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Artificial Intelligence Development for Colorectal Polyp Diagnosis

NCT06447012 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 4000

Last updated 2024-06-06

No results posted yet for this study

Summary

Accurate classification of growths in the large bowel (polyps) identified during colonoscopy is imperative to inform the risk of colorectal cancer. Reliable identification of the cancer risk of individual polyps helps determine the best treatment option for the detected polyp and determine the appropriate interval requirements for future colonoscopy to check the site of removal and for further polyps elsewhere in the bowel.

Current advanced endoscopic imaging techniques require specialist skills and expertise with an associated long learning curve and increased procedure time. It is for these reasons that despite being introduced in clinical practice, uptake of such techniques is limited and current methods of polyp risk stratification during colonoscopy without Artificial intelligence (AI) is suboptimal. Approximately 25% of bowel polyps that are removed by major surgery are analysed and later proved to be non-cancerous polyps that could have been removed via endoscopy thus avoiding anatomy altering surgery and the associated risks. With accurate polyp diagnosis and risk stratification in real time with AI, such polyps could have been removed non-surgically (endoscopically). Current Computer Assisted Diagnosis (CADx, a form of AI) platforms only differentiate between cancerous and non cancerous polyps which is of limited value in providing a personalised patient risk for colorectal cancer. The development of a multi-class algorithm is of greater complexity than a binary classification and requires larger training and validation datasets. A robust CADx algorithm should also involve global trainable data to minimise the introduction of bias. It is for these reasons that this is a planned international multicentre study.

The Investigators aim to develop a novel AI five class pathology prediction risk prediction tool that provides reliable information to identify cancer risk independent of the endoscopists skill.

These 5 categories are chosen because treatment options differ according to the polyp type and future check colonoscopy guidelines require these categories

Conditions

  • Polyp of Colon
  • Colorectal Polyp

Interventions

DIAGNOSTIC_TEST

Colonoscopy

No intervention required from this study, however images will be obtained from patient presenting for colonoscopy

Sponsors & Collaborators

  • King's College Hospital NHS Trust

    lead OTHER

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2024-05-04
Primary Completion
2025-06-01
Completion
2026-05-30

Countries

  • United Kingdom

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06447012 on ClinicalTrials.gov