Using machine‐learning algorithms to identify patients at high risk of upper gastrointestinal lesions for endoscopy. Issue 10 (10th May 2021)

• Record Type: Journal Article
• Authors: Liu, Yongjia; Lin, Da; Li, Lan; Chen, Yu; Wen, Jiayao; Lin, Yiguang; He, Xingxiang
• Is Part Of: Journal of gastroenterology and hepatology. Volume 36:Issue 10(2021)
• Page Start: 2735
• Page End: 2744
• Publication Date: 2021-05-10
• Subjects: artificial intelligence -- digestive diseases -- endoscopy -- machine‐learning -- upper gastrointestinal lesions; Gastroenterology -- Periodicals; Digestive organs -- Diseases -- Periodicals; Liver -- Diseases -- Periodicals; Gastroenterology -- Periodicals; Liver Diseases -- Periodicals; 616.33
• DOI: 10.1111/jgh.15530
• Languages: English
• ISSNs: 0815-9319

Abstract

Abstract: 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.