An interventional study of AI assisted recognition of colonic segments in Colonic Diseases, sponsored by Shandong University. Status unknown. Open to participants aged 18 Years to 70 Years. Per ClinicalTrials.gov, last updated 2019-09-12.
Sponsored by Shandong University · Not applicable, Interventional, and Health services research
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.
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.
142 studies on the registry are indexed under Colonic Diseases; 22 are open to participants now.
This study's planned enrollment of 60 is below the median of 143 across 93 interventional studies indexed under Colonic Diseases.
Browse Colonic Diseases studies →Shandong University is the lead sponsor of 284 studies on the registry; 59 are open to participants now.
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Exclusion Criteria:
Patients in this group go through colonoscopy under the AI monitoring device.
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.
The accuracy of each colonic segment real-time recognition with deep learning algorithm.
The segmental recognition accuracy is the proportion of correctly recognized segments divided by the number of involved patients. The accuracy rate of ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum will be separately calculated.
Time frame: 3 months.
The accuracy of total colonic segments recognition with deep learning algorithm as compared to endoscopic experts group.
The total recognition accuracy is the proportion of correctly recognized images divided by the number of AI captured images. Then all AI captured images will be reviewed by experts group to give a human evaluating rate. Two rates will be compared by student t test to analyze the difference.
Time frame: 3 months.
No study locations are listed for this record.
This study is status unknown, as verified in Sep 2019. You cannot join it, but the record below documents what was studied.
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Shandong University