CClinicalTrials.gg
Status unknownNCT03973437Updated Jun 4, 2019

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

An interventional study of Artificial inteligence associated ulcerative colitis severity scoring system and Conventional human scoring in Ulcerative Colitis, sponsored by Shandong University. Status unknown at 1 site in China. Open to participants aged 18 Years to 70 Years. Per ClinicalTrials.gov, last updated 2019-06-04.

Sponsored by Shandong University · Not applicable, Interventional, and Health services research

The sponsor has not verified this record recently (last verified Jun 2019), so the status shown — last known as Not yet recruiting — may be out of date.
Phase
Not applicable
Study type
Interventional
Enrollment
200
Allocation
Randomized
Ages
18 Years to 70 Years
Sex
All
01

Study 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.

Read the 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.

02

Conditions studied

  • Ulcerative Colitis

Keywords

  • Ulcerative Colitis
  • Deep Learning
  • Convolutional Neural Network
03

In context

Colitis

1,073 studies on the registry are indexed under Colitis; 131 are open to participants now.

This study's planned enrollment of 200 is above the median of 60 across 771 interventional studies indexed under Colitis.

Browse Colitis studies →

Lead sponsor

Shandong University is the lead sponsor of 284 studies on the registry; 59 are open to participants now.

Counted across the registry records on this site, refreshed daily.

04

Who can participate

Ages eligible
18 Years to 70 Years
Sexes eligible
All
Accepts healthy volunteers
No

Inclusion criteria

  • Patients with ulcerative colitis undergoing colonoscopy

Exclusion criteria

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
05

Study design

Phase
Not applicable
Primary purpose
Health services research
Allocation
Randomized
Intervention model
Parallel assignment
Masking
Single (Outcomes assessor)
Enrollment
200 participants (estimated)

Study arms

  • Experimental
    Artificial Intelligence assisted Scoring Group

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

    Device: Artificial inteligence associated ulcerative colitis severity scoring system

  • Active comparator
    Conventional Human Scoring Group

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

    Device: Conventional human scoring

Interventions

  • DeviceArtificial 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.

  • DeviceConventional 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.

06

What researchers measure

Primary outcomes

  1. 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.

    Time frame: 6 months

  2. 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.

    Time frame: 6 months

Secondary outcomes

  1. 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.

    Time frame: 6 months

  2. 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.

    Time frame: 6 months

07

Study locations

1 site
  • Qilu hosipital
    Jinan, Shandong 257000, China
08

References and documents

Individual participant data

Plan to share: Yes

No publications or documents are linked to this record.

09

Updates

Tracking since Sep 25, 2026
No changes since tracking began. The registry record was last updated on Jun 4, 2019, before this site started recording changes on Sep 25, 2026. Its history is on ClinicalTrials.gov ↗
10

Registry details

Key details

Study ID
NCT03973437
Lead sponsor
Shandong University
Responsible party
Xiuli Zuo (director of Qilu Hospital gastroenterology department, Shandong University) — Principal investigator
First posted
Jun 4, 2019
Start date
Jun 1, 2019 (estimated)
Primary completion
Dec 31, 2019 (estimated)
Completion
Jun 1, 2020 (estimated)
Last update
Jun 4, 2019

Study contacts

Xiuli Zuo, MD,PhD
Contact
zuoxiuli@sina.com
15588818685
Xiuli Zuo, MD,PhD
principal investigator · Qilu Hospital of Shandong University

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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