Inflammatory Bowel Disease 1NCT04867408
Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease (EVEREST - IBD)
An observational study of Inflammatory Bowel Disease 1, run by Hull University Teaching Hospitals NHS Trust at 1 site in United Kingdom.
Trial summary
To develop and train a convolutional neural network to detect and characterize disease severity of inflammatory bowel disease during endoscopy Read more about this study
- Who is it for?
- Ages 16 to 99 yearsHealthy volunteers can take part
- Conditions the study is for
- Inflammatory Bowel Disease 1
- How many people
- 4,000 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 17 Sep 2021to Sep 2031
- Phase
- Observational
- 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 to 99 years
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
Study population: Inflammatory Bowel Disease (IBD) affects at least one in 250 people of the UK population and the prevalence is rising.
Who may be able to join
- • Any adult patient aged 16 years or older who has consented to undergo endoscopic investigation where images are captured as part of routine clinical care.
Who cannot join
- • Any patient under the age of 16
- Patients who are unable to give informed consent to undergo endoscopic investigation or those who do not wish their pseudo-anonymised images to be used
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
United Kingdom1 site, 1 recruiting
- Recruiting
Hull Royal Infirmary
Hull, East Yorkshire, HU3 2JZ
About this study
Official title: EVEREST - IBD: Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease
To develop and train a convolutional neural network to detect and characterize disease severity of inflammatory bowel disease during endoscopy
Read the detailed description
To develop and train a Convolutional Neural Network to detect and characterize disease severity in inflammatory bowel disease during endoscopy. This initiative will inevitably establish a high-quality large image database. Our secondary study aims are therefore to use the images we collect to advance the field of deep learning and computer aided diagnosis in inflammatory bowel disease by establishing an image database. This will involve developing a framework combining deep learning and computer vision algorithms. The ultimate aim is to use the image database to produce high impact research outcomes and training resources leading to an improvement in the quality of endoscopy performed, reduce inter-observer variability in disease assessment and a reduction in missed bowel cancer rates and associated mortality.
- Conditions
- Inflammatory Bowel Disease 1
- Medical subject headings
- Crohn Disease
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Main group | Patients with/suspected Inflammatory Bowel Disease attending for an endoscopic procedure | |
| Control | Patients without Inflammatory Bowel disease attending for an endoscopic procedure |
What is being measured
The primary outcome is the main question the study is designed to answer.
To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy
To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy
Measured: 5 years
Secondary outcomes (6)
a) To explore whether Artificial Intelligence can predict response to IBD therapies
To explore whether Artificial Intelligence can predict response to IBD therapies
Measured: 5 years
b) To develop an endoscopic image repository to advance training and standardisation in endoscopic detection and characterisation of IBD.
- To develop an endoscopic image repository to advance training and standardisation
Measured: 5 years
c) To develop and assess methodologies for training and quality assurance of IBD diagnostic endoscopy
To develop and assess methodologies for training and quality assurance of IBD
Measured: 5 years
d) To evaluate comparisons in endoscopic image interpretation between endoscopist's
To evaluate comparisons in endoscopic image interpretation between endoscopist's
Measured: 5 years
e) To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD
To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD
Measured: 5 years
f) To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time.
To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time.
Measured: 5 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Apr 2021
- Study start17 Sep 2021
- Primary completionSep 2031 (estimated)
- Study completionSep 2031 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Inflammatory Bowel Disease 1Studies 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
- Observational
- Observational model
- Other
- Time perspective
- Cross-sectional
- Enrolment
- 4,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Hull University Teaching Hospitals NHS Trust
- Sponsor type
- Other government
- Collaborators
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London
- Responsible party
- Sponsor
- Investigators
- Shaji Sebastian, Principal investigator, Hull University Teaching Hospitals NHS Trust
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04867408
- Sponsor's protocol ID
- 299614
- Status last verified
- Nov 2024
- First submitted
- 27 Apr 2021
- Last updated
- 15 Nov 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Study at a glance
From the registry record, last updated by the sponsor on 15 Nov 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- EVEREST - IBD: Endoscopic Severity Image Recognition to Advance Research and Training in Inflammatory Bowel Disease
- ClinicalTrials.gov ID
- NCT04867408
- Acronym / sponsor ID
- 299614
- Conditions
- Inflammatory Bowel Disease 1
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 4,000 participants (estimated)
- Lead sponsor
- Hull University Teaching Hospitals NHS TrustOther government
- Collaborators
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London
- Study start
- 17 Sep 2021 (actual)
- Primary completion
- Sep 2031 (estimated)
- Study completion
- Sep 2031 (estimated)
- First posted
- 30 Apr 2021 (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.
To develop and train a convolutional neural network to detect and characterize disease severity of inflammatory bowel disease during endoscopy
Detailed description
To develop and train a Convolutional Neural Network to detect and characterize disease severity in inflammatory bowel disease during endoscopy. This initiative will inevitably establish a high-quality large image database. Our secondary study aims are therefore to use the images we collect to advance the field of deep learning and computer aided diagnosis in inflammatory bowel disease by establishing an image database. This will involve developing a framework combining deep learning and computer vision algorithms. The ultimate aim is to use the image database to produce high impact research outcomes and training resources leading to an improvement in the quality of endoscopy performed, reduce inter-observer variability in disease assessment and a reduction in missed bowel cancer rates and associated mortality.
Study design
- Observational model
- Other
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Main group | – | –Patients with/suspected Inflammatory Bowel Disease attending for an endoscopic procedure |
| Control | – | –Patients without Inflammatory Bowel disease attending for an endoscopic procedure |
Who may qualify
- Ages
- 16 to 99 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Inflammatory Bowel Disease (IBD) affects at least one in 250 people of the UK population and the prevalence is rising.
Key inclusion criteria (1 listed)
- • Any adult patient aged 16 years or older who has consented to undergo endoscopic investigation where images are captured as part of routine clinical care.
Key exclusion criteria (2 listed)
- • Any patient under the age of 16
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy
Time frame: 5 years
To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy
6 secondary outcomes
a) To explore whether Artificial Intelligence can predict response to IBD therapies
Time frame: 5 years
b) To develop an endoscopic image repository to advance training and standardisation in endoscopic detection and characterisation of IBD.
Time frame: 5 years
c) To develop and assess methodologies for training and quality assurance of IBD diagnostic endoscopy
Time frame: 5 years
d) To evaluate comparisons in endoscopic image interpretation between endoscopist's
Time frame: 5 years
e) To develop deep learning algorithms and computer vision techniques to allow for automated measurement of quality metrics in endoscopy for IBD
Time frame: 5 years
f) To create a future robust research platform to ensure the above objectives are continuously developed as novel imaging techniques emerge over time.
Time frame: 5 years
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 15 Nov 2024.
- Individual participant dataSharing planned: Undecided
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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.
Shaji Sebastian principal investigator
Hull University Teaching Hospitals NHS Trust
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Inflammatory Bowel Disease 1, run by Hull University Teaching Hospitals 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 1 inclusion and 2 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: looking for participants now, as of the sponsor's last update on 15 Nov 2024. 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?
Contact Shaji Sebastian (01482 816764, shaji.sebastian@hey.nhs.uk), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To develop and train a convolutional neural network to detect and characterise disease severity of inflammatory bowel disease during endoscopy (time frame: 5 years). 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 15 Nov 2024.