Clinical Research Directory

Clinical Evaluation of a New Platform for Bi-manual Endoscopic Resection in the Rectum and Sigma (EndoTEM)

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.

Liquid Biopsy and Machine Learning for Early Colorectal Cancer, Adenomas, Lynch Cancers, and Residual Disease Detection

This is an multicenter study that will test the diagnostic accuracy of a blood test (i.e., a liquid biopsy) for the diagnosis of colorectal cancer (CRC), advanced adenomas (AAs), as well as Lynch-syndrome associated cancers. Additionally, a pre-planned analysis will evaluate the use of this liquid biopsy as a tool for molecular residual disease monitoring purposes.

Evaluating Adenoma Recurrence After Endoscopic Mucosal Resection With Margin Marking or Post Treatment With Snare Tip Soft Coagulation

Non-inferiority trial comparing the recurrence rate of adenomas in non-pedunculated colonic lesions following endoscopic mucosal resection with margin marking (EMR-MM) and endoscopic mucosal resection with thermal margin ablation (EMR-STSC)

Artificial Intelligence-assisted Colonoscopy, Tandem Study

The goal of this clinical trial is to evaluate effect of artifial intelligent (AI) system, Endoscopy as AI-powered Device (ENAD) on adenoma miss rate from colonoscopy underwent by trainee endoscopist. It will also evaluate effect of AI on adenoma and polyp detection rate from colonoscopy underwent by trainee endoscopist. The main questions it aims to answer are: • Does AI-system lower adenoma miss rate in colonoscopy underwent by trainee endoscopist? Researchers will do the tandem colonoscopy and devided the participant in 4 groups as follows: A. First pass: trainee; Second pass: expert B. First pass: trainee + AI; Second pass: expert C. First pass: trainee; Second pass: expert + AI D. First pass: trainee+AI; Second pass: expert+AI Participants will take bowel preparation in split dose regimen and nothing per oral for 4 hours. They will underwent colonoscopy as above, with sedation by anesthesiologist. Details on qualities of colonoscopy, polyps detection and pathology results will be recorded.

Raman Spectroscopy-Based Deep Learning Model for Early Pan-Cancer Early Diagnosis

The goal of this observational study is to explore whether a Raman-based, deep learning-assisted approach can be used to develop an effective method for early pan-cancer screening. The study includes healthy individuals, patients at risk of cancer, and patients with diagnosed cancers. The main questions it aims to answer are: * Evaluating the deep-learning model's accuracy and specificity in identifying cancer-specific features in Raman spectral data and determining whether this method can accurately classify patients based on risk. * Identifying which model is more adaptable to the Raman spectrum * Providing an interpretable analysis of the model-generated diagnosis Participants are already being diagnosed and follow-up to determine the type of cancer.

Artificial Intelligence-assisted System in Colonoscopy

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.

Triple Assay of Rectal Mucus in the Diagnosis of Colorectal Cancer

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.

Prospective Study of EndoAim: ASUS AI Solution for Colorectal Polyp Diagnosis

"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."

The Impact of a Colonoscopy Monitoring Program on Endoscopists' Performance

The goal of this clinical trial is to learn if colonoscopy monitoring program works to adenoma detection rate in endoscopists. It will also learn about the impact on sessile serrated lesion detection rate, adenomas per colonoscopy, sessile serrated lesions per colonoscopy, advanced adenoma detection rate. The main questions it aims to answer are: Does colonoscopy monitoring program effect on adenoma detection rate in endoscopists? Participants will: Receive result of colonoscopy monitoring program or not every 3 months for 1 year