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Showing 1-4 of 4 trials
NCT07146165
The goal of this clinical trial is to determine whether the use of the EndoTEM system during the endoscopic removal of polyps in the distal colon is feasible and safe. The main questions it aims to answer are: * Is the use of the EndoTEM system during the endoscopic removal of polyps in the distal colon feasible (i.e., does it enable complete resection of the polyp)? * Is the use of the EndoTEM system during the endoscopic removal of polyps in the distal colon safe? Participants will: * be treated with the EndoTEM system during the endoscopic submucosal dissection of polyps in the distal colon. * answere questionnaires on fecal continence and quality of life before and after the intervention. * be treated following standard clinical procedures before, during and after the endoscopic removal.
NCT06649123
Development of a multiomics assay for use on OriColTM sampled rectal mucus for detection of cancer and significant polyps in symptomatic patients on the Colorectal Urgent Suspected Cancer pathway.
NCT06656312
"The colorectal cancer mortality rate in Taiwan ranks third among all cancers, so it is crucial to prevent colorectal cancer through regular colonoscopy screenings and remove polyps with higher cancer risk. However, during colonoscopy, doctors tend to miss about 22% to 28% of polyps, and 20% to 24% of these missed polyps may turn into cancerous adenomas. Introducing an Artificial Intelligence (AI) assisted system can improve the overall quality of colonoscopy. This study aims to evaluate the effectiveness of the ASUS AI-assisted system (EndoAim) in diagnosing polyps during colonoscopy. It includes comparing the outcomes of colonoscopy with and without the use of EndoAim and assessing the impact of EndoAim on diagnostic effectiveness across different subgroups. Each participant will be randomly assigned to undergo a colonoscopy with or without the assistance of EndoAim. The performance of the AI-assisted system in colonoscopy will be comprehensively evaluated using indicators such as APC(Adenoma Per Colonoscopy), ADR(Adenoma Detection Rate), PDR(Polyp Detection Rate), and Positive Predictive Value (PPV).. A subgroup analysis will also be conducted based on several important factors. Polyps will be biopsied and sent for pathological examination, with the pathology report serving as the final diagnosis for subsequent analysis."
NCT03775811
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.