Deep Learning Algorithm for Recognition of Colonic Segments.

Colonic Diseases Clinical Trial

Official title: Development and Validation of a Deep Learning Algorithm for Real-time Recognition of Colonic Segments.

Status Not yet recruiting

Clinical Trial Summary

The purpose of this study is to develop and validate a deep learning algorithm to realize automatic recognition of colonic segments under conventional colonoscopy. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.

Clinical Trial Description

Colonoscopy is recommended as a routine examination for colorectal cancer screening. Complete inspection of all colon segments is the basis of colonoscopy quality control, and furthermore improves the detection rates of small adenomas. Recently, deep learning algorithm based on central neural networks (CNN) has shown multiple potential in computer-aided detection and computer-aided diagnose of gastrointestinal lesions. However, there is still a blank in recognition of anatomic sites, which restricts the realization of AI-aided lesions detection and disease severity scoring. This study aim to train an algorithm to recognize key colonic segments, and testify the accuracy of each segments recognition as compared to endoscopic physicians. ;

Related Conditions & MeSH terms

• Colonic Diseases

• NCT number: NCT04087824
• Study type: Interventional
• Source: Shandong University
• Contact: Xiuli Zuo, MD,PhD
• Phone: 15588818685
• Email: zuoxiuli@sdu.edu.cn
• Status: Not yet recruiting
• Phase: N/A
• Start date: September 15, 2019
• Completion date: December 15, 2019