Ulcerative Colitis Mayo Score With Artificial Intelligence

NCT ID: NCT05336773

Last Updated: 2022-04-20

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

500 participants

Study Classification

OBSERVATIONAL

Study Start Date

2022-04-30

Study Completion Date

2023-06-30

Brief Summary

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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

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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

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Ulcerative Colitis Colonoscopy Deep Learning

Study Design

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Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

1. Subjects were 18-72 years old, male and female;
2. Clinical diagnosis of ulcerative colitis;
3. The subjects underwent colonoscopy and the colonoscopy report was complete.

Exclusion Criteria

1. Subjects are younger than 18 years old or older than 72 years old;
2. Subjects underwent colectomy, ileostomy, colostomy, ileostomy, or other intestinal resection;
3. subjects with ambiguous diagnosis.
Minimum Eligible Age

18 Years

Maximum Eligible Age

72 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Third Military Medical University

OTHER

Sponsor Role lead

Responsible Party

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Yanling Wei

Associate chief physician, M D.

Responsibility Role PRINCIPAL_INVESTIGATOR

Principal Investigators

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Yanling Wei, Professor

Role: STUDY_DIRECTOR

Third Military Medical University

Locations

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Third Military Medical University

Chongqing, Chongqing Municipality, China

Site Status

Countries

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China

Central Contacts

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Yanling Wei, professor

Role: CONTACT

+8615310354666

Facility Contacts

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Yanling Wei, Professor

Role: primary

15310354666

References

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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. Therap Adv Gastroenterol. 2023 May 22;16:17562848231170945. doi: 10.1177/17562848231170945. eCollection 2023.

Reference Type DERIVED
PMID: 37251086 (View on PubMed)

Other Identifiers

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TMMU-DP--002

Identifier Type: -

Identifier Source: org_study_id

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