Ulcerative ColitisNCT03973437
Development and Validation of a Deep Learning Algorithm to Evaluate Endoscopic Disease Activity of Ulcerative Colitis.
A study testing Artificial inteligence associated ulcerative colitis severity scoring system and Conventional human scoring in people with Ulcerative Colitis, run by Shandong University at 1 site in China.
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. Read more about this study
- Who is it for?
- Ages 18 to 70 years
- Conditions the study is for
- Ulcerative Colitis
- What is being tested?
- Artificial inteligence associated ulcerative colitis severity scoring system and Conventional human scoring
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 200 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2019to 1 Jun 2020
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 70 years
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Patients with ulcerative colitis undergoing colonoscopy
Who cannot join
- 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
Show all 9Show fewer
- Colonic surgery history
- Bad bowel preparation (segmental BBPS<2)
- Unable to give informed consent
Trial locations and contacts
1 site in 1 country.
About this study
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.
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.
- Conditions
- Ulcerative Colitis
- Being tested
- Artificial inteligence associated ulcerative colitis severity scoring system, Conventional human scoring
- Keywords
- Ulcerative Colitis, Deep Learning, Convolutional Neural Network
- Medical subject headings
- Colitis, Ulcerative
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Artificial inteligence associated ulcerative colitis severity scoring systemDevice
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 scoringDevice
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.
| Study group | Role | What they receive |
|---|---|---|
| Artificial Intelligence assisted Scoring Group | Experimental | Patients in this group go through colonoscopy under the AI monitoring device. Receives: Device: Artificial inteligence associated ulcerative colitis severity scoring system |
| Conventional Human Scoring Group | Active comparator | Patients in this group go through conventional colonoscopy without AI monitoring device. Receives: Device: Conventional human scoring |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 6 months
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.
Measured: 6 months
Secondary outcomes (2)
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.
Measured: 6 months
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.
Measured: 6 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Jun 2019
- Study start1 Jun 2019 (estimated)
- Primary completion31 Dec 2019 (estimated)
- Study completion1 Jun 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Ulcerative ColitisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind
- Who is masked
- Outcomes assessor
- Enrolment
- 200 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shandong University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Xiuli Zuo, Shandong University
- Investigators
- Xiuli Zuo, MD,PhD, Principal investigator, Qilu Hospital of Shandong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03973437
- Sponsor's protocol ID
- 2019-SDU-QILU-G002
- Status last verified
- Jun 2019
- First submitted
- 27 May 2019
- Last updated
- 4 Jun 2019
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes
Study at a glance
From the registry record, last updated by the sponsor on 4 Jun 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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.
- ClinicalTrials.gov ID
- NCT03973437
- Acronym / sponsor ID
- 2019-SDU-QILU-G002
- Conditions
- Ulcerative Colitis
- Keywords
- Ulcerative Colitis, Deep Learning, Convolutional Neural Network
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 200 participants (estimated)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Study start
- 1 Jun 2019 (estimated)
- Primary completion
- 31 Dec 2019 (estimated)
- Study completion
- 1 Jun 2020 (estimated)
- First posted
- 4 Jun 2019 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Single blind (outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Artificial Intelligence assisted Scoring Group | Experimental | Device: Artificial inteligence associated ulcerative colitis severity scoring systemPatients in this group go through colonoscopy under the AI monitoring device. |
| Conventional Human Scoring Group | Active comparator | Device: Conventional human scoringPatients in this group go through conventional colonoscopy without AI monitoring device. |
Who may qualify
- Ages
- 18 to 70 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Patients with ulcerative colitis undergoing colonoscopy
Key exclusion criteria (9 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Artificial inteligence associated ulcerative colitis severity scoring systemPatients 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. | Device | Artificial Intelligence assisted Scoring Group |
| Conventional human scoringPatients 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. | Device | Conventional Human Scoring Group |
Primary outcome
The scoring accuracy of Mayo ES in AI-associated group and conventional group.
Time frame: 6 months
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.
The scoring accuracy of UCEIS in AI-associated group and conventional group.
Time frame: 6 months
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.
2 secondary outcomes
The accuracy of mucosal healing judgements using Mayo ES in each group.
Time frame: 6 months
The accuracy of mucosal healing judgements using UCEIS in each group.
Time frame: 6 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Jun 2019.
- Individual participant dataSharing planned: Yes
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Xiuli Zuo, MD,PhD principal investigator
Qilu Hospital of Shandong University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Artificial inteligence associated ulcerative colitis severity scoring system and Conventional human scoring in people with Ulcerative Colitis, run by Shandong University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 9 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 4 Jun 2019. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
Contact Xiuli Zuo, MD,PhD (15588818685, zuoxiuli@sina.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The scoring accuracy of Mayo ES in AI-associated group and conventional group. (time frame: 6 months). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 4 Jun 2019.