NCT04087824

Deep Learning Algorithm for Recognition of Colonic Segments.

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

• Status: Unknown
• Phase: N/A
• Study type: Interventional
• Enrollment: 60 (estimated)
• Sponsor: Shandong University · Academic / Other
• Sex: All
• Age: 18 Years – 70 Years
• Healthy volunteers: Not accepted

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.

Detailed 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.

Conditions

• Colonic Diseases

Interventions

Type	Name	Description
DEVICE	AI assisted recognition of colonic segments	After receiving standard bowel preparation regimen, patients go through colonoscopy under the AI monitoring device. The whole withdrawal process is monitored by AI associated recognition system. Key colonic segments include ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum. When typical anatomic sites are detected, the AI device will automatically captured relevant images and report the name of each segment on the screen. The operating endoscopy expert will give the final answer and judge the performance of AI, which is set as a golden standard. Then all the AI captured images will be reviewed by human group, which consists of three to five experienced endoscopic physicians.

Timeline

• Start date: 2019-09-15
• Primary completion: 2019-11-15
• Completion: 2019-12-15
• First posted: 2019-09-12
• Last updated: 2019-09-12