View clinical trials related to Adenoma Colon Polyp.
Filter by:In recent years, computer-aided diagnosis system based on artificial intelligence (AI) has been used in colorectal polyp detection. In recent years, computer-aided diagnosis system based on artificial intelligence (AI) has been used in colorectal polyp detection. However, whether AI-assisted can improve the adenoma-detection rate (ADR) is inconclusive. This study aims to evaluate the real-world performance of an AI system that combines polyp detection with colonoscopy quality control. This study aims to explore the clinical application value of AI-based polyp detection and quality control function by comparing the data of polyp detection rate and adenoma detection rate in multiple centers with and without AI-assisted colonoscopy in a multicenter, prospective real world study. However, whether AI-assisted can improve the adenoma-detection rate (ADR) is inconclusive. This study aims to evaluate the real-world performance of an AI system that combines polyp detection with colonoscopy quality control. This study aims to explore the clinical application value of AI-based polyp detection and quality control function by comparing the data of polyp detection rate and adenoma detection rate in multiple centers with and without AI-assisted colonoscopy in a multicenter, prospective real world study.
Our group, prior to the present study, developed a handcrafted predictive model based on the extraction of surface patterns (textons) with a diagnostic accuracy of over 90%24. This method was validated in a small dataset containing only high-quality images. Artificial intelligence is expected to improve the accuracy of colorectal polyp optical diagnosis. We propose a hybrid approach combining a Deep learning (DL) system with polyp features indicated by clinicians (HybridAI). A pilot in vivo experiment will carried out.
Although colonoscopy with polypectomy can prevent up to 80% of colorectal cancers, a significant adenoma miss rate still exists, particularly in the right colon. Optimizing the detection of adenomas and sessile serrated lesions in the right colon is crucial to increase the effectiveness of colonoscopy in colorectal cancer prevention. Last generation Fuji videocolonscopes incorporates the Linked Color Imaging (LCI), a recently developed technology that differentiates the red colour spectrum more effectively than White Light imaging thanks to its optimal pre-process composition of light spectrum and advanced signal processing. The increased colour contrast results in more accurate delineation of abnormal inflammatory or neoplastic findings of colonic mucosa. Preliminary data suggest that LCI may be improve the detection of neoplastic lesion of colon. The investigators performe a tandem prospective study to compare the right colon adenoma miss rates of LCI colonoscopy with those of conventional white light colonoscopy. Therefore participants scheduled for colonoscopy for the assessment of symptoms or for colorectal cancer screening/surveillance receive the examination of the right colon twice, in a back to back fashion, with standard white light (WL) and with LCI. Patients are randomly assigned (1:1), via computer-generated randomisation with block size of 20, to which procedure is done first. The endoscopist are masked to group allocation until immediately before the cecum is reached. Examinations are performed with Fuji videocolonscopes series 700 (EC-760R, EC-760ZP).