Common Bile Duct StonesNCT05381064
Effect of a Deep Learning-based Bile Duct Scanning System on the Diagnostic Accuracy of Common Bile Duct Stones During Examination by Novice Ultrasound Endoscopists
A study testing artificial intelligence assistance system in people with Common Bile Duct Stones, run by Renmin Hospital of Wuhan University.
Trial summary
The bile duct scanning system based on deep learning can prompt endoscopists to scan standard stations and identify bile ducts and stones in real time. The purpose of this study is to evaluate the effectiveness and safety of the proposed deep learning-based bile duct scanning system in improving the diagnostic… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Common Bile Duct Stones
- What is being tested?
- artificial intelligence assistance system
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 184 participants
- Where
- No sites listed yet
- Study dates
- 1 Jun 2022to 1 Jan 2024
- 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
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- Males and females aged 18 years and older who are suspected of having common bile duct stones at intermediate to low risk, where intermediate-risk patients are those with normal liver function but with abdominal ultrasound suggestive of bile duct dilatation, and low-risk patients are those with normal abdominal ultrasound and liver function but whose physicians still suspect common bile duct stones;
- Able to read, understand and sign an informed consent;
- The investigator believes that the subjects can understand the process of the clinical study, are willing and able to complete all study procedures and follow-up visits, and cooperate with the study procedures.
Who cannot join
- Patients at high risk of common bile duct stones. High-risk patients are those with common bile duct stones detected by abdominal ultrasound, patients with manifestations of cholangitis or hospitalized patients with a history of gallbladder stones with pain, bile duct dilatation and jaundice;
- Have drug or alcohol abuse or mental disorder in the last 5 years;
- Pregnant or lactating women;
- Altered anatomy due to previous history of upper gastrointestinal surgery;
- Patients with advanced tumors resulting in abnormal upper gastrointestinal anatomy;
- High-risk diseases or other special conditions that the investigator considers the subject unsuitable for participation in the clinical trial.
Trial locations and contacts
No sites are listed yet.
About this study
Official title: Effect of a Deep Learning-based Bile Duct Scanning System on the Diagnostic Accuracy of Common Bile Duct Stones During Examination by Novice Ultrasound Endoscopists: a Single-center, Tandem, Randomized Controlled Trial
The bile duct scanning system based on deep learning can prompt endoscopists to scan standard stations and identify bile ducts and stones in real time. The purpose of this study is to evaluate the effectiveness and safety of the proposed deep learning-based bile duct scanning system in improving the diagnostic accuracy of common bile duct stones and reducing the rate of missed gallstones during bile duct scanning by novice ultrasound endoscopists in a single-center, tandem, randomized controlled trial
Read the detailed description
The incidence of gallstones has been increasing in recent years, up to 10-15% in developed countries, and is still increasing at a rate of 0.6% per year. It is estimated that common bile duct stones (CBDS) are present in about 10-20% of patients with symptomatic bile duct stones. Each year, common bile duct stones lead to acute complications such as biliary obstruction, cholangitis and acute pancreatitis in a large number of patients, seriously endangering their lives and health. In addition, Diagnosis Related Group (DRG) analysis shows that each episode of common bile duct stones costs $9,000, and acute pancreatitis that progresses from common bile duct stones can result in 275,000 hospitalizations annually, incurring $2.6 billion in costs and imposing a significant economic and health burden on society. Therefore, timely diagnosis of common bile duct stones and intervention for them is crucial. Endoscopic retrograde cholangiopancreatography (ERCP) is the method of choice for the diagnosis and treatment of CBDS, and guidelines recommend stone extraction for all patients with CBDS who are physically fit enough to tolerate ERCP operations. However, ERCP is a highly demanding and risky operation with the potential for serious complications such as PEP (incidence 2.6-3.5%). How to diagnose choledocholithiasis early and accurately, achieve timely intervention to improve prognosis, and avoid unnecessary medical operations to reduce risks are the challenges we are currently trying to solve.
The guidelines recommend ultrasound endoscopy (EUS) or magnetic resonance cholangiopancreatography (MRCP) to determine the presence of CBDS, depending on the local level of care, for patients in the intermediate-risk group for CBDS and for patients in the low-risk group whose physicians still have a high suspicion of CBDS. sensitivity. In addition, a cost-effectiveness analysis showed that MRCP would be the preferred test when the predicted probability of CBDS is less than 40%, while EUS is the preferred test when the predicted probability is 40%-90%. Compared to MRCP, EUS has a wide range of applicability but a steep learning curve. ASGE states that a minimum of 225 EUS operations are required to qualify, while the ESGE states that a minimum of 300 operations are required. However, this experience can only be gained at training centers that perform a large number of cases. Thus, the training of novice physicians in resource-limited areas is a huge challenge, which leaves a significant shortage of experienced ultrasound endoscopists with poor performance in the actual diagnosis of common bile duct stones, greatly limiting the popularity of ultrasound endoscopy.
The purpose of this study is to evaluate the effectiveness and safety of the proposed deep learning-based bile duct scanning system in improving the diagnostic accuracy of common bile duct stones and reducing the rate of missed gallstones during bile duct scanning by novice ultrasound endoscopists through a single-center, tandem, randomized controlled trial
- Conditions
- Common Bile Duct Stones
- Being tested
- artificial intelligence assistance system
- Keywords
- Ultrasound Endoscopy, Common bile duct stones, artificial intelligence
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
artificial intelligence assistance systemDevice
A deep learning-based bile duct scanning system that can prompt endoscopists to scan standard stations, identify bile ducts and stones in real time
| Study group | Role | What they receive |
|---|---|---|
| novices with AI-assisted system, Then experts without AI-assisted system | Experimental | The patient is first scanned by a novice endoscopist with the assistance of a deep learning-based bile duct scanning system during the examination, and then rescanned by a specialist without the assistance of AI. Receives: Device: artificial intelligence assistance system |
| experts without AI-assisted system, Then novices with AI-assisted system | Experimental | The patient is first scanned by a specialist without the assistance of AI and then rescanned by a novice endoscopist with the assistance of a deep learning-based bile duct scanning system during the examination. Receives: Device: artificial intelligence assistance system |
What is being measured
The primary outcome is the main question the study is designed to answer.
Accuracy of diagnosis of common bile duct stones in patients with low and intermediate risk by novice combined with AI-assisted and expert
Measured: the time novice finished operation and expert finished operation
Secondary outcomes (8)
Sensitivity, specificity, NPV, and PPV for the diagnosis of common bile duct stones in low and intermediate risk patients
Measured: the time novice finished operation and expert finished operation
Detection rate of gallstone lesions
Measured: the time novice finished operation and expert finished operation
Missed detection rate of gallstone lesions
Measured: the time novice finished operation and expert finished operation
Detection rate of bile duct lesions(all bile duct lesions including gallstones)
Measured: the time novice finished operation and expert finished operation
Missed rate of bile duct lesion(all bile duct lesions including gallstones)
Measured: the time novice finished operation and expert finished operation
Number of bile duct standard station scans
Measured: the time novice finished operation and expert finished operation
scan time
Measured: the time novice finished operation and expert finished operation
Incidence of Adverse Events
Measured: the time novice finished operation and expert finished operation
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 May 2022
- Study start1 Jun 2022 (estimated)
- Primary completion1 Dec 2023 (estimated)
- Study completion1 Jan 2024 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Common Bile Duct StonesStudies 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
- Randomised
- Intervention model
- Crossover
- Masking
- Double blind
- Who is masked
- Participant, Outcomes assessor
- Enrolment
- 184 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Renmin Hospital of Wuhan University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Yu Honggang, Doctor, Principal investigator, Renmin Hospital of Wuhan University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05381064
- Sponsor's protocol ID
- EA-22-004
- Status last verified
- May 2022
- First submitted
- 13 May 2022
- Last updated
- 19 May 2022
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 19 May 2022. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Effect of a Deep Learning-based Bile Duct Scanning System on the Diagnostic Accuracy of Common Bile Duct Stones During Examination by Novice Ultrasound Endoscopists: a Single-center, Tandem, Randomized Controlled Trial
- ClinicalTrials.gov ID
- NCT05381064
- Acronym / sponsor ID
- EA-22-004
- Conditions
- Common Bile Duct Stones
- Keywords
- Ultrasound Endoscopy, Common bile duct stones, artificial intelligence
- Study type
- Interventional
- Recruitment status
- Unknown status
- Enrolment
- 184 participants (estimated)
- Lead sponsor
- Renmin Hospital of Wuhan UniversityUniversity, hospital or other
- Study start
- 1 Jun 2022 (estimated)
- Primary completion
- 1 Dec 2023 (estimated)
- Study completion
- 1 Jan 2024 (estimated)
- First posted
- 19 May 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.
The bile duct scanning system based on deep learning can prompt endoscopists to scan standard stations and identify bile ducts and stones in real time. The purpose of this study is to evaluate the effectiveness and safety of the proposed deep learning-based bile duct scanning system in improving the diagnostic accuracy of common bile duct stones and reducing the rate of missed gallstones during bile duct scanning by novice ultrasound endoscopists in a single-center, tandem, randomized controlled trial
Detailed description
The incidence of gallstones has been increasing in recent years, up to 10-15% in developed countries, and is still increasing at a rate of 0.6% per year. It is estimated that common bile duct stones (CBDS) are present in about 10-20% of patients with symptomatic bile duct stones. Each year, common bile duct stones lead to acute complications such as biliary obstruction, cholangitis and acute pancreatitis in a large number of patients, seriously endangering their lives and health. In addition, Diagnosis Related Group (DRG) analysis shows that each episode of common bile duct stones costs $9,000, and acute pancreatitis that progresses from common bile duct stones can result in 275,000 hospitalizations annually, incurring $2.6 billion in costs and imposing a significant economic and health burden on society. Therefore, timely diagnosis of common bile duct stones and intervention for them is crucial. Endoscopic retrograde cholangiopancreatography (ERCP) is the method of choice for the diagnosis and treatment of CBDS, and guidelines recommend stone extraction for all patients with CBDS who are physically fit enough to tolerate ERCP operations. However, ERCP is a highly demanding and risky operation with the potential for serious complications such as PEP (incidence 2.6-3.5%). How to diagnose choledocholithiasis early and accurately, achieve timely intervention to improve prognosis, and avoid unnecessary medical operations to reduce risks are the challenges we are currently trying to solve.
The guidelines recommend ultrasound endoscopy (EUS) or magnetic resonance cholangiopancreatography (MRCP) to determine the presence of CBDS, depending on the local level of care, for patients in the intermediate-risk group for CBDS and for patients in the low-risk group whose physicians still have a high suspicion of CBDS. sensitivity. In addition, a cost-effectiveness analysis showed that MRCP would be the preferred test when the predicted probability of CBDS is less than 40%, while EUS is the preferred test when the predicted probability is 40%-90%. Compared to MRCP, EUS has a wide range of applicability but a steep learning curve. ASGE states that a minimum of 225 EUS operations are required to qualify, while the ESGE states that a minimum of 300 operations are required. However, this experience can only be gained at training centers that perform a large number of cases. Thus, the training of novice physicians in resource-limited areas is a huge challenge, which leaves a significant shortage of experienced ultrasound endoscopists with poor performance in the actual diagnosis of common bile duct stones, greatly limiting the popularity of ultrasound endoscopy.
The purpose of this study is to evaluate the effectiveness and safety of the proposed deep learning-based bile duct scanning system in improving the diagnostic accuracy of common bile duct stones and reducing the rate of missed gallstones during bile duct scanning by novice ultrasound endoscopists through a single-center, tandem, randomized controlled trial
Study design
- Primary purpose
- Diagnosis
- Allocation
- Randomised
- Intervention model
- Crossover
- Masking
- Double blind (participant, outcomes assessor)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| novices with AI-assisted system, Then experts without AI-assisted system | Experimental | Device: artificial intelligence assistance systemThe patient is first scanned by a novice endoscopist with the assistance of a deep learning-based bile duct scanning system during the examination, and then rescanned by a specialist without the assistance of AI. |
| experts without AI-assisted system, Then novices with AI-assisted system | Experimental | Device: artificial intelligence assistance systemThe patient is first scanned by a specialist without the assistance of AI and then rescanned by a novice endoscopist with the assistance of a deep learning-based bile duct scanning system during the examination. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (3 listed)
- Males and females aged 18 years and older who are suspected of having common bile duct stones at intermediate to low risk, where intermediate-risk patients are those with normal liver function but with abdominal ultrasound suggestive of bile duct dilatation, and low-risk patients are those with normal abdominal ultrasound and liver function but whose physicians still suspect common bile duct stones;
- Able to read, understand and sign an informed consent;
- The investigator believes that the subjects can understand the process of the clinical study, are willing and able to complete all study procedures and follow-up visits, and cooperate with the study procedures.
Key exclusion criteria (6 listed)
- Patients at high risk of common bile duct stones. High-risk patients are those with common bile duct stones detected by abdominal ultrasound, patients with manifestations of cholangitis or hospitalized patients with a history of gallbladder stones with pain, bile duct dilatation and jaundice;
- Have drug or alcohol abuse or mental disorder in the last 5 years;
- Pregnant or lactating women;
- Altered anatomy due to previous history of upper gastrointestinal surgery;
- Patients with advanced tumors resulting in abnormal upper gastrointestinal anatomy;
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 intelligence assistance systemA deep learning-based bile duct scanning system that can prompt endoscopists to scan standard stations, identify bile ducts and stones in real time | Device | experts without AI-assisted system, Then novices with AI-assisted system, novices with AI-assisted system, Then experts without AI-assisted system |
Primary outcome
Accuracy of diagnosis of common bile duct stones in patients with low and intermediate risk by novice combined with AI-assisted and expert
Time frame: the time novice finished operation and expert finished operation
8 secondary outcomes
Sensitivity, specificity, NPV, and PPV for the diagnosis of common bile duct stones in low and intermediate risk patients
Time frame: the time novice finished operation and expert finished operation
Detection rate of gallstone lesions
Time frame: the time novice finished operation and expert finished operation
Missed detection rate of gallstone lesions
Time frame: the time novice finished operation and expert finished operation
Detection rate of bile duct lesions(all bile duct lesions including gallstones)
Time frame: the time novice finished operation and expert finished operation
Missed rate of bile duct lesion(all bile duct lesions including gallstones)
Time frame: the time novice finished operation and expert finished operation
Number of bile duct standard station scans
Time frame: the time novice finished operation and expert finished operation
scan time
Time frame: the time novice finished operation and expert finished operation
Incidence of Adverse Events
Time frame: the time novice finished operation and expert finished operation
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 19 May 2022.
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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.
Yu Honggang, Doctor principal investigator
Renmin Hospital of Wuhan University
Questions about this trial
What is this clinical trial studying?
A study testing artificial intelligence assistance system in people with Common Bile Duct Stones, run by Renmin Hospital of Wuhan 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 3 inclusion and 6 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 19 May 2022. 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 Yu Honggang, Doctor (13871281899, yuhonggang@whu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Accuracy of diagnosis of common bile duct stones in patients with low and intermediate risk by novice combined with AI-assisted and expert (time frame: the time novice finished operation and expert finished operation). 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 19 May 2022.