Artificial Intelligence, Colonic Polyp
Conditions
Keywords
optical diagnosis, artificial intelligence
Brief 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. Human and AI interactions are complex and a framework to reap synergistic effects CADx systems when used by humans to harness optimal performance needs to be established. AI solutions in medicine are usually developed to be used as assistive devices, however, then they rely on humans to correct AI errors. Optical polyp diagnosis is a complex task. Non experts usually achieve diagnostic accuracy in 70-80%. CADx systems have a similar diagnostic accuracy when used autonomously. Clinical evaluation of CADx systems showed that CADx assisted OD performs equally to the operator performance when using non CADx assisted OD. To harness a benefit of clinical CADx implementation we would have to find a way that synergies between human and CADx come into play to eliminate cases in which CADx assisted and/ or human OD results in low diagnostic accuracy and also addresses the problem of serrated polyp recognition.
Detailed description
Our study hypothesis is that for CADx implementation, instead of using the high/low confidence framework, identifying cases with suboptimal diagnostic accuracy could be facilitated through identifying cases in which CADx and endoscopist disagreed in their diagnosis. Eliminating such cases might separate out cases with low accuracy when using CADx assisted OD. Since endoscopists have a high sensitivity but low specificity for serrated polyp OD, this framework will also allow us to implement a strategy to adequately manage serrated polyps found in the cohort.
Interventions
OTHERCADx (AI) system
The CADx system will be used to predict the histopathology of the polyp detected.
Sponsors
Centre hospitalier de l'Université de Montréal (CHUM)
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
DIAGNOSTIC
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
45 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
* Indication for full colonoscopy.
Exclusion criteria
* Known inflammatory bowel disease * Active colitis * coagulopathy * familial polyposis syndrome * poor general health, defined as an American Society of Anesthesiologists class \>3 * emergency colonoscopy
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Accuracy of optical diagnosis, for polyps 1-5mm, compared with an agreed upon CADx-assisted diagnosis | up to 100 weeks | Accuracy of optical diagnosis, for polyps 1-5mm, compared with an agreed upon CADx-assisted diagnosis , when histopathology results are used as the reference |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Test characteristics, including recall, specificity, positive and negative predictive values (PPV/NPV), and particularly the NPV of rectosigmoid neoplastic polyps. | up to 100 weeks | A ≥90% NPV will be used as a quality benchmark for a strategy to not resect such diminutive polyps. |
| Agreement of surveillance interval recommendations of AI-A and AI-H compared with the pathology-based recommendations | up to 100 weeks | 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. |
| Accuracy of optical diagnosis, for polyps 1-10mm, compared with an agreed upon CADx-assisted diagnosis | up to 100 weeks | Accuracy of optical diagnosis, for polyps 1-10mm, compared with an agreed upon CADx-assisted diagnosis, when histopathology results are used as the reference |
| Variability of OD (AI-A and AI-H) across participating endoscopists. | up to 100 weeks | Each participating endoscopist will conduct a similar number of optical diagnoses to assess endoscopist-related factors. |
| Cost-effectiveness of OD ((AI-A and AI-H) | up to 100 weeks | 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. |
| 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. | up to 100 weeks | The potential cost-effectiveness of OD (either approaches) will be evaluated using the measured described above. |
Countries
Canada
Contacts
Primary ContactDaniel von Renteln, MD
Outcome results
None listed