BACKGROUND
<i>Helicobacter pylori</i> plays a central role in the development of gastric cancer, and prediction of <i>H pylori</i> infection by visual inspection of the gastric mucosa is an important function of endoscopy. However, there are currently no established methods of optical diagnosis of <i>H pylori</i> infection using endoscopic images. Definitive diagnosis requires endoscopic biopsy. Artificial intelligence (AI) has been increasingly adopted in clinical practice, especially for image recognition and classification.
OBJECTIVE
This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of <i>H pylori</i> infection using endoscopic images.
METHODS
Two independent evaluators searched core databases. The inclusion criteria included studies with endoscopic images of <i>H pylori</i> infection and with application of AI for the prediction of <i>H pylori</i> infection presenting diagnostic performance. Systematic review and diagnostic test accuracy meta-analysis were performed.
RESULTS
Ultimately, 8 studies were identified. Pooled sensitivity, specificity, diagnostic odds ratio, and area under the curve of AI for the prediction of <i>H pylori</i> infection were 0.87 (95% CI 0.72-0.94), 0.86 (95% CI 0.77-0.92), 40 (95% CI 15-112), and 0.92 (95% CI 0.90-0.94), respectively, in the 1719 patients (385 patients with <i>H pylori</i> infection vs 1334 controls). Meta-regression showed methodological quality and included the number of patients in each study for the purpose of heterogeneity. There was no evidence of publication bias. The accuracy of the AI algorithm reached 82% for discrimination between noninfected images and posteradication images.
CONCLUSIONS
An AI algorithm is a reliable tool for endoscopic diagnosis of <i>H pylori</i> infection. The limitations of lacking external validation performance and being conducted only in Asia should be overcome.
CLINICALTRIAL
PROSPERO CRD42020175957; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=175957