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Optimizing Colorectal Polyp Screening: A Novel Artificial Intelligence-Assisted Colonoscopy Diagnostic System Based

Lin Lin^1,2, Song Yan¹, Xu Xin¹

¹Department of Gastroenterology and Hepatology, Tianjin Medical University General Hospital, Tianjin, China.

Journal of Gastroenterology and Hepatology

|December 4, 2025

Summary

This summary is machine-generated.

The new CAD-N-Pro system significantly improves artificial intelligence-assisted colonoscopy accuracy for detecting colorectal polyps. This AI tool enhances diagnostic performance, aiding clinicians in real-time decision-making during colonoscopies.

Keywords:

NICE classification artificial intelligence colorectal polyps computer‐aided diagnosis optical diagnosis

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Area of Science:

Gastroenterology
Medical Imaging
Artificial Intelligence

Background:

Colonoscopy is crucial for colorectal cancer screening.
Accurate polyp classification is essential for effective treatment.
Existing AI systems require further optimization for clinical use.

Purpose of the Study:

To evaluate the diagnostic performance of an enhanced AI-assisted colonoscopy system, CAD-N-Pro.
To compare CAD-N-Pro's performance against the previous CAD-N system and endoscopists.
To validate the clinical applicability of CAD-N-Pro in real-time colonoscopy.

Main Methods:

Developed CAD-N-Pro using a segmentation network based on the NICE classification.
Trained and validated the model on 14,675 images from five hospitals.
Conducted prospective analysis of 200 colonoscopy videos, comparing AI performance with endoscopists of varying experience levels.

Main Results:

CAD-N-Pro achieved high diagnostic accuracy in external image validation (overall AUC 0.979).
The system demonstrated superior performance to endoscopists in detecting smaller colorectal polyps (<10 mm).
CAD-N-Pro's performance was comparable to experienced endoscopists for larger polyps (>10 mm).

Conclusions:

The optimized CAD-N-Pro model significantly enhances optical diagnostic accuracy for colorectal polyps.
CAD-N-Pro serves as a robust tool for real-time clinical decision-making during colonoscopies.
This AI system has the potential to improve colonoscopy outcomes and patient care.