Development and validation of a real-time artificial intelligence-assisted system for detecting early gastric cancer: A multicentre retrospective diagnostic study.

ABSTRACT:

Background

We aimed to develop and validate a real-time deep convolutional neural networks (DCNNs) system for detecting early gastric cancer (EGC).

Methods

All 45,240 endoscopic images from 1364 patients were divided into a training dataset (35823 images from 1085 patients) and a validation dataset (9417 images from 279 patients). Another 1514 images from three other hospitals were used as external validation. We compared the diagnostic performance of the DCNN system with endoscopists, and then evaluated the performance of endoscopists with or without referring to the system. Thereafter, we evaluated the diagnostic ability of the DCNN system in video streams. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value and Cohen's kappa coeffi

SUBMITTER: Tang D 

PROVIDER: S-EPMC7708824 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature