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Diagnostic Performance of a Convolutional Neural Network for Diminutive Colorectal Polyp Recognition

NCT03822390 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 292

Last updated 2021-12-29

No results posted yet for this study

Summary

Rationale: Diminutive colorectal polyps (1-5mm in size) have a high prevalence and very low risk of harbouring cancer. Current practice is to send all these polyps for histopathological assessment by the pathologist. If an endoscopist would be able to correctly predict the histology of these diminutive polyps during colonoscopy, histopathological examination could be omitted and practise could become more time- and cost-effective. Studies have shown that prediction of histology by the endoscopist remains dependent on training and experience and varies greatly between endoscopists, even after systematic training. Computer aided diagnosis (CAD) based on convolutional neural networks (CNN) may facilitate endoscopists in diminutive polyp differentiation. Up to date, studies comparing the diagnostic performance of CAD-CNN to a group of endoscopists performing optical diagnosis during real-time colonoscopy are lacking.

Objective: To develop a CAD-CNN system that is able to differentiate diminutive polyps during colonoscopy with high accuracy and to compare the performance of this system to a group of endoscopist performing optical diagnosis, with the histopathology as the gold standard.

Study design: Multicentre, prospective, observational trial. Study population: Consecutive patients who undergo screening colonoscopy (phase 2)

Main study parameters/endpoints: The accuracy of optical diagnosis of diminutive colorectal polyps (1-5mm) by CAD-CNN system compared with the accuracy of the endoscopists. Histopathology is used as the gold standard.

Conditions

  • Artificial Intelligence
  • Colorectal Polyp

Interventions

DEVICE

CAD-CNN system

The CAD-CNN system will be trained in predicting the histology of diminutive polyps. Before training, the dataset will be split up into a training set and a test set. To ensure a completely independent test and training set there will be no overlap between patients (i.e. if polyps from a patient A is present in the training set it cannot be in the test set as well).

Sponsors & Collaborators

  • Bergman Clinics

    collaborator OTHER

  • Frisius Medisch Centrum

    collaborator OTHER

  • Academisch Medisch Centrum - Universiteit van Amsterdam (AMC-UvA)

    lead OTHER

Principal Investigators

  • Evelien NA Dekker, Msc · Amsterdam UMC, location VUmc

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-10-16
Primary Completion
2021-10-16
Completion
2021-10-16

Countries

  • Netherlands

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 NCT03822390 on ClinicalTrials.gov