Artificial Intelligence Clinical Trial
Official title:
Application of Artificial Intelligence for Early Diagnosis of Gastric Cancer During Optical Enhancement Magnifying Endoscopy
Previous prospective randomized controlled study demonstrated higher accuracy rate of diagnosing early gastric cancers by Magnifying image-enhanced endoscopy than conventional white-light endoscopy. Nevertheless, it is difficult to differentiate early gastric cancer from noncancerous lesions for beginner. we developed a new computer-aided system to assist endoscopists in identifying early gastric cancers in magnifying optical enhancement images.
Gastric cancer is the third most common cause of cancer-associated deaths worldwide
especially in Asia.Early detection and treatment would cure the disease with 5-year survival
rate greater than 90%.However, the sensitivity of conventional endoscopy with white-light
imaging (C-WLI) in diagnosis of early gastric cancers (EGCs) is merely 40%. Magnifying
image-enhanced endoscopy (IEE) techniques such as magnifying narrow band imaging (M-NBI) have
been developed and 2 RCT report that white-light imaging combine with M-NBI can increase the
sensitivity to 95%. The strategy that using white-light imaging to detect the suspicious
lesion and using M-IEE techniques to make a diagnosis of early gastric cancer is recommend in
screening endoscopy.
Optical enhancement (OE) which is one of the M-IEE techniques was developed by HOYA Co.
(Tokyo, Japan) . This technology combines digital signal processing and optical filterers to
clear display of mucosal microsurface (MS) and microvessel (MV). The advantage of OE is to
overcome the darkness of NBI which leads to less usefulness for detect-ability in the full
extended gastrointestinal lumen.Nevertheless, it is difficult to differentiate early gastric
cancer from noncancerous lesions for beginner, and expertise with sub-optimal inter-observer
agreement is essential for the use of M-IEE.
Nowadays, Artificial intelligence (AI) using deep machine learning has made a major
breakthrough in gastroenterology, which using gradient descent method and backpropagation to
automatically extract specific images features. The diagnostic accuracy in identifying upper
gastrointestinal cancer was 0.955 in C-WLI . Polyps can be identified in real time with 96%
accuracy in screening colonoscopy. AI show an outstanding application in detection and
diagnosis.
This study aims to develop a M-OE assistance model in the diagnosis of EGCs by distinguishing
cancer or not.
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