Clinical trial NCT03973437: Development and Validation of a Deep Learning Algorithm to Evaluate Endoscopic Disease Activity of Ulcerative Colitis.
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.
Study type: INTERVENTIONAL
Overall status: UNKNOWN
Phase: NA
Conditions: Ulcerative Colitis
Keywords: Ulcerative Colitis
Deep Learning
Convolutional Neural Network
Lead sponsor: Shandong University
Sponsor class: OTHER
First posted: 2019-06-04
Enrollment: 200
Sex: ALL
Minimum age (years): 18
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: HEALTH_SERVICES_RESEARCH

Brief 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.

Detailed 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.

Eligibility criteria: 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

Primary outcomes:
measure: The scoring accuracy of Mayo ES in AI-associated group and conventional group.
timeFrame: 6 months
measure: The scoring accuracy of UCEIS in AI-associated group and conventional group.
timeFrame: 6 months
Secondary outcomes:
measure: The accuracy of mucosal healing judgements using Mayo ES in each group.
timeFrame: 6 months
measure: The accuracy of mucosal healing judgements using UCEIS in each group.
timeFrame: 6 months
Other outcomes:
Locations:
city: Jinan
country: China
facility: Qilu hosipital
state: Shandong
Study officials:
affiliation: Qilu Hospital of Shandong University
name: Xiuli Zuo, MD,PhD
role: PRINCIPAL_INVESTIGATOR
References: