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

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

Clinical Phase

NA

Total Enrollment

200 participants

Study Classification

INTERVENTIONAL

Study Start Date

2019-06-01

Study Completion Date

2020-06-01

Brief Summary

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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.

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Detailed Description

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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.

Conditions

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Ulcerative Colitis

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

HEALTH_SERVICES_RESEARCH

Blinding Strategy

SINGLE

Outcome Assessors

Study Groups

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Artificial Intelligence assisted Scoring Group

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

Group Type EXPERIMENTAL

Artificial inteligence associated ulcerative colitis severity scoring system

Intervention Type DEVICE

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 Group

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

Group Type ACTIVE_COMPARATOR

Conventional human scoring

Intervention Type DEVICE

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.

Interventions

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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.

Intervention Type DEVICE

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.

Intervention Type DEVICE

Eligibility Criteria

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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

Minimum Eligible Age

18 Years

Maximum Eligible Age

70 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No