Clinical trial NCT04719117: Retrograde Cholangiopancreatography AI Assisted System Validation on Effectiveness and Safety
Official title: A Multicentric Validation Study on the Effectiveness and Safety of Artificial Intelligence Assisted System in Clinical Application of Retrograde Cholangiopancreatography
Study type: OBSERVATIONAL
Overall status: UNKNOWN
Conditions: Gastrointestinal Disease
Endoscopy
Artificial Intelligence
Cholangiopancreatography, Endoscopic Retrograde
Keywords: Deep Learning
Lead sponsor: Renmin Hospital of Wuhan University
Sponsor class: OTHER
First posted: 2021-01-22
Enrollment: 150
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Patients who meet the admission criteria for endoscopic examination.

Brief 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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

* Who needs ERCP and its related tests are needed to further define the characteristics of digestive tract diseases
* Able to read, understand and sign informed consent
* The investigator believes that the subject can understand the process of the clinical study, is willing and able to complete all the study procedures and follow-up visits, and cooperate with the study procedures
* Patients with a natural duodenal papilla

Exclusion Criteria:

* Has participated in other clinical trials, signed informed consent and is in the follow-up period of other clinical trials
* Has drug or alcohol abuse or mental disorder in the last 5 years
* Women who are pregnant or lactating
* Subjects with previous biliary sphincterotomy
* The investigator determined that subjects were not suitable for ERCP and related tests
* A high-risk disease or other special condition that the investigator considers inappropriate for the subject to participate in a clinical trial
* Patients with known more severe pancreatic head carcinoma
* Patients with acute pancreatitis within 3 days
* Biliary stent replacement or removal did not occur after pancreatic angiography as expected
* Acute cardiovascular and cerebrovascular diseases

Primary outcomes:
measure: Number of stone removal operations
timeFrame: A year
Secondary outcomes:
measure: the accuracy of the measurement
timeFrame: A year
measure: Stone clearance success rate
timeFrame: A year
measure: the sensitivity of the prediction of the stone
timeFrame: A year
measure: the operate time
timeFrame: During surgery
measure: the removal stone time
timeFrame: During surgery
Other outcomes:
Locations:
city: Wuhan
country: China
facility: Renmin hospital
state: Hubei
city: Shanghai
country: China
facility: People's Hospital
state: Shanghai
city: Shanghai
country: China
facility: Changhai Hospital
state: Shanghai
Study officials:
affiliation: Wuhan University Renmin Hospital
name: Honggang Yu, Doctor
role: PRINCIPAL_INVESTIGATOR
References:
citation: Huang L, Xu Y, Chen J, Liu F, Wu D, Zhou W, Wu L, Pang T, Huang X, Zhang K, Yu H. An artificial intelligence difficulty scoring system for stone removal during ERCP: a prospective validation. Endoscopy. 2023 Jan;55(1):4-11. doi: 10.1055/a-1850-6717. Epub 2022 May 12.