In recent years, with the continuous development of artificial intelligence, automatic polyp detection systems have shown its potential in increasing the colorectal lesions. Yet, whether this system can increase polyp and adenoma detection rates in the real clinical setting is still need to be proved. The primary objective of this study is to examine whether a combination of colonoscopy and a deep learning-based automatic polyp detection system is a feasible way to increase adenoma detection rate compared to standard colonoscopy.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Enrollment
1,118
When colonoscopists withdraw the colonoscopies and inspect the colons, the video streaming of colonoscopies was real-time switched to the automatic polyp detection system, which made it feasible to detect lesions in real time. When any potential polyp is detected by the system, there will be a tracing box on an adjacent monitor to locate the lesion with a simultaneous sound alarm.
Changhai Hospital, Second Military Medical University
Shanghai, China
RECRUITINGadenoma detection rate(ADR)
the number of patients with at least one adenoma divided by the total number of patients.
Time frame: 30 minutes
polyp detection rate(PDR)
the number of patients with at least one polyp divided by the total number of patients.
Time frame: 30 minutes
adenoma per colonoscopy
the number of adenomas detected during colonoscopy withdraw divided by the number of colonoscopies.
Time frame: 30 minutes
polyp per colonoscopy
the number of polyps detected during colonoscopy withdraw divided by the number of colonoscopies.
Time frame: 30 minutes
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