Accuracy of artificial intelligence-assisted detection of esophageal cancer and neoplasms on endoscopic images: A systematic review and meta-analysis.

OBJECTIVE

To investigate systematically previous studies on the accuracy of artificial intelligence (AI)-assisted diagnostic models in detecting esophageal neoplasms on endoscopic images so as to provide scientific evidence for the effectiveness of these models.

METHODS

A literature search was conducted on the PubMed, EMBASE and Cochrane Library databases for studies on the AI-assisted detection of esophageal neoplasms on endoscopic images published up to December 2020. A bivariate mixed-effects regression model was used to calculate the pooled diagnostic efficacy of AI-assisted system. Subgroup analyses and meta-regression analyses were performed to explore the sources of heterogeneity. The effectiveness of AI-assisted models was also compared with that of the endoscopists.

RESULTS

Sixteen studies were included in the systematic review and meta-analysis. The pooled sensitivity, specificity, positive and negative likelihood ratios, diagnostic odds ratio and area under the summary receiver operating characteristic curve regarding AI-assisted detection of esophageal neoplasms were 94% (95% confidence interval [CI] 92%-96%), 85% (95% CI 73%-92%), 6.40 (95% CI 3.38-12.11), 0.06 (95% CI 0.04-0.10), 98.88 (95% CI 39.45-247.87) and 0.97 (95% CI 0.95-0.98), respectively. AI-based models performed better than endoscopists in terms of the pooled sensitivity (94% [95% CI 84%-98%] vs 82% [95% CI 77%-86%, P < 0.01).

CONCLUSIONS

The use of AI results in increased accuracy in detecting early esophageal cancer. However, most of the included studies have a retrospective study design, thus further validation with prospective trials is required.