View clinical trials related to Adenoma Colon.
Filter by:The purpose of the study is to assess whether the AI characterisation system of the CADDIE device improves the endoscopists accuracy in the optical diagnosis of diminutive colorectal polyps in the bowel during colonoscopy. Participants will either have a colonoscopy with the assistance of the CADDIE device characterisation AI system ("intervention group") or have a colonoscopy in line with routine clinical practice i.e., without the CADDIE device characterisation AI system ("control group"). The randomisation method of this trial will allocate enrolled participants to the "intervention" group and to the "control" group by a technique similar to flipping a coin.
PURPOSE OF PROTOCOL Objective: To evaluate if the speedometer based on the real-time image analysis can help endoscopists increase their withdrawal time, which is defined as time spent examining the colon during withdrawal of the colonoscope, during colonoscopy. Hypothesis: The trial hypothesis is that use of the speedometer during colonoscopy will increase the average withdrawal time, which is defined as time spent examining the colon during withdrawal of the colonoscope, by 1.6 minutes, possibly increasing the performance of the participating endoscopists. Our objective is to clarify the clinical benefits of this digital tool in colonoscopy. Endpoint: Withdrawal time difference between colonoscopies done without the speedometer (control period) and colonoscopies done with the speedometer (intervention period).
Colorectal cancer is one of the most common cancer in Hong Kong. In 2018, CRC accounted for 17.4%, 5,780 cases, of the total new cancers. CRC claimed 2,279 lives (15.8%) making it the second most deadly killer in the population. Since 2010, the Cancer Expert Working Group (CEWG) has recommended that asymptomatic average-risk individuals aged 50 to 75 years should consider one of the screening methods: fecal occult blood test (FOBT) every one to two years; OR flexible sigmoidoscopy every 5 years; OR colonoscopy every 10 years. However, it poses great challenges for large scale CRC screening using colonoscopy, such as bowel preparation difficulties, complications of procedure and poor compliance. ColoClear® is intended for use as an adjunctive screening test for the detection of colorectal neoplasia associated DNA markers and for the presence of occult hemoglobin in human stool. It has the potential of increasing the sensitivity of detecting CRC as compared to FOBT or faecal immunochemical test (FIT), which detects the presence of hemoglobin in stool alone. A positive result may indicate the presence of colorectal cancer or pre-malignant colorectal neoplasia. ColoClear® is not intended as a replacement for diagnostic colonoscopy. A positive result in ColoClear®, as with any screening test, should be followed by colonoscopy. ColoClear® is intended for colorectal cancer screening in average risk individuals: adults of either sex, 40 years or older, who are at high risk for colorectal cancer.
The aim of the study is to develop a computer program which is able to classify different entities of colorectal polyps on the basis of optical polyp features. In the end, the computer program shall differentiate between (i) hypeplastic polyps, (ii) adenomas and (iii) serrated adenomas . In the first phase of the study a computer program will be established which aims to distinguish between the above mentions entities on the basis of optical features derived from still images. A machine learning apporach will be used for creating the program. Afterwards, in a second phase of the study, still images of 100 polyps (not used in the first phase) will be presented to the computer program. Quality of the computer program will be tested by calculating the accuracy for differentiating the three different polyp types. The gold standard for true polyp diagnoses will be based on histopathological diagnoses of the polyps. The same pictures of 100 polyps will also be presented to human experts. Experts will also predict histopathological diagnoses on the basis of optical polyps featurs. Accuracy of computer-decisions and human expert predictions will be compared. The establishment of a well- functioning computer program is the primary aim of the study.
Colorectal cancer (CRC) is the third most common type of cancer among men and the second among women in Brazil. Despite the high incidence and significance of CRC in Brazil, very little is known about its prevalence among the asymptomatic population. Recently, a CRC screening program was implemented at the Cancer Hospital of Barretos. Characterization of the clinical findings detected in the screening population and the prevalence of basal CRC might contribute to better organization of the program and define the best strategy for a future national screening program. We hypothesize that recruitment and the early outcomes of our screening program based on the fecal immunochemical test (FIT) will differ from the outcomes corresponding to other populations due to sociodemographic differences. Aims: i. To implement a data collection and storage system for follow-up of the screening program participants and to measure early outcomes (adenoma, advanced adenoma and cancer) and associate them with sociodemographic risk factors; ii. to quantify the risk of CRC in the Brazilian population and to develop algorithms for risk stratification of CRC screening; and iii. to compare the risk stratification to other countries with low, medium and high incomes. Methods: Individuals aged 50 to 65 years will be included in the HCB screening program from November 2017 to December 2018. The following data will be collected from all participants: sociodemographic and ethnic (skin color) characteristics; risk factors for CRC, such as smoking and drinking; comorbidities, including diabetes mellitus and arterial hypertension; and FIT, colonoscopy and histopathology examination results. Data collection will be performed using the REDCap data collection/database system. The risk score will be formulated using the Chi-square test (or Fisher's exact test) and simple logistic regression, and the regression coefficients will be calculated. Then, the model identified for the training sample will be replicated with a validation sample. The resulting score will be used to calculate the sensitivity, specificity, positive predictive value, negative predictive value, accuracy, area under the receiver operating characteristic (ROC) curve and Kolmogorov D statistic.