Diagnostic value of real-time computer-aided detection for precancerous lesion during esophagogastroduodenoscopy: A meta-analysis

Abstract

BACKGROUND

Early detection of precancerous lesions is of vital importance for reducing the incidence and mortality of upper gastrointestinal (UGI) tract cancer. However, traditional endoscopy has certain limitations in detecting precancerous lesions. In contrast, real-time computer-aided detection (CAD) systems enhanced by artificial intelligence (AI) systems, although they may increase unnecessary medical procedures, can provide immediate feedback during examination, thereby improving the accuracy of lesion detection. This article aims to conduct a meta-analysis of the diagnostic performance of CAD systems in identifying precancerous lesions of UGI tract cancer during esophagogastroduodenoscopy (EGD), evaluate their potential clinical application value, and determine the direction for further research.

AIM

To investigate the improvement of the efficiency of EGD examination by the real-time AI-enabled real-time CAD system (AI-CAD) system.

METHODS

PubMed, EMBASE, Web of Science and Cochrane Library databases were searched by two independent reviewers to retrieve literature with per-patient analysis with a deadline up until April 2025. A meta-analysis was performed with R Studio software (R4.5.0). A random-effects model was used and subgroup analysis was carried out to identify possible sources of heterogeneity.

RESULTS

The initial search identified 802 articles. According to the inclusion criteria, 2113 patients from 10 studies were included in this meta-analysis. The pooled accuracy difference, logarithmic difference of diagnostic odds ratios, sensitivity, specificity and the area under the summary receiver operating characteristic curve (area under the curve) of both AI group and endoscopist group for detecting precancerous lesion were 0.16 (95%CI: 0.12-0.20), -0.19 (95%CI: -0.75-0.37), 0.89 (95%CI: 0.85-0.92, AI group), 0.67 (95%CI: 0.63-0.71, endoscopist group), 0.89 (95%CI: 0.84-0.93, AI group), 0.77 (95%CI: 0.70-0.83, endoscopist group), 0.928 (95%CI: 0.841-0.948, AI group), 0.722 (95%CI: 0.677-0.821, endoscopist group), respectively.

CONCLUSION

The present studies further provide evidence that the AI-CAD is a reliable endoscopic diagnostic tool that can be used to assist endoscopists in detection of precancerous lesions in the UGI tract. It may be introduced on a large scale for clinical application to enhance the accuracy of detecting precancerous lesions in the UGI tract.

Keywords: Artificial intelligence; Real-time computer-aided detection system; Precancerous lesion; Esophagogastroduodenoscopy; Endoscopy; Upper gastrointestinal tract; Diagnostic performance; Meta-analysis

Core Tip: This meta-analysis indicates that the artificial intelligence-enabled real-time computer-aided detection system (AI-CAD) system is superior to endoscopists in detecting precancerous lesions of the upper gastrointestinal (UGI) tract. Its sensitivity, specificity, and diagnostic accuracy are higher, which is helpful in improving lesion recognition ability and may reduce the rate of missed diagnoses. These findings support the clinical potential of integrating AI-CAD into routine endoscopy practice to enhance the early detection and prevention of UGI cancers.