A Multicentric Validation Study on the Effectiveness and Safety of Artificial Intelligence Assisted System in Clinical Application of Retrograde Cholangiopancreatography
A Multicentric Validation Study on the Effectiveness and Safety of Artificial Intelligence Assisted System in Clinical Application of Retrograde Cholangiopancreatography
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.
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.
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