Ulcerative Colitis Mayo Score With Artificial Intelligence

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Ulcerative Colitis; Colonoscopy; Deep Learning

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 Type: Observational
• Enrollment (Anticipated): 500

Contacts and Locations

• Study Contact: Name: Yanling Wei, professor; Phone Number: +8615310354666; Email: lingzi016@tmmu.edu.cn

Study Locations

• China
  • Chongqing
    • Chongqing, Chongqing, China, 400042
      • Third Military Medical University
      • Contact: Yanling Wei, Professor; Phone Number: 15310354666; Email: lingzi016@126.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years to 72 years (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• 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.

Description

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.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Through study completion, an average of 1 year.

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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.	Through study completion, an average of 1 year.

Collaborators and Investigators

• Sponsor: Third Military Medical University
• Investigators: Study Director: Yanling Wei, Professor, Third Military Medical University

Study record dates

Study Major Dates

• Study Start (ANTICIPATED): April 1, 2022
• Primary Completion (ANTICIPATED): December 1, 2022
• Study Completion (ANTICIPATED): June 1, 2023

Study Registration Dates

• First Submitted: February 27, 2022
• First Submitted That Met QC Criteria: April 14, 2022
• First Posted (ACTUAL): April 20, 2022

Study Record Updates

• Last Update Posted (ACTUAL): April 20, 2022
• Last Update Submitted That Met QC Criteria: April 14, 2022
• Last Verified: April 1, 2022

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Digestive System Diseases; Pathologic Processes; Gastrointestinal Diseases; Gastroenteritis; Colonic Diseases; Intestinal Diseases; Inflammatory Bowel Diseases; Ulcer; Colitis; Colitis, Ulcerative
• Other Study ID Numbers: TMMU-DP--002

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No