Ulcerative ColitisNCT05336773
Ulcerative Colitis Mayo Score With Artificial Intelligence
An observational study of Ulcerative Colitis and Colonoscopy, run by Third Military Medical University at 1 site in China.
Trial summary
This project will use deep learning to classify colonoscopy images of different severity of ulcerative colitis, so as to assist clinicians in the accurate diagnosis of ulcerative colitis. Read more about this study
- Who is it for?
- Ages 18 to 72 yearsHealthy volunteers can take part
- Conditions the study is for
- Ulcerative Colitis, Colonoscopy, Deep Learning
- How many people
- 500 participants
- Where
- 1 siteChina
- Study dates
- Apr 2022to Jun 2023
- Phase
- Observational
- Are there results?
- None posted in the registry
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 18 to 72 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The images were retrieved from the endoscopy database of Army Medical Center of PLA and obtained from the colonoscopy of patients with ulcerative colitis who met the inclusion criteria. The data images were used to establish and verify the model of ai-assisted recognition system.
Who may be able to join
- Subjects were 18-72 years old, male and female;
- Clinical diagnosis of ulcerative colitis;
- The subjects underwent colonoscopy and the colonoscopy report was complete.
Who cannot join
- Subjects are younger than 18 years old or older than 72 years old;
- Subjects underwent colectomy, ileostomy, colostomy, ileostomy, or other intestinal resection;
- subjects with ambiguous diagnosis.
Trial locations and contacts
1 site in 1 country.
China1 site
Third Military Medical University
Chongqing, Chongqing Municipality, 400042
Yanling Wei, Professor, 15310354666, lingzi016@126.com
About this study
Official title: Research on Artificial Intelligence-based Mayo Score Recognition System for Disease Activity Degree of Ulcerative Colitis Under Digestive Endoscopy
This project will use deep learning to classify colonoscopy images of different severity of ulcerative colitis, so as to assist clinicians in the accurate diagnosis of ulcerative colitis.
Read the detailed description
In this project, artificial intelligence was used to colonoscopic images of patients with ulcerative colitis with different disease activity levels and classify them according to the evaluation standard Mayo score to assist endoscopists in identifying disease activity levels of patients with ulcerative colitis during colonoscopy. It can help clinical endoscopists to accurately identify, and the visualization technology of artificial intelligence category response map can comprehensively display the areas with high importance for deep network classification results, and visualize the experimental lesion sites, thus effectively verifying the reliability and interpretability of deep network. This study can provide strong support for accurate identification of disease activity in clinical ulcerative colitis, effectively reduce the workload of clinicians, and provide a convenient, effective and practical clinical teaching tool.
- Conditions
- Ulcerative Colitis, Colonoscopy, Deep Learning
- Medical subject headings
- Colitis, Ulcerative
What is being measured
The primary outcome is the main question the study is designed to answer.
The accuracy of deep learning model in the training and validation datasets assessment of Mayo score in ulcerative colitis patients.
In the training and validation datasets, we plotted the AUC (area under curve) for Mayo 0, Mayo 1, Mayo 2, and Mayo 3 to evaluate our model objectively.
Measured: Through study completion, an average of 1 year.
Secondary outcomes (1)
The accuracy and time efficiency of endoscopists assessment of Mayo score in ulcerative colitis patients.
The dataets were randomly assigned to endoscopists. All endoscopists were trained in diagnostic studies, finished both clinical and specific endoscopic training, and were not involved in the enrollment and labeling of the patients and images. During the comparison test, all data were randomized and deidentified beforehand. The average time spent by 10 endoscopists in diagnosing the test dataset in the deep learning model and the number of correct cases were analyzed.
Measured: Through study completion, an average of 1 year.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted20 Apr 2022
- Study startApr 2022 (estimated)
- Primary completionDec 2022 (estimated)
- Study completionJun 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Ulcerative ColitisStudies 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
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 500 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Third Military Medical University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yanling Wei, Third Military Medical University
- Investigators
- Yanling Wei, Professor, Study director, Third Military Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05336773
- Sponsor's protocol ID
- TMMU-DP--002
- Status last verified
- Apr 2022
- First submitted
- 27 Feb 2022
- Last updated
- 20 Apr 2022
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Qi J, Ruan G, Ping Y, Xiao Z, Liu K, Cheng Y, Liu R, Zhang B, Zhi M, Chen J, Xiao F, Zhao T, Li J, Zhang Z, Zou Y, Cao Q, Nian Y, Wei Y. Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis. Ther Adv Gastroenterol. 2023 May 22;16:17562848231170945. doi: 10.1177/17562848231170945. eCollection 2023.
PubMed 37251086
Study at a glance
From the registry record, last updated by the sponsor on 20 Apr 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Research on Artificial Intelligence-based Mayo Score Recognition System for Disease Activity Degree of Ulcerative Colitis Under Digestive Endoscopy
- ClinicalTrials.gov ID
- NCT05336773
- Acronym / sponsor ID
- TMMU-DP--002
- Conditions
- Ulcerative Colitis, Colonoscopy, Deep Learning
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 500 participants (estimated)
- Lead sponsor
- Third Military Medical UniversityUniversity, hospital or other
- Study start
- Apr 2022 (estimated)
- Primary completion
- Dec 2022 (estimated)
- Study completion
- Jun 2023 (estimated)
- First posted
- 20 Apr 2022 (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.
This project will use deep learning to classify colonoscopy images of different severity of ulcerative colitis, so as to assist clinicians in the accurate diagnosis of ulcerative colitis.
Detailed description
In this project, artificial intelligence was used to colonoscopic images of patients with ulcerative colitis with different disease activity levels and classify them according to the evaluation standard Mayo score to assist endoscopists in identifying disease activity levels of patients with ulcerative colitis during colonoscopy. It can help clinical endoscopists to accurately identify, and the visualization technology of artificial intelligence category response map can comprehensively display the areas with high importance for deep network classification results, and visualize the experimental lesion sites, thus effectively verifying the reliability and interpretability of deep network. This study can provide strong support for accurate identification of disease activity in clinical ulcerative colitis, effectively reduce the workload of clinicians, and provide a convenient, effective and practical clinical teaching tool.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 to 72 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The images were retrieved from the endoscopy database of Army Medical Center of PLA and obtained from the colonoscopy of patients with ulcerative colitis who met the inclusion criteria. The data images were used to establish and verify the model of ai-assisted recognition system.
Key inclusion criteria (3 listed)
- Subjects were 18-72 years old, male and female;
- Clinical diagnosis of ulcerative colitis;
- The subjects underwent colonoscopy and the colonoscopy report was complete.
Key exclusion criteria (3 listed)
- Subjects are younger than 18 years old or older than 72 years old;
- Subjects underwent colectomy, ileostomy, colostomy, ileostomy, or other intestinal resection;
- subjects with ambiguous diagnosis.
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
The accuracy of deep learning model in the training and validation datasets assessment of Mayo score in ulcerative colitis patients.
Time frame: Through study completion, an average of 1 year.
In the training and validation datasets, we plotted the AUC (area under curve) for Mayo 0, Mayo 1, Mayo 2, and Mayo 3 to evaluate our model objectively.
1 secondary outcome
The accuracy and time efficiency of endoscopists assessment of Mayo score in ulcerative colitis patients.
Time frame: Through study completion, an average of 1 year.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Apr 2022.
- Derived from this study: PubMed 37251086Qi J, Ruan G, Ping Y, Xiao Z, Liu K, Cheng Y, Liu R, Zhang B, Zhi M, Chen J, Xiao F, Zhao T, Li J, Zhang Z, Zou Y, Cao Q, Nian Y, Wei Y. Development and validation of a deep learning-based approach to predict the Mayo endoscopic score of ulcerative colitis. Ther Adv Gastroenterol. 2023 May 22;16:17562848231170945. doi: 10.1177/17562848231170945. eCollection 2023.
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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.
Yanling Wei, Professor study director
Third Military Medical University
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 Ulcerative Colitis and Colonoscopy, run by Third Military Medical University at 1 site in China.
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 3 inclusion and 3 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: status not confirmed, as of the sponsor's last update on 20 Apr 2022. 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 China. 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?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Yanling Wei, professor (+8615310354666, lingzi016@tmmu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The accuracy of deep learning model in the training and validation datasets assessment of Mayo score in ulcerative colitis patients. (time frame: Through study completion, an average of 1 year.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05336773, which also lists 1 linked publication.
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 20 Apr 2022.