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Updated: Jul 15, 2025

Flexible Colonoscopy in Mice to Evaluate the Severity of Colitis and Colorectal Tumors Using a Validated Endoscopic Scoring System

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Clinical Validation Benchmark Dataset and Expert Performance Baseline for Colorectal Polyp Localization Methods.

Luisa F Sánchez-Peralta^1,2, Ben Glover³, Cristina L Saratxaga⁴

¹Jesús Usón Minimally Invasive Surgery Centre, E-10071 Cáceres, Spain.

Journal of Imaging

|September 27, 2023

Summary

Artificial intelligence aids in detecting colorectal cancer polyps, improving adenoma detection rates. A new dataset and expert baseline performance are established for validating AI systems before clinical trials.

Keywords:

artificial intelligence clinical validation colorectal cancer deep learning polyp detection polyp localization survey

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Area of Science:

Medical imaging
Artificial Intelligence
Gastroenterology

Background:

Colorectal cancer is a leading cause of death globally.
Early detection significantly improves survival rates.
Adenoma detection rate (ADR) is a key indicator of colonoscopy quality.

Purpose of the Study:

To establish a standardized laboratory setting for comparing AI and expert performance in polyp detection.
To introduce the ClinExpPICCOLO dataset for evaluating AI-driven polyp detection systems.
To provide a clinical expert performance baseline for AI validation.

Main Methods:

Development of the ClinExpPICCOLO dataset with 65 unedited endoscopic images (white light and narrow band imaging).
Inclusion of lesions not always centered in images to mimic real clinical scenarios.
Establishment of a performance baseline using 146 gastroenterologists to locate lesions.

Main Results:

Expert gastroenterologists achieved an accuracy of 77.74%.
Sensitivity and specificity for expert performance were 86.47% and 74.33%, respectively.
Statistically significant differences were observed between different experience groups.

Conclusions:

The established expert performance provides minimum benchmarks for AI/deep learning (DL) methods.
This study sets a precedent for standardized laboratory validation of AI in colonoscopy.
The findings support the potential of AI to enhance adenoma detection rates and colonoscopy quality.