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Background and Aim: Endoscopic screening for early detection of upper gastrointestinal (UGI) lesions is important. However, population-based endoscopic screening is difficult to implement in populous countries. By identifying high-risk individuals from the general population, the screening targets can be narrowed to individuals who are in most need of an endoscopy. This study was designed to develop an artificial intelligence (AI)-based model to predict patient risk of UGI lesions to identify high-risk individuals for endoscopy., Methods: A total of 620 patients (from 5300 participants) were equally allocated into 10 parts for 10-fold cross validation experiments. The machine-learning predictive models for UGI lesion risk were constructed using random forest, logistic regression, decision tree, and support vector machine (SVM) algorithms. A total of 48 variables covering lifestyles, social-economic status, clinical symptoms, serological results, and pathological data were used in the model construction., Results: The accuracies of the four models were between 79.3% and 93.4% in the training set and between 77.2% and 91.2% in the testing dataset (logistics regression: 77.2%; decision tree: 87.3%; random forest: 88.2%; SVM: 91.2%;). The AUCs of four models showed impressive predictive ability. Comparing the four models with the different algorithms, the SVM model featured the best sensitivity and specificity in all datasets tested., Conclusions: Machine-learning algorithms can accurately and reliably predict the risk of UGI lesions based on readily available parameters. The predictive models have the potential to be used clinically for identifying patients with high risk of UGI lesions and stratifying patients for necessary endoscopic screening., (© 2021 Journal of Gastroenterology and Hepatology Foundation and John Wiley & Sons Australia, Ltd.)

Background and Aim: Gastric metaplasia (GM) of the duodenum is difficult to assess because of its patchy distribution, and the role of GM in functional dyspepsia (FD) is not clear. The aims of this study were to determine if endomicroscopy could identify GM of the duodenum and whether GM has associations with FD., Methods: A series of 51 patients with FD and 25 asymptomatic controls were enrolled. Confocal laser endomicroscopy was performed to evaluate villi changes in vivo. Targeted biopsy specimens were then compared with histopathological results., Results: The accuracy of the endomicroscopy diagnosis of GM during endoscopy was 92.8%, and the sensitivity, specificity, and positive and negative predictive values were 86.2%, 97.4%, 89.3%, and 96.6%, respectively. The mean κ-value for interobserver agreement was 0.89. GM in the duodenal bulb was more frequent in patients with FD than in the controls (33.3% vs 12%, P<0.05), especially in patients with epigastric pain syndrome (47.6% vs 12%, P<0.01)., Conclusions: Endomicroscopy is useful for identifying GM, and GM might be related to FD. These findings could have potential applicability for duodenal screening, and suggest a possible targeting therapy in FD., (© 2010 Journal of Gastroenterology and Hepatology Foundation and Blackwell Publishing Asia Pty Ltd.)