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

Shandong University

Status: Unknown

Conditions: Ulcerative Colitis

Treatments: Device: Artificial inteligence associated ulcerative colitis severity scoring system; Device: Conventional human scoring

Study type: Interventional

Funder types: Other

Identifiers

NCT03973437

2019-SDU-QILU-G002

Details and patient eligibility

About

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.

Full 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.

Enrollment: 200 estimated patients

Sex: All

Ages: 18 to 70 years old

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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200 participants in 2 patient groups

Artificial Intelligence assisted Scoring Group

Experimental group

Description:

Patients in this group go through colonoscopy under the AI monitoring device.

Treatment:

Device: Artificial inteligence associated ulcerative colitis severity scoring system

Conventional Human Scoring Group

Active Comparator group

Description:

Patients in this group go through conventional colonoscopy without AI monitoring device.

Treatment:

Device: Conventional human scoring