images from 84 424 individuals were used to develop and test GRAIDS. The diagnostic accuracy in identifying upper gastrointestinal cancers was 0·955 (95% CI 0·952–0·957) in the internal validation set, 0·927 (0·925–0·929) in the prospective set, and ranged from 0·915 (0·913–0·917) to 0·977 (0·977–0·978) in the five external validation sets. GRAIDS achieved diagnostic sensitivity similar to that of the expert endoscopist (0·942 [95% CI 0·924–0·957] vs 0·945 [0·927–0·959]; p=0·692) and superior sensitivity compared with competent (0·858 [0·832–0·880], p
ISSN:	1470-2045 1474-5488