Development and Validation of an Artificial Intelligence-based Biliary Stricture Navigation System in MRCP-based ERCP

June 1, 2021 updated by: Renmin Hospital of Wuhan University

A Single-center Study on the Effectiveness and Safety of Artificial Intelligence Assisted System in Clinical Application of Endoscopic Retrograde Cholangiopancreatography

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 Overview

Detailed Description

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

Enrollment (Anticipated)

62

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Contact Backup

  • Name: Honggang Yu Yu, Doctor
  • Phone Number: +862788041911
  • Email: whdxrmyy@126.com

Study Locations

    • Hubei
      • Wuhan, Hubei, China, 430000
        • Recruiting
        • Renmin Hospital of Wuhan University
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Description

Inclusion Criteria:

  1. Bile duct segmentation model 1) Male or female aged 18 or above; 2) Who needs ERCP,MRCP and its related tests are needed to further define the characteristics of digestive tract diseases; 3)The images of MRCP and ERCP are clear; 4) Able to read, understand and sign informed consent; 5) The investigator believes that the subject can understand the process of the clinical study and is willing and able to complete all the study procedures and follow-up visits and cooperate with the study procedures.
  2. Bile duct matching model

In addition to the criteria mentioned in the bile duct segmentation model, the bile duct matching model should also meet the following criteria:

  1. Able to complete MRCP in prone position;
  2. Bile ducts are almost completely visible in MRCP and ERCP.

(3) Clinical trials

In addition to the criteria mentioned in the bile duct segmentation model, the clinical trials should also meet the following criteria:

  1. Able to complete MRCP in prone position;
  2. Patients requiring biliary drainage by ERCP due to malignant hilar biliary obstruction.

Exclusion Criteria:

  1. Bile duct segmentation model and bile duct matching model 1)Has participated in other clinical trials, signed the informed consent and was in the follow-up period of other clinical trials; 2) Drug or alcohol abuse or psychological disorder in the last 5 years; 3) Patients in pregnancy or lactation; 4) The investigator considers that the subjects were not suitable for MRCP, ERCP and related tests; 5)A high-risk diseases or other special conditions that the investigator considers inappropriate for the subject to participate in a clinical trial;
  2. Clinical trials

In addition to the criteria mentioned in the above, the clinical trial must not meet any of the following criteria:

  1. Previous gastrectomy;
  2. Stent replacement;
  3. Pyloric or duodenal obstruction.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Treatment
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Double

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Experimental: with AI navigation system
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.All patients underwent MRCP in the prone position prior to ERCP. A round box with a diameter of 2mm filled with water was pasted next to the patient's spine at the level of angulus inferior scapulae during MRCP, and a sheet metal with a diameter of 2mm was pasted at the same area during ERCP.
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 .
No Intervention: without AI navigation system
The endoscopists in the contrpl group performs ERCP routinely without special prompts.All patients underwent MRCP in the prone position prior to ERCP. A round box with a diameter of 2mm filled with water was pasted next to the patient's spine at the level of angulus inferior scapulae during MRCP, and a sheet metal with a diameter of 2mm was pasted at the same area during ERCP.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Procedure time
Time Frame: During procedure
The time of performing ERCP
During procedure

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Intersection over Union of bile duct segmentation
Time Frame: A month
Intersection over Union of bile ducts predicted by artificial intelligence devices and actual bile ducts
A month
Intersection over union of bile duct matching model:
Time Frame: 6 month
Intersection over Union of the bile ducts generated by the AI device and the actual bile ducts in ERCP
6 month
Success rate of stent placement
Time Frame: During procedure
The number of successful patients is the numerator, and the total number of patients with stent placement is the denominator.
During procedure
Rate of adverse events
Time Frame: Until discharge assessed up to 14 days
The number of patients who experienced adverse events was numerator, and the total number of patients undergoing stent placement was denominator.
Until discharge assessed up to 14 days
Fluoroscopy time
Time Frame: During procedure
The sum of the total X ray fluoroscopy time during the whole procedure.
During procedure
Total amount of contrast medium
Time Frame: During procedure
Total amount of contrast medium during the whole procedure.
During procedure
The difference of the area of bile duct visualization in different position
Time Frame: During procedure
The area of bile duct visualization of MRCP in different position
During procedure
The difference in the time required to perform MRCP in different position
Time Frame: During procedure
The difference in the time required to perform MRCP in different position
During procedure

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Investigators

  • Principal Investigator: Honggang Yu Yu, Doctor, Renmin Hospital of Wuhan University

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 27, 2021

Primary Completion (Anticipated)

June 1, 2022

Study Completion (Anticipated)

July 1, 2022

Study Registration Dates

First Submitted

May 17, 2021

First Submitted That Met QC Criteria

May 24, 2021

First Posted (Actual)

May 26, 2021

Study Record Updates

Last Update Posted (Actual)

June 4, 2021

Last Update Submitted That Met QC Criteria

June 1, 2021

Last Verified

May 1, 2021

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • EA-19-003-22

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

Clinical Trials on Artificial Intelligence

Clinical Trials on Artificial intelligence assistant system

3
Subscribe