Autonomous Artificial Intelligence Versus AI Assisted Human Optical Diagnosis
NCT Number: NCT06543862
Acronym: CADx-RCT
Status: NOT_YET_RECRUITING
Study Type: INTERVENTIONAL
Phases: NA
Conditions: Colonic Polyp|Artificial Intelligence
Interventions: OTHER: CADx (AI) system
Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)
Collaborators: Roupen Djinbachian, MD|Mahsa Taghiakbari, MD PHD
Sex: ALL
Age: ADULT, OLDER_ADULT
Enrollment: 400
Study Design: Allocation: RANDOMIZED|Intervention Model: PARALLEL|Masking: SINGLE (PARTICIPANT)|Primary Purpose: DIAGNOSTIC
Start Date: 2024-08-15
Completion Date: 2024-08-15
Locations: Ghislaine Ahoua, Montréal, Quebec, Canada

Summary:
Computer-aided image-enhanced endoscopy can predict the nature of colorectal polyps with over 90% accuracy. This technology uses artificial intelligence (AI) to analyze video recordings of polyps, learning to make diagnoses in real-time. This means that doctors can get immediate predictions about small polyps during the procedure, reducing the need for separate pathology exams and saving costs, ultimately improving patient care.

Traditionally, non-computer-aided diagnosis of polyps varies greatly between doctors. AI can help reduce this variability by providing consistent and accurate computer-aided diagnoses (CADx). In the past, CADx systems could only tell if polyps were cancerous or not. However, recent advancements show that current CADx systems are as accurate as doctors. For widespread use, these systems need to recognize different types of polyps, including serrated ones, which are more challenging to identify. Newer CADx systems are being developed to recognize these serrated polyps, but they still need further validation for autonomous use.

Primary Outcome Measures: Accuracy of autonomous CADx system (AI-A) optical diagnosis compared with the real-time CADx diagnosis (AI-H), Accuracy of AI-A optical diagnosis compared with AH, when histopathology results are used as the reference, up to 100 weeks
Secondary Outcome Measures: Test characteristics, including recall, specificity, positive and negative predictive values (PPV/NPV), and particularly the NPV of rectosigmoid neoplastic polyps., A ≥90% NPV will be used as a quality benchmark for a strategy to not resect such diminutive polyps., up to 100 weeks|Agreement of surveillance interval recommendations of AI-A and AI-H compared with the pathology-based recommendations, For surveillance interval assignment, the pathology results of concomitant polyps \>5 mm (including multiple concomitant polyps of all sizes and histology) will be considered when calculating the surveillance interval recommendation. Surveillance recommendations will be based on the 2020 United States Multi Society Task Force Guidelines as is current standard of practice at our center., up to 100 weeks|Proportion of patients for whom an immediate surveillance recommendation can be directly provided for each approach, and how often histopathology-based polyp examination would have been avoided., The potential cost-effectiveness of OD (either approaches) will be evaluated using the measured described above., up to 100 weeks|Variability of OD (AI-A and AI-H) across participating endoscopists., Each participating endoscopist will conduct a similar number of optical diagnoses to assess endoscopist-related factors., up to 100 weeks|Cost-effectiveness of OD ((AI-A and AI-H), A cost-effectiveness model will be applied to better quantify costs and understand cost impact including key cost drivers when generalized to a broader screening population., up to 100 weeks