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

Wuhan University

Status: Unknown

Conditions: Common Bile Duct Stones

Treatments: Device: artificial intelligence assistance system

Study type: Interventional

Funder types: Other

Identifiers

NCT05381064

EA-22-004

Details and patient eligibility

About

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

Full 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

Enrollment: 184 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

1. 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;
2. Able to read, understand and sign an informed consent;
3. 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.

Exclusion criteria

1. 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;
2. Have drug or alcohol abuse or mental disorder in the last 5 years;
3. Pregnant or lactating women;
4. Altered anatomy due to previous history of upper gastrointestinal surgery;
5. Patients with advanced tumors resulting in abnormal upper gastrointestinal anatomy;
6. High-risk diseases or other special conditions that the investigator considers the subject unsuitable for participation in the clinical trial.

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Crossover Assignment

Masking: Double Blind

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184 participants in 2 patient groups

novices with AI-assisted system, Then experts without AI-assisted system

Experimental group

Description:

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.

Treatment:

Device: artificial intelligence assistance system

experts without AI-assisted system, Then novices with AI-assisted system

Experimental group

Description:

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.

Treatment:

Device: artificial intelligence assistance system