Colonic DiseasesNCT04087824
Deep Learning Algorithm for Recognition of Colonic Segments.
A study testing AI assisted recognition of colonic segments in people with Colonic Diseases, run by Shandong University.
Trial summary
The purpose of this study is to develop and validate a deep learning algorithm to realize automatic recognition of colonic segments under conventional colonoscopy. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice. Read more about this study
- Who is it for?
- Ages 18 to 70 years
- Conditions the study is for
- Colonic Diseases
- What is being tested?
- AI assisted recognition of colonic segments
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 60 participants
- Where
- No sites listed yet
- Study dates
- 15 Sep 2019to 15 Dec 2019
- 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 aged 18-70 years undergoing conventional colonoscopy
Who cannot join
- Known or suspected bowel obstruction, stricture or perforation
- Compromised swallowing reflex or mental status
- Severe chronic renal failure(creatinine clearance < 30 ml/min)
- 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)
- Dehydration
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- Disturbance of electrolytes
- Pregnancy or lactation
- Hemodynamically unstable
- Unable to give informed consent
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Development and Validation of a Deep Learning Algorithm for Real-time Recognition of Colonic Segments.
The purpose of this study is to develop and validate a deep learning algorithm to realize automatic recognition of colonic segments under conventional colonoscopy. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.
Read the detailed description
Colonoscopy is recommended as a routine examination for colorectal cancer screening. Complete inspection of all colon segments is the basis of colonoscopy quality control, and furthermore improves the detection rates of small adenomas. Recently, deep learning algorithm based on central neural networks (CNN) has shown multiple potential in computer-aided detection and computer-aided diagnose of gastrointestinal lesions. However, there is still a blank in recognition of anatomic sites, which restricts the realization of AI-aided lesions detection and disease severity scoring. This study aim to train an algorithm to recognize key colonic segments, and testify the accuracy of each segments recognition as compared to endoscopic physicians.
- Conditions
- Colonic Diseases
- Being tested
- AI assisted recognition of colonic segments
- Keywords
- Deep learning, Colonoscopy, Central Neural Networks
- Medical subject headings
- Colonic Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI assisted recognition of colonic segmentsDevice
After receiving standard bowel preparation regimen, patients go through colonoscopy under the AI monitoring device. The whole withdrawal process is monitored by AI associated recognition system. Key colonic segments include ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum. When typical anatomic sites are detected, the AI device will automatically captured relevant images and report the name of each segment on the screen. The operating endoscopy expert will give the final answer and judge the performance of AI, which is set as a golden standard. Then all the AI captured images will be reviewed by human group, which consists of three to five experienced endoscopic physicians.
| Study group | Role | What they receive |
|---|---|---|
| AI monitoring colonoscopy | Experimental | Patients in this group go through colonoscopy under the AI monitoring device. Receives: Device: AI assisted recognition of colonic segments |
What is being measured
The primary outcome is the main question the study is designed to answer.
The accuracy of each colonic segment real-time recognition with deep learning algorithm.
The segmental recognition accuracy is the proportion of correctly recognized segments divided by the number of involved patients. The accuracy rate of ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum will be separately calculated.
Measured: 3 months.
Secondary outcomes (1)
The accuracy of total colonic segments recognition with deep learning algorithm as compared to endoscopic experts group.
The total recognition accuracy is the proportion of correctly recognized images divided by the number of AI captured images. Then all AI captured images will be reviewed by experts group to give a human evaluating rate. Two rates will be compared by student t test to analyze the difference.
Measured: 3 months.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted12 Sep 2019
- Study start15 Sep 2019 (estimated)
- Primary completion15 Nov 2019 (estimated)
- Study completion15 Dec 2019 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Colonic DiseasesStudies 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
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 60 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
- NCT04087824
- Sponsor's protocol ID
- 2019SDU-QILU-G003
- Status last verified
- Sep 2019
- First submitted
- 11 Sep 2019
- Last updated
- 12 Sep 2019
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 12 Sep 2019. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Deep Learning Algorithm for Real-time Recognition of Colonic Segments.
- ClinicalTrials.gov ID
- NCT04087824
- Acronym / sponsor ID
- 2019SDU-QILU-G003
- Conditions
- Colonic Diseases
- Keywords
- Deep learning, Colonoscopy, Central Neural Networks
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 60 participants (estimated)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Study start
- 15 Sep 2019 (estimated)
- Primary completion
- 15 Nov 2019 (estimated)
- Study completion
- 15 Dec 2019 (estimated)
- First posted
- 12 Sep 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 and validate a deep learning algorithm to realize automatic recognition of colonic segments under conventional colonoscopy. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.
Detailed description
Colonoscopy is recommended as a routine examination for colorectal cancer screening. Complete inspection of all colon segments is the basis of colonoscopy quality control, and furthermore improves the detection rates of small adenomas. Recently, deep learning algorithm based on central neural networks (CNN) has shown multiple potential in computer-aided detection and computer-aided diagnose of gastrointestinal lesions. However, there is still a blank in recognition of anatomic sites, which restricts the realization of AI-aided lesions detection and disease severity scoring. This study aim to train an algorithm to recognize key colonic segments, and testify the accuracy of each segments recognition as compared to endoscopic physicians.
Study design
- Primary purpose
- Health services research
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI monitoring colonoscopy | Experimental | Device: AI assisted recognition of colonic segmentsPatients in this group go through colonoscopy under the 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 aged 18-70 years undergoing conventional colonoscopy
Key exclusion criteria (10 listed)
- Known or suspected bowel obstruction, stricture or perforation
- Compromised swallowing reflex or mental status
- Severe chronic renal failure(creatinine clearance < 30 ml/min)
- 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)
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 |
|---|---|---|
| AI assisted recognition of colonic segmentsAfter receiving standard bowel preparation regimen, patients go through colonoscopy under the AI monitoring device. The whole withdrawal process is monitored by AI associated recognition system. Key colonic segments include ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum. When typical anatomic sites are detected, the AI device will automatically captured relevant images and report the name of each segment on the screen. The operating endoscopy expert will give the final answer and judge the performance of AI, which is set as a golden standard. Then all the AI captured images will be reviewed by human group, which consists of three to five experienced endoscopic physicians. | Device | AI monitoring colonoscopy |
Primary outcome
The accuracy of each colonic segment real-time recognition with deep learning algorithm.
Time frame: 3 months.
The segmental recognition accuracy is the proportion of correctly recognized segments divided by the number of involved patients. The accuracy rate of ileocecal valve, ascending colon, transverse colon, descending colon, sigmoid colon and rectum will be separately calculated.
1 secondary outcome
The accuracy of total colonic segments recognition with deep learning algorithm as compared to endoscopic experts group.
Time frame: 3 months.
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 12 Sep 2019.
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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
Questions about this trial
What is this clinical trial studying?
A study testing AI assisted recognition of colonic segments in people with Colonic Diseases, run by Shandong University.
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 10 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 12 Sep 2019. Status can change between updates, so confirm with the study contact before making plans.
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@sdu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The accuracy of each colonic segment real-time recognition with deep learning algorithm. (time frame: 3 months.). 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 12 Sep 2019.