Stenosis of Bile DuctNCT05321472
Artificial Intelligence-assisted Common Bile Duct Stent Selection in Endoscopic Retrograde Cholangiopancreatography
An observational study of Stenosis of Bile Duct and Endoscopic Retrograde Cholangiopancreatography, run by Shandong University at 1 site in China.
Trial summary
Common bile duct stenosis is an important indication for endoscopic retrograde cholangiopancreatography(ERCP). Appropriate selection of bile duct stent size is not only conducive to successful stent implantation but also to improve the prognosis of patients. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Stenosis of Bile Duct, Endoscopic Retrograde Cholangiopancreatography
- How many people
- 600 participants
- Where
- 1 siteChina
- Study dates
- 20 Apr 2022to Apr 2023
- Phase
- Observational
- 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
- No healthy volunteers
- Adult, Older adult
Study population: The eligible images was randomly devided into two groups. One group for training the algorithm and the other group for testing the algorithm.
Who may be able to join
- Patients older than 18 years old who underwent ERCP
Who cannot join
- failed cholangiopancreatography caused by failed intubation, gastric retention, duodenal disease and so on
- patients proved no stenosis in common bile duct
- poor cholangiograms due to the lack of contrast agent or insufficient filling of contrast agent (cholangiograms without the completed CBD or thumbnails)
Trial locations and contacts
1 site in 1 country.
China1 site, 1 recruiting
- Recruiting
Department of Gastroenterology,QiLu Hospital,Shandong University
Jinan, Shandong, 250012
Rui Ji, MD, PhD, 18560086103, qljirui@email.sdu.edu.cn
About this study
Common bile duct stenosis is an important indication for endoscopic retrograde cholangiopancreatography(ERCP). Appropriate selection of bile duct stent size is not only conducive to successful stent implantation but also to improve the prognosis of patients. Currently, the selection of stent specifications is based on the operator's empirical estimation, which is not only not accurate but also increases the radiation exposure time, causing unnecessary harm to both the operator and the patient. Our objective is to develop an artificial intelligence algorithm to automatically select appropriate stent.
Read the detailed description
Endoscopic Retrograde Cholangiopancreatography (ERCP) is an operation with high risk. Common bile duct stone and stenosis are important indications. The quality control of ERCP is the key to improve its success rate and reduce complications, which has received great attention. In 2015, the American Society of Gastrointestinal Endoscopy/American College of Gastroenterology (ASGE/ACG) issued ERCP quality control indicators, among which biliary stent placement and radiographic fluoroscopy time are important intraoperative quality control indicators.
The selection of appropriate biliary stent size is not only conducive to successful stent implantation but also to improve the prognosis of patients. Choose a stent of appropriate length. The proximal side of the stent should be 1cm above the obstruction segment, and the distal tail should be located just outside the nipple. The length of the stent can be determined by measuring the distance between the proximal end of the obstruction and the nipple under X-ray.
Current stent size selection is based on the operator's empirical estimation :(1) estimate the distance by endoscope diameter or cone length or catheter marking; (2) By retracting the guidewire, calculate the distance of the guidewire retracting between two points to estimate the length of the stent.The long radiation exposure time results in unnecessary injuries to both the operator and the patient.
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| group for training the algorithm | This group of images is used for training the algorithm of the artificial intelligence | |
| group for testing the algorithm | This group of images is used for testing the algorithm of the artificial intelligence |
What is being measured
The primary outcome is the main question the study is designed to answer.
The accuracy of the calculated length of the stents by the artificial intelligence
The length of the stent was calculated "the length from the stenosis to the papilla+2cm".The length of the stent selected by experts is the gold standard
Measured: 6 months
Secondary outcomes (1)
The accuracy of the segmentation of the artificial intelligence
The accuracy of the segmentation of the common bile duct, duodenoscopy and stenosis lesions by the artificial intelligence
Measured: 4 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted11 Apr 2022
- Study start20 Apr 2022
- Primary completionAug 2022 (estimated)
- Study completionApr 2023 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Stenosis of Bile DuctStudies 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
- Observational
- Observational model
- Other
- Time perspective
- Retrospective
- Enrolment
- 600 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Shandong University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yanqing Li, Shandong University
- Investigators
- Yanqing Li, MD, PhD, Study chair, Qilu Hospital of Shandong University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05321472
- Sponsor's protocol ID
- 2021-SDU-QILU-090
- Status last verified
- May 2022
- First submitted
- 3 Apr 2022
- Last updated
- 6 Jul 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Adler DG, Lieb JG 2nd, Cohen J, Pike IM, Park WG, Rizk MK, Sawhney MS, Scheiman JM, Shaheen NJ, Sherman S, Wani S. Quality indicators for ERCP. Gastrointest Endosc. 2015 Jan;81(1):54-66. doi: 10.1016/j.gie.2014.07.056. Epub 2014 Dec 2. No abstract available.
PubMed 25480099Huang L, Lu X, Huang X, Zou X, Wu L, Zhou Z, Wu D, Tang D, Chen D, Wan X, Zhu Z, Deng T, Shen L, Liu J, Zhu Y, Gong D, Chen D, Zhong Y, Liu F, Yu H. Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicenter study. Endoscopy. 2021 May;53(5):491-498. doi: 10.1055/a-1244-5698. Epub 2020 Nov 9.
PubMed 32838430
Study at a glance
From the registry record, last updated by the sponsor on 6 Jul 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-assisted Common Bile Duct Stent Selection in Endoscopic Retrograde Cholangiopancreatography
- ClinicalTrials.gov ID
- NCT05321472
- Acronym / sponsor ID
- 2021-SDU-QILU-090
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 600 participants (estimated)
- Lead sponsor
- Shandong UniversityUniversity, hospital or other
- Study start
- 20 Apr 2022 (actual)
- Primary completion
- Aug 2022 (estimated)
- Study completion
- Apr 2023 (estimated)
- First posted
- 11 Apr 2022 (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.
Common bile duct stenosis is an important indication for endoscopic retrograde cholangiopancreatography(ERCP). Appropriate selection of bile duct stent size is not only conducive to successful stent implantation but also to improve the prognosis of patients. Currently, the selection of stent specifications is based on the operator's empirical estimation, which is not only not accurate but also increases the radiation exposure time, causing unnecessary harm to both the operator and the patient. Our objective is to develop an artificial intelligence algorithm to automatically select appropriate stent.
Detailed description
Endoscopic Retrograde Cholangiopancreatography (ERCP) is an operation with high risk. Common bile duct stone and stenosis are important indications. The quality control of ERCP is the key to improve its success rate and reduce complications, which has received great attention. In 2015, the American Society of Gastrointestinal Endoscopy/American College of Gastroenterology (ASGE/ACG) issued ERCP quality control indicators, among which biliary stent placement and radiographic fluoroscopy time are important intraoperative quality control indicators.
The selection of appropriate biliary stent size is not only conducive to successful stent implantation but also to improve the prognosis of patients. Choose a stent of appropriate length. The proximal side of the stent should be 1cm above the obstruction segment, and the distal tail should be located just outside the nipple. The length of the stent can be determined by measuring the distance between the proximal end of the obstruction and the nipple under X-ray.
Current stent size selection is based on the operator's empirical estimation :(1) estimate the distance by endoscope diameter or cone length or catheter marking; (2) By retracting the guidewire, calculate the distance of the guidewire retracting between two points to estimate the length of the stent.The long radiation exposure time results in unnecessary injuries to both the operator and the patient.
Study design
- Observational model
- Other
- Time perspective
- Retrospective
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| group for training the algorithm | – | –This group of images is used for training the algorithm of the artificial intelligence |
| group for testing the algorithm | – | –This group of images is used for testing the algorithm of the artificial intelligence |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- The eligible images was randomly devided into two groups. One group for training the algorithm and the other group for testing the algorithm.
Key inclusion criteria (1 listed)
- Patients older than 18 years old who underwent ERCP
Key exclusion criteria (3 listed)
- failed cholangiopancreatography caused by failed intubation, gastric retention, duodenal disease and so on
- patients proved no stenosis in common bile duct
- poor cholangiograms due to the lack of contrast agent or insufficient filling of contrast agent (cholangiograms without the completed CBD or thumbnails)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
The accuracy of the calculated length of the stents by the artificial intelligence
Time frame: 6 months
The length of the stent was calculated "the length from the stenosis to the papilla+2cm".The length of the stent selected by experts is the gold standard
1 secondary outcome
The accuracy of the segmentation of the artificial intelligence
Time frame: 4 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Jul 2022.
- Background: PubMed 25480099Adler DG, Lieb JG 2nd, Cohen J, Pike IM, Park WG, Rizk MK, Sawhney MS, Scheiman JM, Shaheen NJ, Sherman S, Wani S. Quality indicators for ERCP. Gastrointest Endosc. 2015 Jan;81(1):54-66. doi: 10.1016/j.gie.2014.07.056. Epub 2014 Dec 2. No abstract available.
- Background: PubMed 32838430Huang L, Lu X, Huang X, Zou X, Wu L, Zhou Z, Wu D, Tang D, Chen D, Wan X, Zhu Z, Deng T, Shen L, Liu J, Zhu Y, Gong D, Chen D, Zhong Y, Liu F, Yu H. Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicenter study. Endoscopy. 2021 May;53(5):491-498. doi: 10.1055/a-1244-5698. Epub 2020 Nov 9.
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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.
Yanqing Li, MD, PhD study chair
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?
An observational study of Stenosis of Bile Duct and Endoscopic Retrograde Cholangiopancreatography, 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 3 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 6 Jul 2022. 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 Yanqing Li, MD, PhD (053182169385, liyanqing@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 the calculated length of the stents by the artificial intelligence (time frame: 6 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT05321472, which also lists 2 linked publications.
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 6 Jul 2022.