Artificial intelligence for early gastric cancer boundary recognition in NBI and nF-NBI endoscopic images.

OBJECTIVES

Precise delineation of early gastric cancer (EGC) margins is essential for complete resection during endoscopic submucosal dissection. This study aimed to develop deep learning-based models for EGC boundary detection in narrow-band imaging (NBI) and near-focus NBI (NF-NBI) images.

METHODS

A total of 1215 NBI and 1646 NF-NBI images from EGC patients were used to train three convolutional neural networks (CNN1-CNN3), generating six deep learning models (Model1-Model6). Segmentation performance was compared among models and endoscopists of varying seniority.

RESULTS

On NBI images, Model3 achieved an accuracy of 0.9348, compared to 0.7272, 0.7277, and 0.9435 for junior, intermediate, and senior endoscopists, respectively. The corresponding Dice coefficients were 0.8310 (95% CI, 0.8120-0.8500), 0.6153 (95% CI, 0.5827-0.6480), 0.6528 (95% CI, 0.6237-0.6819), and 0.8360 (95% CI, 0.8169-0.8550), with recall values of 0.9773, 0.6845, 0.7596, and 0.9784, respectively. On NF-NBI images, Model6 showed an accuracy of 0.9483, compared to 0.6885 (junior), 0.7826 (intermediate), and 0.9621 (senior endoscopists). Dice coefficients were 0.8526 (95% CI, 0.8410-0.8642), 0.6757 (95% CI, 0.6569-0.6944), 0.7161 (95% CI, 0.6941-0.7382), and 0.8618 (95% CI, 0.8512-0.8725), with recall values of 0.9831, 0.8095, 0.8317, and 0.9889, respectively.

CONCLUSIONS

The proposed deep learning models accurately delineated EGC boundaries in NBI and NF-NBI images, achieving diagnostic performance comparable to that of senior endoscopists.