Artificial Intelligence Development for Colorectal Polyp Diagnosis

Trial statusRecruiting
Trial phaseNot listed
Trial typeObservational
Biological sexAll
Age18+
SponsorKing's College Hospital NHS Trust

About this trial

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

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

None

Disqualifiers

Unable to provide informed consent.

Colitis Associated Dysplasia

Polyps at surgical anastomosis sites

Pregnancy

Trial population

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

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

  • Colonoscopy

    Diagnostic test

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

Treatment groups

No treatment groups listed

Trial outcomes

Primary outcomes

1

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

Sensitivity and Specificity

Time frame
24 months
2

Positive and negative predicted value

Assess the accuracy to the trained device

Time frame
24 months

Secondary outcomes

1

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

We will analyse the calculate the histology agreement between the advance endoscopist

Time frame
36 months
2

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

To assess if the prediction of patient gender, ethnicity, and age is possible with use of the developed CADx model.

Time frame
36 months
3

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

A qualitative analysis of CADx incorrect diagnoses will also be conducted by a multidisciplinary panel to evaluate potential impact

Time frame
36 months
4

Learned effects of AI augmented endoscopy on endoscopist optical diagnosis

We will evaluate if the use of AI during colonoscopy can be a learning tools for endoscopist

Time frame
36 months

Other outcomes

Sponsors and contacts

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