NCT04589078

Polyp REcognition Assisted by a Device Interactive Characterization Tool - The PREDICT Study

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 200 (actual)
• Sponsor: Cosmo Artificial Intelligence-AI Ltd · Industry
• Sex: All
• Age: 40 Years – 80 Years
• Healthy volunteers: —

Summary

Diminutive colorectal polyps (≤ 5 mm) represent most of the polyps detected during colonoscopy, especially in the rectum-sigmoid tract. The characterization of these polyps by virtual chromoendoscopy is recognized as a key element for innovative imaging techniques. As a matter of facts diminutive colorectal polyps are very frequent and, if located in the rectosigmoid colon, they present a very low malignant risk (0.3% of evolution towards advanced adenoma and up to 0.08% of evolution towards invasive carcinoma). The real-time characterization would allow to identify the lowest risk polyps (hyperplastic subtype), to leave them in situ or, if resected, not to send them for histological examination, allowing a huge saving in healthcare associated costs. Recently, the American Society for Gastrointestinal Endoscopy (ASGE) Technology Committee established the Preservation and Incorporation of Valuable endoscopic Innovations (PIVI) document, specific for real-time histological assessment for tiny colorectal polyps, to establish reference quality thresholds. Two performance standards have been developed to guide the use of advanced imaging: 1. for diminutive polyps to be resected and discarded without pathologic assessment, endoscopic technology (when used with high confidence) used to determine histology of polyps ≤ 5mm in size, when combined with the histopathology assessment of polyps \> 5 mm in size, should provide a ≥ 90% agreement in assignment of post-polypectomy surveillance intervals when compared to decisions based on pathology assessment of all identified polyps; 2. in order for a technology to be used to guide the decision to leave suspected rectosigmoid hyperplastic polyps ≤ 5 mm in size in place (without resection), the technology should provide ≥ 90% negative predictive value (when used with high confidence) for adenomatous histology. Computer-Aided-Diagnosis (CAD) is an artificial intelligence-based tool that would allow rapid and objective characterization of these lesions. The GI Genius CADx was developed to help endoscopists in their clinical practices for polyps characterization.

Conditions

• Artificial Intelligence

Interventions

Type	Name	Description
DEVICE	GI Genius CADe system	Each patient will undergo standard white-light colonoscopy with the support of the latest version of the CE marked GI Genius CADe available.

Timeline

• Start date: 2020-09-08
• Primary completion: 2020-12-22
• Completion: 2020-12-22
• First posted: 2020-10-19
• Last updated: 2021-05-12

Locations

1 site across 1 country: Italy

Regulatory

• FDA-regulated device study