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Small Bowel Deep Learning Algorithm Project

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT03706664
Recruitment Status : Active, not recruiting
First Posted : October 16, 2018
Last Update Posted : February 27, 2020
Sponsor:
Collaborator:
Imperial College London
Information provided by (Responsible Party):
London North West Healthcare NHS Trust

Brief 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.


Condition or disease Intervention/treatment Phase
Crohn Disease Other: Machine learning algorithm Not Applicable

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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 226 participants
Allocation: Randomized
Intervention Model: Single Group Assignment
Intervention Model Description:

Radiologists will label 226 MR Enterography images as normal or abnormal. The labelled images will be randomised between training and validation sets.

The training dataset will be used to develop machine learning algorithm to localise the terminal ileum & classify the terminal ileum as normal or abnormal.

The validation dataset will test the accuracy of the algorithm against the Radiologists labels.

Masking: None (Open Label)
Masking Description: Neither the Radiologists nor the Computer scientists/outcomes assessors will be masked to the image labels or if a given MR Enterography has been used in the training or validation dataset.
Primary Purpose: Diagnostic
Official Title: Pilot Study to Develop a Deep Learning Algorithm for Identification & Scoring of Terminal Ileal Crohn's Disease in Magnetic Resonance Enterography Images.
Actual Study Start Date : March 1, 2019
Estimated Primary Completion Date : November 2020
Estimated Study Completion Date : January 2021

Resource links provided by the National Library of Medicine

MedlinePlus related topics: Crohn's Disease

Arm Intervention/treatment
Training of machine learning algorithm
113 MR Enterography images labelled by Radiologists will be used to develop a machine learning algorithm to (1) localise the terminal ileum, (2) classify the terminal ileum as normal or abnormal.
Other: Machine learning algorithm
Study will develop and test a machine learning algorithm using MR Enterography images labelled by Radiologists.

Testing of machine learning algorithm

113 MR Enterography images labelled by Radiologists will be used to test the accuracy of the machine learning algorithm to (1) localise the terminal ileum, (2) classify the terminal ileum as normal or abnormal compared to Radiologists opinion.

Cross Validation analysis will be used for data analysis.

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




Primary Outcome Measures :
  1. Machine learning algorithm's ability to accurately localize the terminal ileum. [ Time Frame: 24 months ]
    Study will compare manually segmented regions of interest by Radiologists with predictions by machine learning localisation algorithm.


Secondary Outcome Measures :
  1. Data processing time until a diagnosis reported by algorithm. [ Time Frame: 24 months ]
    Study will assess time taken for algorithm to give a diagnostic outcome. (Previous studies have shown this time can be variable).

  2. Machine learning algorithm's ability to accurately distinguish abnormal and normal terminal ileum. [ Time Frame: 24 months ]
    Agreement between Radiologists and predictions by machine learning classification algorithm will be analysed.



Information from the National Library of Medicine

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Ages Eligible for Study:   16 Years and older   (Child, Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Inclusion Criteria for all cases:

  • Patient's age >16 years of age, (this age cut off has been used in the recent METRIC trial investigating imaging in Crohn's disease)
  • MRI sequences obtained include axial T2 weighted images; coronal T2 weighted images and axial post contrast MRI images.

Inclusion criteria for normal MR Enterography cases:

• Normal MR Enterography studies reviewed in consensus by two Radiologists (UP & PL). Normal is defined as no sites of small or large bowel Crohn's disease.

Inclusion criteria for terminal ileal Crohn's cases:

  • MR Enterography studies reviewed in consensus by two Radiologists shows terminal ileal Crohn's disease. Patients with more than one segment of small bowel Crohn's disease including terminal ileum are eligible. Patients with terminal ileal Crohn's disease continuous with large bowel are eligible.
  • Diagnosis of Crohn's disease of terminal ileum based on endoscopic, histological and radiological findings. (This criteria has been used in the recent METRIC trial investigating imaging in Crohn's disease).

Exclusion Criteria for all cases:

  • Poor quality MRI images as judged by consensus Radiologist opinion.
  • No more than 3 MRI scans will come from the same patient.

Exclusion criteria for terminal ileal Crohn's cases:

  • MR Enterography shows any bowel abnormality not due to Crohn's.
  • Patient has undergone previous small or large bowel resection (this will distort anatomy and is beyond the scope of the present project). Patients' with other previous surgeries are eligible.
  • Patients with large bowel Crohn's disease not continuous with the terminal ileum.

Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03706664


Locations
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United Kingdom
St Mark's Hospital
London, Harrow, United Kingdom, HA13UJ
Sponsors and Collaborators
London North West Healthcare NHS Trust
Imperial College London
Investigators
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Principal Investigator: Uday Patel, FRCR MBBS London NorthWest Healthcare NHS Trust
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Responsible Party: London North West Healthcare NHS Trust
ClinicalTrials.gov Identifier: NCT03706664    
Other Study ID Numbers: IRAS No:238924
First Posted: October 16, 2018    Key Record Dates
Last Update Posted: February 27, 2020
Last Verified: February 2020
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: Undecided

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Additional relevant MeSH terms:
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Crohn Disease
Inflammatory Bowel Diseases
Gastroenteritis
Gastrointestinal Diseases
Digestive System Diseases
Intestinal Diseases