Clinical trial · Observational
Artificial Intelligence Development for Colorectal Polyp Diagnosis
Development of a Novel Real Time Computer Assisted Colonoscopy Diagnostic Tool for Colorectal Polyps: Lesion Diagnosis and Personalised Patient Management
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 8, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260908-000001
Summary
Brief summary (as posted)
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
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Colorectal Polyp | — | UNRESOLVED | — |
| Polyp of Colon | — | UNRESOLVED | — |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| Colonoscopy | Diagnostic Test | — | UNRESOLVED |
Design
Arms and outcomes
Arms (0)
[]Primary outcomes (2)
- measure
- To achieve an overall accuracy of 85% for the five-classification lesion prediction algorithm.
- timeFrame
- 24 months
- description
- Sensitivity and Specificity
- measure
- Positive and negative predicted value
- timeFrame
- 24 months
- description
- Assess the accuracy to the trained device
Secondary outcomes (4)
- measure
- Interobserver agreement of the endoscopists' prediction of histology of polyps during the annotation process.
- timeFrame
- 36 months
- description
- We will analyse the calculate the histology agreement between the advance endoscopist
Eligibility
Eligibility (as posted)
- Sex
- All
- Minimum age
- 18 Years
Show eligibility criteria text
Inclusion Criteria: \- Above 18 years at inclusion Symptomatic or screening colonoscopy Exclusion Criteria: * Unable to provide informed consent. * Colitis Associated Dysplasia * Polyps at surgical anastomosis sites * Pregnancy
References
Publications (0)
Data not yet available