In this study, the investigators proposed an artificial intelligence-based biliary stricture navigation system in MRCP-based ERCP, which can instruct the direction of guide wire and the position of stent placement in real time.
585/5000 Biliary stricture can be divided into benign biliary stricture and malignant biliary stricture, and malignant hilar biliary obstruction is the one of the common cause. Since there is no specific early screening method for malignant hilar biliary obstruction at present and most patients have no obvious clinical symptoms in the early stage, most patients are already in the advanced stage when they are first diagnosed. Advanced malignant hilar biliary obstruction cannot undergo resection surgery, whose first choice for the treatment is palliative endoscopic biliary drainage.Biliary drainage can relieve jaundice, pruritus and other symptoms due to cholestasis. However,before the narrow segment was placed the stent, the contrast agent could not pass through the narrow segment and the bile duct above the narrow segment could not be seen.So it was difficult for doctors to determine the direction of the guide wire and the position of the stent. In addition, indiscriminate application of the contrast agent may cause outflow obstruction leading to infection. However, there is no relevant research to solve these problems. MRCP is the preferred examination method of pancreatic and bile duct diseases. Therefore, MRCP should be routinely performed before patients are treated with ERCP. At present, MRCP is in supine position, and ERCP is in prone position. Different positions lead to differences in the morphology of MRCP and the bile duct on ERCP.So preoperative MRCP in supine position has limited role in advising physicians on the morphology of the bile duct. Therefore, MRCP in the prone position is more favorable for endoscopists to perform ERCP . In recent years, deep learning algorithms have been continuously developed and increasingly mature.They have been gradually applied to the medical field. Computer vision is a science that studies how to make machines "see". Through deep learning, camera and computer can replace human eyes to carry out machine vision such as target recognition, tracking and measurement.Interdisciplinary cooperation in the field of medical imaging and computer vision is also one of the research hotspots in recent years. At present, it is mainly applied to the automatic identification and detection of lesions and quality control, and has achieved good results. It can assist doctors to find lesions, make disease diagnosis and standardize doctors' operations, so as to improve the quality of doctors' operations.With mature technical support, it has a good prospect and application value to develop endoscopic operating system for lesion detection and quality control based on artificial intelligence methods such as deep learning. In this study, the investigators proposed an artificial Intelligence-based Biliary Stricture Navigation System in MRCP-based ERCP, which can instruct the direction of guide wire and the position of stent placement in real time.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
DOUBLE
Enrollment
62
The endoscopists in the experimental group will be assisted by AI system, which can instruct the direction of guide wire and the position of stent placement in real time. The system is an non-invasive AI system .
Renmin hospital of Wuhan University
Wuhan, Hubei, China
RECRUITINGProcedure time
The time of performing ERCP
Time frame: During procedure
Intersection over Union of bile duct segmentation
Intersection over Union of bile ducts predicted by artificial intelligence devices and actual bile ducts
Time frame: A month
Intersection over union of bile duct matching model:
Intersection over Union of the bile ducts generated by the AI device and the actual bile ducts in ERCP
Time frame: 6 month
Success rate of stent placement
The number of successful patients is the numerator, and the total number of patients with stent placement is the denominator.
Time frame: During procedure
Rate of adverse events
The number of patients who experienced adverse events was numerator, and the total number of patients undergoing stent placement was denominator.
Time frame: Until discharge assessed up to 14 days
Fluoroscopy time
The sum of the total X ray fluoroscopy time during the whole procedure.
Time frame: During procedure
Total amount of contrast medium
Total amount of contrast medium during the whole procedure.
Time frame: During procedure
The difference of the area of bile duct visualization in different position
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The area of bile duct visualization of MRCP in different position
Time frame: During procedure
The difference in the time required to perform MRCP in different position
The difference in the time required to perform MRCP in different position
Time frame: During procedure