Clinical Trial Details
— Status: Enrolling by invitation
Administrative data
| NCT number |
NCT04719117 |
| Other study ID # |
EA-19-006-08 |
| Secondary ID |
|
| Status |
Enrolling by invitation |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
September 1, 2020 |
| Est. completion date |
December 31, 2021 |
Study information
| Verified date |
January 2021 |
| Source |
Renmin Hospital of Wuhan University |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
In this study, the investigators proposed a prospective study about the effectiveness of
artificial intelligence system for Retrograde cholangiopancreatography. The subjects would be
include in an analyses groups. The AI-assisted system helps endoscopic physicians estimate
the difficulty of Endoscopic retrograde cholangiopancreatography for choledocholithiasis and
make recommendations based on guidelines and difficulty scores. The investigators used the
stone removal times, success rate of stone extraction and Operating time to reflect the
difficulty of the operation, and evaluated whether the results of the AI system were correct.
Description:
Endoscopy is a routine and reliable method for the diagnosis of digestive tract
diseases.Common endoscopy are gastroscopy, colonoscopy, capsule endoscopy and enteroscopy,
ultrasonic gastroscopy, after ercp and other related technology, can be used in early gastric
cancer and peptic ulcer, esophageal varices, the stomach before lesion, intestinal polyps and
adenomas and colorectal lesions, inflammatory bowel disease, pancreas disease, biliary tract
disease diagnosis and follow-up.At present, digestive endoscopy almost covers the diagnosis
of the vast majority of diseases of the digestive tract, and diseases of the digestive system
that cannot be directly seen by endoscopy can also be realized through endoscopic-based
technologies such as endoscopy and ERCP (here the investigators collectively refer to
endoscopy), so as to achieve the coverage of the whole digestive system.It can be seen that
digestive endoscopy is of great significance for the diagnosis of digestive diseases and the
development of digestive field.
With the popularization of these related technologies, the number of endoscopy increased
rapidly, which further increased the workload of endoscopists. The operation of endoscopy by
high-load endoscopists would reduce the quality of endoscopy, which is prone to problems such
as incomplete examination coverage and incomplete detection of lesions.In digestive
endoscopy, there are some problems in China, such as lack of endoscopic physicians and uneven
distribution, and the quality of endoscopy is not up to standard. These problems need to be
solved urgently in order to relieve the pain of patients, save medical resources, save the
time and money of patients, and ensure the quality of patients' medical treatment.
In 2015, the proposal of deep learning brought great changes to the field of artificial
intelligence, which made the development of artificial intelligence leap to a new
level.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 a prospective study about the effectiveness of
artificial intelligence system for Retrograde cholangiopancreatography. The subjects would be
include in an analyses groups. The AI-assisted system helps endoscopic physicians estimate
the difficulty of Endoscopic retrograde cholangiopancreatography for choledocholithiasis and
make recommendations based on guidelines and difficulty scores. The investigators used the
stone removal times, success rate of stone extraction and Operating time to reflect the
difficulty of the operation, and evaluated whether the results of the AI system were correct.
