The COMBO CAD Study

NCT05141409 | Completed

• 1 other identifier: 11
• Study Type: observational
• Target: 500
• Locations: 1 country
• Sites: 1

Brief Summary

Implementation of clinical strategies based on optical diagnosis of \<5 mm colorectal polyps may lead to a substantial saving of economic and financial resources. Despite this, 84.2% of European endoscopists reported not to use such strategies - also named as leave-in situ and resect- and-discard - in their practice due to the fear of an incorrect optical diagnosis. Indeed, accuracy of optical diagnosis is operator-dependent, and values reported in the community setting are below the safety thresholds proposed for its incorporation in clinical practice. Artificial intelligence (AI) is being increasingly explored in different domains of medicine, particularly those entailing image analysis. As optical diagnosis involves subitaneous processing of multiple images, searching for specific visual clues, and recognizing well-defined visual patterns, AI systems has the potential to help endoscopists in distinguish neoplastic from non-neoplastic polyps, making the characterization process more reliable and objective. Computer-Aided-Diagnosis systems aiming at characterization are called CADx. Preliminary data on CADx showed a high feasibility and accuracy of AI for optical diagnosis of colorectal polyp, and initial experiences in clinical practice confirmed preliminary results. To assess the potential benefit and risk of AI-assisted optical diagnosis with standard colonoscopy, we exploited two new Computer-Aided-Diagnosis systems (CAD-EYE® Fujifilm Co., and GI-Genius® Medtronic) that provide the endoscopist with a real-time polyp characterization without the need of optical magnification.

Conditions

Artificial Intelligence

Outcome Measures

Primary Outcomes (1)

• AI-assisted optical diagnosis performance

  AI-assisted optical diagnosis performance

  6 Months

Secondary Outcomes (1)

• AI alone optical diagnosis performance

  6 Months

Study Arms (2)

CAD-A

Device: Artificial Intelligence

CAD-B

Device: Artificial Intelligence

Interventions

Artificial Intelligence DEVICE

Artificial Intelligence

CAD-A; CAD-B

Eligibility Criteria

• Age: 40 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All patients aged 40 or older undergoing a colonoscopy for gastrointestinal symptoms, fecal immunohistochemical test positivity, primary screening or post-polypectomy surveillance.

You may qualify if:

• \- All patients aged 40 or older undergoing a colonoscopy for gastrointestinal symptoms, fecal immunohistochemical test positivity, primary screening or post-polypectomy surveillance

You may not qualify if:

• subjects with personal history of CRC, or IBD.
• Subjects affected with Lynch syndrome or Familiar Adenomatous Polyposis.
• patients with inadequate bowel preparation (defined as Boston Bowel Preparation Scale \< 2 in any colonic segment).
• patients with previous colonic resection.
• patients on antithrombotic therapy, precluding polyp resection.
• patients who were not able or refused to give informed written consent.

Sponsors & Collaborators

• Istituto Clinico Humanitas: lead

Study Sites (1)

Department of Gastroenterology, Humanitas Research Hospital

Rozzano, Milano, 20089, Italy

Location

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: November 19, 2021
• First Posted: December 2, 2021
• Study Start: January 26, 2022
• Primary Completion: September 30, 2022
• Study Completion: September 30, 2022
• Last Updated: December 29, 2022

Record last verified: 2022-12

Data Sharing

• IPD Sharing: Will not share

Locations

IT (1)