Edge Artificial Intelligence Device in Real-Time Endoscopy for Classification of Gastric Neoplasms: Development and Validation Study.

ABSTRACT:

Objective

We previously developed artificial intelligence (AI) diagnosis algorithms for predicting the six classes of stomach lesions. However, this required significant computational resources. The incorporation of AI into medical devices has evolved from centralized models to decentralized edge computing devices. In this study, a deep learning endoscopic image classification model was created to automatically categorize all phases of gastric carcinogenesis using an edge computing device.

Design

A total of 15,910 endoscopic images were collected retrospectively and randomly assigned to train, validation, and internal-test datasets in an 8:1:1 ratio. The major outcomes were as follows: 1. lesion classification accuracy in six categories: normal/atrophy/intestinal metaplasia/

SUBMITTER: Gong EJ 

PROVIDER: S-EPMC11672907 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature