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Small Bowel Deep Learning Algorithm Project

NCT03706664 · Status: ACTIVE_NOT_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 226

Last updated 2026-05-18

No results posted yet for this study

Summary

Crohn's disease affects 200,000 people in the UK (\~1 in 500), most are young (diagnosed \< 35 years) with costs of direct medical care exceeding £500 million.

Crohn's disease is caused by an auto-immune response and affects any part of the digestive tract, most commonly the last segment of the small bowel (the terminal ileum).

Magnetic resonance imaging (MRI) plays a role in 3 areas: Crohn's disease diagnosis , monitoring treatment response \& assessing development of complications.

To evaluate the small bowel using MRI, Radiologists visually examine the scan slice-by-slice. The interpretation is time consuming and error-prone because of disease presentation variability and differentiation of diseased segments from collapsed segments.

Deep learning for image analysis is based on a computer algorithm "learning" from human (Radiologist) generated training data.

This method has been successfully applied to medical imaging, for example computer detection of lung cancer on chest X-rays.

This pilot study investigates if a deep learning algorithm can identify and score segments of inflamed terminal ileum affected by Crohn's disease.

To our knowledge this is the first project attempting to develop such an algorithm.The study will retrospectively review MR images obtained as part of standard care from patients being investigated for, Crohn's or being followed up with Crohn's disease. 226 patients' images will be used for the study.

On fully anonymised images two Radiologists working at Northwick Park Hospital will score and outline normal and abnormal loops of terminal ileum. Imperial College computer science department will then develop a deep learning algorithm from imaging features of normal and abnormal loops.

The study end-point is algorithm performance vs. images labelled by Radiologists.

The eventual aim is to develop an algorithm that assists Radiologists in the accurate diagnosis and follow-up of patients with Crohn's disease.

Conditions

  • Crohn Disease

Interventions

OTHER

Machine learning algorithm

Study will develop and test a machine learning algorithm using MR Enterography images labelled by Radiologists.

Sponsors & Collaborators

  • London North West Healthcare NHS Trust

    lead OTHER

  • Imperial College London

    collaborator OTHER

Principal Investigators

  • Uday Patel, FRCR MBBS · London NorthWest Healthcare NHS Trust

Study Design

Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
NONE
Model
SINGLE_GROUP

Eligibility

Min Age
16 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-03-01
Primary Completion
2028-08-31
Completion
2028-12-31

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 NCT03706664 on ClinicalTrials.gov