University College London
London, United Kingdom
NCT Number: NCT04325815
Background:
Colonoscopy is accepted to be the gold standard for screening of colorectal cancer (CRC). Most CRCs develop from adenomatous polyps, with colonoscopy accepted to be the gold standard for screening of CRC. An endoscopist's ability to detect polyps is assessed in the form of an Adenoma Detection Rate (ADR). Each 1.0% increase in ADR is associated with a 3.0% decrease in the risk of the patient developing an interval CRC. There remains a wide variation in endoscopist ADR.
More recently, the use of artificial intelligence (AI) and computer aided diagnosis in endoscopy has been gaining increasing attention for its role in automated lesion detection and characterisation. AI can potentially improve ADR, but previous AI related work has largely focused on retrospectively assessing still endoscopic images and selected video sequences which may be subject to bias and lack clinical utility. There are only limited clinical studies evaluating the effect of AI in improving ADR.
The CADDIE device uses convolutional neural networks developed for computer assisted detection and computer assisted diagnosis of polyps.
Primary objective: To determine whether the CADDIE artificial intelligence system improves endoscopic detection of adenomas during colonoscopy.
Primary endpoint: The difference in adenoma detection rate (ADR) between the intervention (supported with the CADDIE system) and non-intervention arm
Study design: Multi-Centre, open-label, randomised, prospective trial to assess efficacy and safety of the CADDIE artificial intelligence system for improving endoscopic detection of colonic polyps in real-time.
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Notify Me18 year and older
All sexes
Observational
London, United Kingdom
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
Time frame: 30 days
The difference in endoscopist ADR between the intervention (CADDIE system) and non-intervention arm.
Time frame: 30 days
Compare the difference in the number of adenomas detected per colonoscopy between the intervention and non-intervention arm
Time frame: 30 days
Polyp Detection Rate (including proximal polyp detection rate) in the interventional group compared to the control group.
Time frame: 30 days
Compare the accuracy of CADDIE against human endoscopist (high confidence diagnoses) for the optical diagnosis of diminutive polyps. Accuracy is defined as true positive and true negative divided by diagnoses total polyps that were optically diagnosed with high confidence.
Time frame: 30 days
Compare the accuracy of using CADDIE's optical diagnosis to assign surveillance colonoscopy intervals against using human endoscopist optical diagnosis (high confidence diagnoses) for assigning surveillance colonoscopy intervals.
Time frame: 30 days
Compare the accuracy of CADDIE against human endoscopist (high confidence diagnoses) for the optical diagnosis of diminutive rectal polyps.
Time frame: 30 days
Safety of the device will be assessed through monitoring of adverse events for 30 days' post-procedure. Adverse events are defined as:
Time frame: 30 days
Patient response to the question 'How anxious or calm were you during the colonoscopy?' assessed by Likert Scale with 5 options - 1 (Very anxious), 2 (Somewhat anxious), 3 (Neither Anxious nor calm), 4 (Somewhat calm), 5 (Very calm).
University College, London
Other
Multi-Centre, Open-label, Randomised, Prospective Trial to Assess Efficacy and Safety of the CADDIE Artificial Intelligence System for Improving Endoscopic Detection of Colonic Polyps in Real-time.
Acronym: CADDIE
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View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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