Post written by Wai K. Leung, MD, FRCP, from the Department of Medicine, Queen Mary Hospital, University of Hong Kong, Hong Kong.
This meta-analysis aims to summarize the performance of all recently published studies on the use of artificial intelligence (AI)-assisted endoscopic detection of gastric and esophageal neoplastic lesions as well as H. pylori infection.
With the rapid development on AI-assisted medical diagnostics, there is a pressing need to rationalize the latest data on the performance of AI-assisted endoscopic detection of upper-gastrointestinal (GI) lesions. Unlike detection of colonic pathology, which is rather restrictive to polyp detection, there are multiple upper-GI pathologies in the esophagus and stomach that would require independent analysis.
In this meta-analysis of 23 studies, including more than 960,000 endoscopic images, we found that the accuracy of AI detection, expressed as area under the hierarchical summary receiver-operating characteristic curve (AUC), of neoplastic lesions in the stomach, Barrett’s esophagus, and squamous esophagus and HP status were 0.96 (95% confidence interval [CI], 0.94-0.99), 0.96 (95% CI, 0.93-0.99), 0.88 (95% CI, 0.82-0.96), and 0.92 (95% CI, 0.88-0.97), respectively. The performance of the AI was also superior to endoscopists in the detection of neoplastic lesions in the stomach (AUC, 0.98 vs 0.87; P < 0.001), Barrett’s esophagus (AUC, 0.96 vs 0.82; P < .001), and HP status (AUC, 0.90 vs 0.82; P < 0.001).
Despite the promising performance of early studies on AI-assisted detection of upper-GI lesions, these results were largely based on retrospective reviews of selected images only. Moreover, most AI platforms were trained on detection of a single pathology rather than all upper-GI pathologies. Further multi-center prospective randomized studies are needed to verify the actual benefits of AI-assisted endoscopic detection of upper-GI neoplastic lesions.
Figure 1. Diagnostic performance of artificial intelligence on detection of neoplastic lesions in the stomach. TP, True positive; FP, false positive; TN, true negative; FN, false negative; AUROC, area under the receiver-operating characteristic curve.
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