Computer-aided endoscopic diagnostic system modified with hyperspectral imaging for the classification of esophageal neoplasms.

ABSTRACT:

Introduction

The early detection of esophageal cancer is crucial to enhancing patient survival rates, and endoscopy remains the gold standard for identifying esophageal neoplasms. Despite this fact, accurately diagnosing superficial esophageal neoplasms poses a challenge, even for seasoned endoscopists. Recent advancements in computer-aided diagnostic systems, empowered by artificial intelligence (AI), have shown promising results in elevating the diagnostic precision for early-stage esophageal cancer.

Methods

In this study, we expanded upon traditional red-green-blue (RGB) imaging by integrating the YOLO neural network algorithm with hyperspectral imaging (HSI) to evaluate the diagnostic efficacy of this innovative AI system for superficial esophageal neoplasms. A total of

SUBMITTER: Wang YK 

PROVIDER: S-EPMC11646837 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature