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Nonapeptide-1

Melanostatine-5, White 05

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Studies 4
Trials 100
Completed OBSERVATIONAL NCT04589078

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

View on ClinicalTrials.gov Updated Dec 15, 2025

Brief 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.

Interventions

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

Primary Outcomes

Measure: Negative Predictive Value of histology prediction on diminutive (≤5 mm) rectosigmoid polyps
TimeFrame: 1 day
Description:

Trial Information

NCT ID

NCT04589078

Status

Completed

Study Type

OBSERVATIONAL

Sponsor

Cosmo Artificial Intelligence-AI Ltd

Last Updated

December 15, 2025