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Updated: Jan 15, 2026

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Imagen de luz blanca frente a imagen de luz blanca asistida por inteligencia artificial para la detección de

Carlos Eduardo Oliveira Dos Santos¹, Cadman Leggett², Prateek Sharma³

¹Department of Endoscopy, Santa Casa de Caridade Hospital, Bagé, RS, Brazil.

Revista de gastroenterologia del Peru : organo oficial de la Sociedad de Gastroenterologia del Peru

|January 13, 2026

Abstracto

Introduction:

Adenoma detection rate (ADR) and sessile serrated lesion (SSL) detection rate (SDR) are crucial quality indicators for colonoscopy, as their improvement contributes to effective prevention of colorectal cancer. Artificial intelligence (AI) has been shown to significantly increase ADR. This study compared white light imaging (WLI) versus AI-assisted WLI for neoplasia detection.

Materials And Methods:

This was a prospective, randomised trial of screening, surveillance, and symptomatic patients. Our primary objective was to evaluate ADR. Secondary objectives included SDR, mean number of adenomas per patient (MAP), neoplasia detection rate (NDR), advanced ADR (AADR), and colonoscope withdrawal time.

Results:

A total of 621 adenomas were diagnosed in 711 patients, with 310 adenomas in the WLI group and 311 adenomas in the WLI+AI group (p=0.65). Eighty-three SSLs and two intramucosal carcinomas were also detected, totalling 706 neoplasms. ADR was 45.9% in the WLI group and 50.8% in the WLI+AI group (p=0.20). ADR was 54.4% for screening, 49.0% for surveillance, and 40.0% for symptomatic patients (p=0.01). Marginal significance was observed in the WLI+AI group for screening patients (61.5% vs. 49.2%, p=0.06). SDR was 9.0% for both groups. MAP (0.9 vs. 0.9, p=0.34), NDR (51.0% vs. 56.8%, p=0.13), and AADR (8.4% vs. 7.6%, p=0.78) did not differ significantly between the groups. Withdrawal time was similar for the WLI (12.4 ± 5.1 min) and WLI+AI (12.2 ± 4.1 min) groups (p=0.32).

Conclusions:

AI-assisted colonoscopy demonstrated high ADR and NDR. While without statistical relevance overall, marginal significance was observed for screening patients.

Palabras clave:

colonoscopia inteligencia artificial detección de cáncer neoplasias colorrectales imagen de luz blanca

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