Gastrointestinal DiseaseNCT04222439
Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases
A study testing AI for the Diagnosis of Gastrointestinal Diseases in people with Gastrointestinal Disease, run by Shandong University at 1 site in China.
Trial summary
The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of gastrointestinal diseases. 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 years and olderHealthy volunteers can take part
- Conditions the study is for
- Gastrointestinal Disease
- What is being tested?
- AI for the Diagnosis of Gastrointestinal Diseases
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2020to Feb 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 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
- Participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals.
Who cannot join
- -
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Qilu Hospital, Shandong University
Jinan, Shandong, 250012
Xiuli Zuo, PhD, 15588818685 ext. 053188369277, zuoxiuli@sdu.edu.cn
About this study
Official title: Development and Validation of a Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases
The purpose of this study is to develop and validate a deep learning algorithm for the diagnosis of gastrointestinal diseases. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.
Read the detailed description
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 all gastrointestinal diseases. This study aim to develop and validate a deep learning algorithm for the diagnosis of gastrointestinal diseases. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.
- Conditions
- Gastrointestinal Disease
- Being tested
- AI for the Diagnosis of Gastrointestinal Diseases
- Keywords
- Deep Learning, Central Neural Networks, Endoscopy, Gastrointestinal Disease
- Medical subject headings
- Gastrointestinal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AI for the Diagnosis of Gastrointestinal DiseasesDevice
After receiving standard preparation regimen, patients go through colonoscopy or gastroscopy under the AI monitoring device. The whole procedure is monitored by AI associated recognition system. Gastrointestinal diseases will be detect and diagnosis in which the AI device will automatically captured relevant images and report the site of each segment on the screen. Histology analysis 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 gastrointestinal endoscopy | Experimental | After receiving standard preparation regimen, patients go through colonoscopy or gastroscopy under the AI monitoring device. The whole procedure is monitored by AI associated recognition system. Gastrointestinal diseases will be detect and diagnosis in which the AI device will automatically captured relevant images and report the site of each segment on the screen. Histology analysis 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. Receives: Device: AI for the Diagnosis of Gastrointestinal Diseases |
What is being measured
The primary outcome is the main question the study is designed to answer.
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
Measured: 1 month
Secondary outcomes (4)
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.
Measured: 1 month
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
Measured: 1 month
The diagnostic positive predictive value of gastrointestinal diseases with deep learning algorithm.
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
Measured: 1 month
The diagnostic negative predictive value of gastrointestinal diseases with deep learning algorithm.
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
Measured: 1month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Jan 2020
- Study start1 Jan 2020
- Primary completionFeb 2020 (estimated)
- Study completionFeb 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastrointestinal DiseaseStudies 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
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 100,000 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
- NCT04222439
- Sponsor's protocol ID
- 2019-SDU-QILU-G710
- Status last verified
- Feb 2020
- First submitted
- 7 Jan 2020
- Last updated
- 18 Feb 2020
- 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 18 Feb 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of a Deep Learning Algorithm for the Diagnosis of Gastrointestinal Diseases
- ClinicalTrials.gov ID
- NCT04222439
- Acronym / sponsor ID
- 2019-SDU-QILU-G710
- Conditions
- Gastrointestinal Disease
- Keywords
- Deep Learning, Central Neural Networks, Endoscopy, Gastrointestinal Disease
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 100,000 participants (estimated)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Study start
- 1 Jan 2020 (actual)
- Primary completion
- Feb 2020 (estimated)
- Study completion
- Feb 2020 (estimated)
- First posted
- 10 Jan 2020 (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 for the diagnosis of gastrointestinal diseases. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.
Detailed description
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 all gastrointestinal diseases. This study aim to develop and validate a deep learning algorithm for the diagnosis of gastrointestinal diseases. Then, evaluate the accuracy this new artificial intelligence(AI) assisted recognition system in clinic practice.
Study design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| AI monitoring gastrointestinal endoscopy | Experimental | Device: AI for the Diagnosis of Gastrointestinal DiseasesAfter receiving standard preparation regimen, patients go through colonoscopy or gastroscopy under the AI monitoring device. The whole procedure is monitored by AI associated recognition system. Gastrointestinal diseases will be detect and diagnosis in which the AI device will automatically captured relevant images and report the site of each segment on the screen. Histology analysis 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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- Participants, aged 18 years or older, who had not had a previous endoscopy were retrieved from all participating hospitals.
Key exclusion criteria (1 listed)
- -
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 for the Diagnosis of Gastrointestinal DiseasesAfter receiving standard preparation regimen, patients go through colonoscopy or gastroscopy under the AI monitoring device. The whole procedure is monitored by AI associated recognition system. Gastrointestinal diseases will be detect and diagnosis in which the AI device will automatically captured relevant images and report the site of each segment on the screen. Histology analysis 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 gastrointestinal endoscopy |
Primary outcome
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
Time frame: 1 month
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm.
4 secondary outcomes
The diagnostic sensitivity of gastrointestinal diseases with deep learning algorithm.
Time frame: 1 month
The diagnostic specificity of gastrointestinal diseases with deep learning algorithm.
Time frame: 1 month
The diagnostic positive predictive value of gastrointestinal diseases with deep learning algorithm.
Time frame: 1 month
The diagnostic negative predictive value of gastrointestinal diseases with deep learning algorithm.
Time frame: 1month
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Feb 2020.
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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 AI for the Diagnosis of Gastrointestinal Diseases in people with Gastrointestinal Disease, 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 1 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 18 Feb 2020. 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@sdu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The diagnostic accuracy of gastrointestinal diseases with deep learning algorithm. (time frame: 1 month). 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 18 Feb 2020.