Compare Between Computer-assisted Colonoscopy and Standard Colonoscopy Clinical Trial
Official title:
Computer-aided Detection With Deep Learning for Colorectal Adenoma During Colonoscopic Examination
We developed an artificial intelligent computer system with a deep neural network to analyze real-time video signals from the endoscopy station. This randomised controlled trial compared adenoma detection rate between computer-assisted colonoscopy and standard colonoscopy.
Colonoscopy is a primary screening and follow-up tool to detect colorectal cancer, a third
leading cause of cancer death in Taiwan. Most colorectal cancers (CRCs) arise from
preexisting adenomas, and the adenoma-carcinoma sequence offers an opportunity for the
screening and prevention of CRCs. The removal of adenomatous polyps can lower the incidence
of CRCs and result in reduced motality from CRCs. The adenoma detection rate, the proportion
of screening colonoscopies performed by a endoscopist that detect at least one colorectal
adenoma or adenocarcinoma, has been recommended as a quality indicator. The adenoma detection
rate was inversely associated with the risks of interval colorectal cancer, advanced-stage
interval cancer, and fatal interval cancer. However, adenoma detection rates vary widely
among endoscopists in both academic and community settings. Polyp miss rates as high as 20%
have been reported for high definition resolution colonoscopy. An improvement in adenoma
detection rate at screening colonoscopy, translates into reduced risks of interval colorectal
cancer and colorectal cancer death. Computer-aided detection of polyps might assist
endoscopists to reduce the miss rate and enhance screening performance during colonoscopy.
Computer-aided diagnosis and computer-aided detection are computerized systems that learn and
inference in medical fields. Computer-aided diagnosis has been developed in colon polyp
classification.
Computer-assisted image analysis has the potential to further aid adenoma detection but has
remained underdeveloped. A notable benefit of such a system is that no alteration of the
colonoscope or procedure is necessary. Machine learning with a deep neural network has been
successfully applied to many areas of science and technology, such as object recognition and
detection of computer vision, speech recognition, natural language processing. We developed
an artificial intelligent computer system (PX-1) with a deep neural network to analyze
real-time video signals from the endoscopy station. This randomised controlled trial compared
ADR between computer-assisted colonoscopy and standard colonoscopy.
;