Artificial Intelligence Development for Colorectal Polyp Diagnosis

NCT06447012 | Recruiting

• 1 other identifier: 323988
• Study Type: observational
• Target: 4,000
• Locations: 1 country
• Sites: 1

Brief 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

Keywords

Artificial Intelligence; Computer assisted diagnosis (CADx); Colorectal cancer; Colorectal polyp; Machine learning

Outcome Measures

Primary Outcomes (2)

• To achieve an overall accuracy of 85% for the five-classification lesion prediction algorithm.

  Sensitivity and Specificity

  24 months

• Positive and negative predicted value

  Assess the accuracy to the trained device

  24 months

Secondary Outcomes (4)

• Interobserver agreement of the endoscopists' prediction of histology of polyps during the annotation process.

  36 months

• Sub-analysis of the polyp characteristics focused on different gender and ethnicity.

  36 months

• This will be a sub analysis of an AI algorithm that is trained to predict polyp histology using the prospective data cohort.

  36 months

• Learned effects of AI augmented endoscopy on endoscopist optical diagnosis

  36 months

Interventions

Colonoscopy DIAGNOSTIC_TEST

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

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All adult (over 18 years) presenting for screening or symptomatic colonoscopy

You may not qualify if:

• Unable to provide informed consent.
• Colitis Associated Dysplasia
• Polyps at surgical anastomosis sites
• Pregnancy

Sponsors & Collaborators

• King's College Hospital NHS Trust: lead

Study Sites (1)

King's College Hospital NHS Foundation Trust

London, SE5 9RS, United Kingdom

RECRUITING

MeSH Terms

Conditions

Colonic Polyps; Colorectal Neoplasms

Interventions

Colonoscopy

Condition Hierarchy (Ancestors)

Intestinal Polyps; Polyps; Pathological Conditions, Anatomical; Pathological Conditions, Signs and Symptoms; Intestinal Neoplasms; Gastrointestinal Neoplasms; Digestive System Neoplasms; Neoplasms by Site; Neoplasms; Digestive System Diseases; Gastrointestinal Diseases; Colonic Diseases; Intestinal Diseases; Rectal Diseases

Intervention Hierarchy (Ancestors)

Endoscopy, Gastrointestinal; Endoscopy, Digestive System; Diagnostic Techniques, Digestive System; Diagnostic Techniques and Procedures; Diagnosis; Endoscopy; Diagnostic Techniques, Surgical; Digestive System Surgical Procedures; Surgical Procedures, Operative; Minimally Invasive Surgical Procedures

Central Study Contacts

Shraddha B Gulati, MBBS PHD MRCP

CONTACT

+442032996044; shraddha.gulati@nhs.net

Olaolu Olabintan, MBBS MRCP

CONTACT

+447939056819; olaolu.olabintan@nhs.net

Study Design

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

Study Record Dates

• First Submitted: May 7, 2024
• First Posted: June 6, 2024
• Study Start: May 4, 2024
• Primary Completion: June 1, 2025
• Study Completion (Estimated): May 30, 2026
• Last Updated: June 6, 2024

Record last verified: 2024-06

Locations

GB (1)