Artificial Intelligence for Diminutive Polyp Characterization

NCT ID: NCT05391477

Last Updated: 2023-05-31

Basic Information

• Recruitment Status: UNKNOWN
• Clinical Phase: NA
• Total Enrollment: 643 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-02-27
• Study Completion Date: 2024-12-31

Brief Summary

Artificial intelligence is a promising tool that may have a role in characterizing colon epithelial lesions (CADx), helping to get a reliable optical diagnosis regardless of the endoscopist experience. Performances of the different CADx systems are variable but it seems that, in most cases, high accuracy and sensitivities are achieved. However, these CADx systems have been developed and validated using still pictures or videos, and a real-world accurate test is lacking. No clinical trials have tested this technology in clinical practice and, therefore, performance in real colonoscopies, practical problems, applicability, and cost are unknown.

Detailed Description

The resect-and-discard (R&D) and diagnose-and-leave (D&L) strategies have been proposed as a means to reduce costs in the evaluation of colorectal polyps avoiding a substantial number of pathology evaluations. A pre-requisite for this paradigm shift is an accurate optical diagnosis (HOD). However, performance results for HOD have been highly variable among endoscopists representing a barrier for the adoption of the R&D and the D&L strategies.

Artificial intelligence is a promising tool that may have a role in characterizing colon epithelial lesions (CADx), helping to get a reliable optical diagnosis regardless of the endoscopist experience. Performances of the different CADx systems are variable but it seems that, in most cases, high accuracy and sensitivities are achieved. However, these CADx systems have been developed and validated using still pictures or videos, and a real-world accurate test is lacking. No clinical trials have tested this technology in clinical practice and, therefore, performance in real colonoscopies, practical problems, applicability, and cost are unknown.

Methods and analysis: The ODDITY trial is a European multicenter randomized, parallel-group superiority trial comparing GI-Genius artificial intelligence optical diagnosis (AIOD) to human optical diagnosis (HOD) of colon lesions ≤ 5 mm performed by endoscopists, using histopathology as the gold standard. A total of 643 patients attending a colonoscopy within a CRC screening program (either FIT- or colonoscopy-based) or because of post-polypectomy surveillance will be randomized to the ADI group or the HOD (control) group. A computer-generated 1:1 blocking randomization scheme stratified for center and endoscopist will be used.

Conditions

Colorectal Neoplasms

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: DIAGNOSTIC
• Blinding Strategy: SINGLE

Study Groups

Human optical diagnosis (HOD)

The examinator will provide a HOD for every lesion (regardless of their size) found during the examination (adenoma vs non-adenoma) following one of the available validated classifications (NICE, JNET, BASIC). He/she will also give a level of confidence in his/her diagnosis (high/low confidence). However, only diminutive lesions will be considered when analyzing the main outcome. The time to get a HOD will be recorded. An in situ surveillance interval will be provided if possible.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Artificial intelligence optical diagnosis (AIOD):

GI-Genius will provide an artificial intelligence diagnosis (AIOD) for every lesion detected (adenoma vs non-adenoma). Only diminutive lesions will be considered for the analysis of the main outcome. However, data on larger lesions will be recorded to describe GI-Genius´ performance in detail (secondary outcome). The time to get an AIOD will be recorded. An in situ surveillance interval will be provided if possible

Group Type: EXPERIMENTAL

GI-Genius artificial intelligence

Intervention Type: DEVICE

The software allows for the real-time characterization of framed polyps during a colonoscopy classifying them on adenoma or non-adenoma.

Interventions

GI-Genius artificial intelligence

The software allows for the real-time characterization of framed polyps during a colonoscopy classifying them on adenoma or non-adenoma.

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• Patients attending a colonoscopy within a population-based CRC screening program (FIT- or colonoscopy-based) or because of post-polypectomy surveillance,
• Written informed consent before the colonoscopy,

Exclusion Criteria

• None, patient included
• Previous history of inflammatory bowel disease.
• Previous history of CRC
• Previous CR resection
• Polyposis or hereditary CRC syndrome
• Coagulopathy/Anticoagulants
• Unwillingness to participate

• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

European Society of Gastrointestinal Endoscopy

OTHER

Sponsor Role: collaborator

Medtronic

INDUSTRY

Sponsor Role: collaborator

Hospital Universitario La Fe

OTHER

Sponsor Role: lead

Responsible Party

Marco Bustamante-Balén

Principal Investigator

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Marco Bustamante Balén, M.D., Ph.D.

Role: PRINCIPAL_INVESTIGATOR

Hospital Universitario La Fe

Locations

Hospital Universitari i Politècnic La Fe

Valencia, , Spain

Site Status: RECRUITING

Countries

Spain

Central Contacts

Marco Bustamante Balén, M.D., Ph.D.

Role: CONTACT

bustamante_mar@gva.es

+34 961244000 ext. 440225

Sylwia Jaworska Fernandez

Role: CONTACT

sylwia_jaworska@iislafe.es

9621244262

Facility Contacts

Marco Bustamante Balén, M.D.;Ph.D.

Role: primary

bustamante_mar@gva.es

961244000 ext. 440225

Sylwia Jaworska Fernandez

Role: backup

sylwia_jaworska@iislafe.es

9621244262

Other Identifiers

Oddity

Identifier Type: -

Identifier Source: org_study_id