Benefit of Linked-Color Imaging in Artificial Intelligence-Assisted Diagnosis of Early Gastric Cancer: A Pilot Study With Propensity Score Adjustment.

BACKGROUND AND AIMS: Artificial intelligence (AI)-assisted endoscopy represents a promising approach for lesion detection, yet frequent false-positive detections impair clinical utility by disrupting examinations and diminishing physician confidence. Linked-color imaging (LCI), an image-enhanced endoscopy technique that amplifies mucosal and vascular contrast, may address this limitation. This investigation evaluated whether LCI reduces false-positive AI detections compared with white-light imaging (WLI). METHODS: This retrospective study analyzed consecutive AI-assisted upper endoscopies performed between March 2024 and June 2025. WLI and LCI were performed sequentially within the same endoscopic session in each patient. False-positive AI detections were compared between modalities using two computer-aided detection (CAD) versions. Propensity score adjustment was used as a sensitivity analysis for baseline differences between CAD Versions I and II. RESULTS: Of 66 initially screened cases, 63 remained after excluding patients with prior gastric surgery. LCI reduced false-positive AI detections compared with WLI (median 2 vs. 5; p < 0.001). In CAD version-stratified sensitivity analyses, LCI reduced false-positive AI detections in both Version I (5 to 2; p = 0.01) and Version II (2 to 0; p = 0.03). This reduction remained consistent across atrophic grades. Both imaging modalities identified all gastric lesions, achieving 100% detection sensitivity. CONCLUSIONS: LCI assessment performed after WLI observation yielded fewer false-positive CAD-EYE detections while maintaining lesion detection sensitivity. However, because the observation sequence was fixed, these findings should be interpreted cautiously and require confirmation in prospective or counterbalanced studies. UNLABELLED: Trial Registration: N/A (retrospective study).