Clinical trial NCT05336773: Ulcerative Colitis Mayo Score With Artificial Intelligence
Official title: Research on Artificial Intelligence-based Mayo Score Recognition System for Disease Activity Degree of Ulcerative Colitis Under Digestive Endoscopy
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Ulcerative Colitis
Colonoscopy
Deep Learning
Lead sponsor: Third Military Medical University
Sponsor class: OTHER
First posted: 2022-04-20
Enrollment: 500
Sex: ALL
Minimum age (years): 18
Maximum age (years): 72
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: RETROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
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.

Brief 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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: The accuracy of deep learning model in the training and validation datasets assessment of Mayo score in ulcerative colitis patients.
timeFrame: Through study completion, an average of 1 year.
Secondary outcomes:
measure: The accuracy and time efficiency of endoscopists assessment of Mayo score in ulcerative colitis patients.
timeFrame: Through study completion, an average of 1 year.
Other outcomes:
Locations:
city: Chongqing
country: China
facility: Third Military Medical University
state: Chongqing
Study officials:
affiliation: Third Military Medical University
name: Yanling Wei, Professor
role: STUDY_DIRECTOR
References: