Crohn DiseaseNCT03706664
Small Bowel Deep Learning Algorithm Project
A study testing Machine learning algorithm in people with Crohn Disease, run by London North West Healthcare NHS Trust at 1 site in United Kingdom.
Trial 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… Read more about this study
- Who is it for?
- Ages 16 years and older
- Conditions the study is for
- Crohn Disease
- What is being tested?
- Machine learning algorithm
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 226 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 1 Mar 2019to Dec 2028
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 16 years and older
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- 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.
- 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.
- 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).
Who cannot join
- Poor quality MRI images as judged by consensus Radiologist opinion.
- No more than 3 MRI scans will come from the same patient.
- 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.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Pilot Study to Develop a Deep Learning Algorithm for Identification & Scoring of Terminal Ileal Crohn's Disease in Magnetic Resonance Enterography Images.
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).
Read more
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.
Read the detailed description
Introduction.
The principal aim of the study is evaluating the accuracy of deep learning algorithm in differentiating between normal and abnormal terminal ileum against experienced Radiologists on MR Enterography images.
The study builds on existing research, which has shown statistical methods can identify sites of small bowel Crohn's disease. However the process was time consuming >1hr and not fully automatic. Our project investigates if cutting edge "deep learning" algorithm (based on neural networks) coupled with increased computing power can provide accurate and timely information.
The project has been designed jointly by Specialist Radiologists in Gastrointestinal imaging (who are aware of the challenges in imaging Crohn's disease accurately) and Imperial College Computer Science Department (who are experienced in developing neural networks for medical imaging). Input and review from London North-West Research and Development department is also acknowledged.
Study design.
Retrospective design \& Recruitment.
The study will retrospectively identify eligible patients and use a consecutive case sampling technique, (all eligible images will be included working backwards from most recent).
This retrospective approach compromises between generalisability of findings being reduced vs. the study being carried out relatively quickly and at low cost (study has no grant funding).
The investigators are confident of the generalisability of the results as a recruitment target of 113 normal cases and 113 cases with terminal ileal disease should cover the spectrum of normal and abnormal appearances (previous studies have used <50 image sets).
Cases with normal terminal ileum on MRI are included as an approach to algorithm development involves comparison of imaging features of normal and abnormal terminal ileum on MRI studies.
Non-experimental approach.
The method uses MRI scans undertaken as part of standard clinical care. No additional imaging is undertaken for this study. The study results will not change the current treatment/s eligible patients are taking..
Consent \& confidentiality.
As the images used for algorithm development are fully anonymized so explicit consent will not be obtained. This follows guidance from the General Medical Council Guidelines in 2011 and The Royal College of Radiologists(UK) in 2017 which state anonymized recordings can be shared for use in research without consent.
MRI images used for this study were acquired as part of routine standard clinical care, and would routinely be viewed by the Radiologists taking part in this study as part of their normal working practice.
As soon as suitable patient is identified the patient's images will be copied in a fully anonymized form with no direct or indirect identifiers. A robust anonymization function is included in the Radiology image viewing program. Study subject Identifiers will be randomly allocated preventing pseudo-anonymization if scans from the same patient at different time points are included.
No sensitive/patient identifiable data will be transferred for algorithm development during the study. The algorithm development is based on matching MRI pixel intensities to disease scores/ annotations across multiple scans. Anonymization does not affect the pixels within the image. Only aggregate data will be presented in publications- i.e. single case examples will not be published.
Conflict of interest. The researchers on this study declare no conflict of interest.
- Conditions
- Crohn Disease
- Being tested
- Machine learning algorithm
- Medical subject headings
- Crohn Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Machine learning algorithmOther
Study will develop and test a machine learning algorithm using MR Enterography images labelled by Radiologists.
| Study group | Role | What they receive |
|---|---|---|
| Training of machine learning algorithm | Other | 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. Receives: Other: Machine learning algorithm |
| Testing of machine learning algorithm | Other | 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. Receives: Other: Machine learning algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
Machine learning algorithm's ability to accurately localize the terminal ileum.
Study will compare manually segmented regions of interest by Radiologists with predictions by machine learning localisation algorithm.
Measured: 24 months
Secondary outcomes (2)
Data processing time until a diagnosis reported by algorithm.
Study will assess time taken for algorithm to give a diagnostic outcome. (Previous studies have shown this time can be variable).
Measured: 24 months
Machine learning algorithm's ability to accurately distinguish abnormal and normal terminal ileum.
Agreement between Radiologists and predictions by machine learning classification algorithm will be analysed.
Measured: 24 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Oct 2018
- Study start1 Mar 2019
- Primary completionAug 2028 (estimated)
- Study completionDec 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Crohn DiseaseStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Single group
- 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)
- Enrolment
- 226 participants (estimated)
Sponsor and investigators
- Lead sponsor
- London North West Healthcare NHS Trust
- Sponsor type
- University, hospital or other
- Collaborators
- Imperial College London
- Responsible party
- Sponsor
- Investigators
- Uday Patel, FRCR MBBS, Principal investigator, London NorthWest Healthcare NHS Trust
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03706664
- Sponsor's protocol ID
- IRAS No:238924
- Status last verified
- May 2026
- First submitted
- 11 Oct 2018
- Last updated
- 18 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 18 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Pilot Study to Develop a Deep Learning Algorithm for Identification & Scoring of Terminal Ileal Crohn's Disease in Magnetic Resonance Enterography Images.
- ClinicalTrials.gov ID
- NCT03706664
- Acronym / sponsor ID
- IRAS No:238924
- Conditions
- Crohn Disease
- Study type
- Interventional
- Recruitment status
- Active, not recruiting
- Enrolment
- 226 participants (estimated)
- Lead sponsor
- London North West Healthcare NHS TrustUniversity, hospital or other
- Collaborators
- Imperial College London
- Study start
- 1 Mar 2019 (actual)
- Primary completion
- Aug 2028 (estimated)
- Study completion
- Dec 2028 (estimated)
- First posted
- 16 Oct 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Detailed description
Introduction.
The principal aim of the study is evaluating the accuracy of deep learning algorithm in differentiating between normal and abnormal terminal ileum against experienced Radiologists on MR Enterography images.
The study builds on existing research, which has shown statistical methods can identify sites of small bowel Crohn's disease. However the process was time consuming >1hr and not fully automatic. Our project investigates if cutting edge "deep learning" algorithm (based on neural networks) coupled with increased computing power can provide accurate and timely information.
The project has been designed jointly by Specialist Radiologists in Gastrointestinal imaging (who are aware of the challenges in imaging Crohn's disease accurately) and Imperial College Computer Science Department (who are experienced in developing neural networks for medical imaging). Input and review from London North-West Research and Development department is also acknowledged.
Study design.
Retrospective design \& Recruitment.
The study will retrospectively identify eligible patients and use a consecutive case sampling technique, (all eligible images will be included working backwards from most recent).
This retrospective approach compromises between generalisability of findings being reduced vs. the study being carried out relatively quickly and at low cost (study has no grant funding).
The investigators are confident of the generalisability of the results as a recruitment target of 113 normal cases and 113 cases with terminal ileal disease should cover the spectrum of normal and abnormal appearances (previous studies have used <50 image sets).
Cases with normal terminal ileum on MRI are included as an approach to algorithm development involves comparison of imaging features of normal and abnormal terminal ileum on MRI studies.
Non-experimental approach.
The method uses MRI scans undertaken as part of standard clinical care. No additional imaging is undertaken for this study. The study results will not change the current treatment/s eligible patients are taking..
Consent \& confidentiality.
As the images used for algorithm development are fully anonymized so explicit consent will not be obtained. This follows guidance from the General Medical Council Guidelines in 2011 and The Royal College of Radiologists(UK) in 2017 which state anonymized recordings can be shared for use in research without consent.
MRI images used for this study were acquired as part of routine standard clinical care, and would routinely be viewed by the Radiologists taking part in this study as part of their normal working practice.
As soon as suitable patient is identified the patient's images will be copied in a fully anonymized form with no direct or indirect identifiers. A robust anonymization function is included in the Radiology image viewing program. Study subject Identifiers will be randomly allocated preventing pseudo-anonymization if scans from the same patient at different time points are included.
No sensitive/patient identifiable data will be transferred for algorithm development during the study. The algorithm development is based on matching MRI pixel intensities to disease scores/ annotations across multiple scans. Anonymization does not affect the pixels within the image. Only aggregate data will be presented in publications- i.e. single case examples will not be published.
Conflict of interest. The researchers on this study declare no conflict of interest.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 2
- 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.
| Arm | Type | Receives |
|---|---|---|
| Training of machine learning algorithm | Other | Other: Machine learning algorithm113 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. |
| Testing of machine learning algorithm | Other | Other: Machine learning algorithm113 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. |
Who may qualify
- Ages
- 16 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (5 listed)
- 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.
- 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.
- 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).
Key exclusion criteria (5 listed)
- Poor quality MRI images as judged by consensus Radiologist opinion.
- No more than 3 MRI scans will come from the same patient.
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Machine learning algorithmStudy will develop and test a machine learning algorithm using MR Enterography images labelled by Radiologists. | Other | Testing of machine learning algorithm, Training of machine learning algorithm |
Primary outcome
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.
2 secondary outcomes
Data processing time until a diagnosis reported by algorithm.
Time frame: 24 months
Machine learning algorithm's ability to accurately distinguish abnormal and normal terminal ileum.
Time frame: 24 months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 May 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Uday Patel, FRCR MBBS principal investigator
London NorthWest Healthcare NHS Trust
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Machine learning algorithm in people with Crohn Disease, run by London North West Healthcare NHS Trust at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 18 May 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Machine learning algorithm's ability to accurately localize the terminal ileum. (time frame: 24 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 May 2026.