Development and Validation of a Deep Learning Algorithm to Evaluate Endoscopic Disease Activity of Ulcerative Colitis.

Ulcerative Colitis Clinical Trial

Official title:

Real-time Evaluation of Severity and Mucosal Healing in Patients With Ulcerative Colitis by a Deep Learning Algorithm: a Multi-center Prospective Study.

Clinical Trial Details — Status: Not yet recruiting

Administrative data

• NCT number: NCT03973437
• Other study ID #: 2019-SDU-QILU-G002
• Status: Not yet recruiting
• Phase: N/A
• Start date: June 1, 2019
• Est. completion date: June 1, 2020

Study information

• Verified date: June 2019
• Source: Shandong University
• Contact: Xiuli Zuo, MD,PhD
• Phone: 15588818685
• Email: zuoxiuli[@]sina.com
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial Summary

The purpose of this study is to develop an artificial intelligence(AI) assisted scoring system, which can evaluate the disease severity and mucosal healing stage in patients with ulcerative colitis. Then testify whether this new scoring system can help physicians to enhance the accuracy of disease severity assessments in a multi-center clinical practice.

Description:

Ulcerative colitis is a non-specific chronic inflammation of gut characterized by referral bloody stool, diarrhea and abdominal pain. Endoscopic features of the disease severity and mucosal healing stage are strongly associated with treatment response and prognosis in the future. Currently, the Mayo endoscopic sub-score (Mayo ES) and Ulcerative colitis endoscopic index of severity (UCEIS) are commonly recommended to guide therapeutic adjustments. However, the accuracy of these scales greatly relies on intra-observer and inter-observer consistency for lack of objective measurements. Recently, deep learning algorithm based on convolutional neural network (CNN) has shown multiple potential in computer-aided detection and computer-aided diagnose of gastrointestinal lesions. Up to now, no randomized controlled trials have been conducted to evaluate the performance of deep learning algorithm for assessing disease activity in ulcerative colitis. This study aims to train a deep learnig algorithm to assess severity and mucosal healing stage of ulcerative colitis using the Mayo ES and UCEIS scale, then testify whether the engagement of AI can improve the evaluation accuracy of physicians in a multi-center clinical practice.

Recruitment information / eligibility

• Status: Not yet recruiting
• Enrollment: 200
• Est. completion date: June 1, 2020
• Est. primary completion date: December 31, 2019
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 70 Years

Eligibility

Inclusion Criteria:

• Patients with ulcerative colitis undergoing colonoscopy

Exclusion Criteria:

• Known or suspected bowel obstruction, stricture or perforation

• Compromised swallowing reflex or mental status

• Severe congestive heart failure (New York Heart Association class III or IV)

• Uncontrolled hypertension (systolic blood pressure > 170 mm Hg, diastolic blood pressure > 100 mm Hg)

• Pregnancy or lactation

• Hemodynamically unstable

• Colonic surgery history

• Bad bowel preparation (segmental BBPS<2)

• Unable to give informed consent

Related Conditions & MeSH terms

• Colitis
• Colitis, Ulcerative
• Ulcer
• Ulcerative Colitis

Intervention

Device:
• Artificial inteligence associated ulcerative colitis severity scoring system
Patients in this group go through a flexible colonoscopy under the AI monitoring device. During the withdrawal process, inflammatory lesions are detected by AI-associated scoring system. Pictures are automatically captured and analyzed by the computer. The Mayo ES and UCEIS sores will be calculated and presented on a second screen, providing a reference for the physician to evaluate the disease severity and mucosal healing stage of the patient. Biopsies will be taken from inflammatory region for histological examination. Videos will be recorded and re-evaluated by a group of experts to determine the final Mayo ES and UCEIS scores.
• Conventional human scoring
Patients in this group go through a conventional colonoscopy without the AI monitoring device. During the withdrawal process, physician evaluates the disease severity and mucosal healing stage of the patient according to his personal experience. Biopsies will be taken from inflammatory region for histological examination. Videos will be recorded and re-evaluated by a group of experts to determine the final Mayo ES and UCEIS scores.

Locations

Country	Name	City	State
China	Qilu hosipital	Jinan	Shandong

Sponsors (1)

Lead Sponsor	Collaborator
Shandong University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The scoring accuracy of Mayo ES in AI-associated group and conventional group.	The scoring accuracy of Mayo endoscopic sub-score (Mayo ES) in each group will be calculated using scores from expert group as reference standard. The Mayo ES is a 4-point scale, which classifies the endoscopic severity of ulcerative colitis into the following four categories: point 0 refers to normal or inactive disease, point 1 refers to mild disease with erythema, decreased vascular patterns and mild friability, point 2 refers to moderate disease with marked erythema, absence of vascular patterns, friability and erosions, point 3 refers to severe disease with spontaneous bleed and ulceration. The scoring accuracy of Mayo ES ranging from 0 to 3 point will be separately evaluated in both groups.	6 months
Primary	The scoring accuracy of UCEIS in AI-associated group and conventional group.	The scoring accuracy of Ulcerative colitis endoscopic index of severity (UCEIS) in each group will be separately calculated using scores from expert group as reference standard. The UCEIS is an 8-point scale consists of 3 parts: vascular pattern (point 0 refers to normal mucosa, point 1 refers to patchy obliteration of vascular pattern, point 2 refers to complete obliteration of vascular pattern), bleeding (point 0 refers to no visible blood, point 1 refers to some spots of coagulated blood, point 2 refers to free liquid, point 3 refers to frank blood in the lumen), erosions and ulcers (point 0 refers to normal mucosa, point 1 refers to erosions, point 2 refers to superficial ulcers, point 3 refers to deep ulcers. The total UCEIS score summarized by the above 3 parts will be analyzed. The scoring accuracy of UCEIS ranging from 0 to 8 point will be separately evaluated in both group.	6 months
Secondary	The accuracy of mucosal healing judgements using Mayo ES in each group.	The accuracy of mucosal healing judgements using Mayo ES will be calculated in each group. Assessments from expert group will be used as reference standard. Complete mucosal healing is defined as point 0 in Mayo ES scale, which refers to normal or inactive disease.	6 months
Secondary	The accuracy of mucosal healing judgements using UCEIS in each group.	The accuracy of mucosal healing judgements using UCEIS will also be calculated in each group. Assessments from expert group will be used as reference standard. Complete mucosal healing is defined as point 0 in UCEIS scale, which refers to normal vascular pattern without bleeding, erosions and ulceration.	6 months