# White Light, Magnifying Endoscopy, Endocytoscopy, and Artificial Intelligence in Diagnosis of Early Colorectal Cancer: A Comparative Study

## TL;DR

This study compares different endoscopic techniques and AI to diagnose early colorectal cancer, finding that AI improves accuracy and confidence in identifying cancer depth.

## Contribution

The study demonstrates that AI-assisted diagnosis significantly enhances the accuracy and confidence of endoscopists in assessing cancer invasion depth.

## Key findings

- AI-assisted diagnosis achieved the highest accuracy (88.9%) and specificity (93.1%) in predicting cancer invasion depth.
- The proportion of high-confidence readings increased from 40.2% with white light imaging to 75.5% with AI assistance.
- Trainee endoscopists showed the most significant improvement in confidence when using AI support.

## Abstract

Early detection of colorectal cancer is critical for improving prognosis. However, assessing invasion depth—distinguishing between superficial cancer (T1a) and deep submucosal invasive cancer (T1b)—remains challenging. Recently, artificial intelligence (AI)‐assisted computer‐aided diagnosis (CADx) systems have been introduced to complement conventional endoscopy. This study aims to compare the diagnostic accuracy of endoscopists in predicting deep submucosal invasion in early colorectal cancer under four modalities: white‐light imaging (WLI), magnifying endoscopy (including narrow‐band imaging magnification and pit pattern), endocytoscopy (EC), and CADx support.

We conducted a single‐center retrospective study using stored endoscopic images between April 2021 and December 2022. Each lesion was evaluated using white light imaging, magnifying endoscopy, EC, and CADx analysis with the EndoBRAIN‐Plus system. Trainee and expert endoscopists assessed the images sequentially, recording their estimations of invasion depth (T1a vs. T1b) and confidence levels. Sensitivity, specificity, and accuracy were calculated against the pathological reference. We compared performances stratified by confidence level and endoscopist experience.

During the study period, 66 lesions were eligible. Of them, 27% (18 lesions) were T1b cancers. Diagnostic accuracy improved progressively from white light imaging (82.7% [95% confidence interval {95%CI}: 81.2–86.9]) to EC (85.6% [95%CI: 82.7–88.2]). The highest specificity and accuracy were achieved when AI‐assisted diagnosis was incorporated (accuracy: 88.9% [95%CI: 86.3–91.2], specificity: 93.1% [95%CI: 90.6–95.2]). The proportion of high‐confidence readings rose from 40.2% to 75.5%. This was most pronounced in the trainee group.

Integrating advanced endoscopic imaging with CADx significantly improved accuracy in assessing invasion depth. This approach may guide treatment decisions in early‐stage colorectal cancer.