A Multi-center Study on the Efficacy and Safety of AI-assisted Navigation System for Biliopancreatic EUS

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

UNKNOWN

Clinical Phase

NA

Total Enrollment

285 participants

Study Classification

INTERVENTIONAL

Study Start Date

2021-05-12

Study Completion Date

2022-11-30

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

Pancreatic cancer is one of the most lethal cancers. Survival rates vary largely depending on the stage at which it is diagnosed. EUS is considered one of the most sensitive modalities for pancreatic cancer detection. To avoid a missed diagnosis of the pancreatic cancer, the continuity and integrity of EUS needs to be ensured as much as possible. The station approach in pancreatic EUS has been established as the standard scanning procedure. Complete anatomical scanning is helpful for the identification of standard stations, and its imaging findings can assist in the diagnosis of pancreatic lesions and guide patient treatment and prognosis. But EUS is highly operator-dependent and the learning curve is steep. In this study, we constructed a deep learning-based pancreatic scanning navigation system in EUS, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. In order to improve the quality of EUS and reduce the missed diagnosis of pancreatic lesions.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Clinical Research on a Novel Deep-learning Based System in Pancreatic Endoscopic Ultrasound Scanning

NCT05792267

Pancreatic Disease

RECRUITING NA

Artificial Intelligence in EUS for Diagnosing Pancreatic Solid Lesions

NCT05476978

Pancreatic Ductal Adenocarcinoma Pancreatitis, Chronic Pancreatic Neuroendocrine Tumor +1 more

COMPLETED

Validation of an AI-Assisted Pancreatic EUS System for Training Improvement: a Prospective, Multi-Center, Randomized Trial

NCT06790134

Pancreatic Diseases

RECRUITING NA

A Cohort Study on Screening and Follow-up of High-risk Population of PDAC Based on EUS

NCT05621824

Carcinoma, Pancreatic Ductal

NOT_YET_RECRUITING

An Ultrasound Imaging Technique to Measure Tumors in People With Pancreatic Ductal Adenocarcinoma (PDAC) or Gastrointestinal (GI) Adenocarcinoma

NCT04837833

Pancreas Cancer Gastrointestinal Adenocarcinoma

ACTIVE_NOT_RECRUITING NA

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Pancreatic cancer is a malignant tumor of the digestive system with insidious onset, rapid progression and very poor prognosis. According to the latest cancer data in China in 2020 released by the International Agency for Research on Cancer (IARC) of the World Health Organization, there are about 120,000 new cases of pancreatic cancer in China, with a mortality rate close to 100%, which seriously endangers the national health. Early diagnosis of pancreatic cancer can be achieved by surgical resection with a 5-year survival rate of 58%, Once advanced pancreatic cancer develops, patient survival is 7.2%. As a rapidly developing deadly cancer, missed diagnosis of pancreatic cancer may have extremely serious consequences for patients.How to improve the diagnostic rate of early pancreatic cancer is an urgent problem to be solved.

EUS(endoscopic ultrasonography) is considered one of the most sensitive modalities for pancreatic cancer detection. It has a much higher diagnostic accuracy than MRI and CT for the diagnosis of pancreatic cancer, especially early pancreatic cancer \< 1 cm in diameter (EUS-FNA 95.6% vs CT 77.4%, MRI 76.2%). EUS is the modality of choice for the early diagnosis of pancreatic tumors. To avoid a missed diagnosis of the pancreatic cancer, the continuity and integrity of EUS needs to be ensured as much as possible. But EUS is highly operator-dependent and the learning curve is steep, and the quality of the examination is highly dependent on the operator's technique. Therefore, it is necessary to develop a system that can effectively assist the full scanning of EUS.

The station approach in pancreatic EUS has been established as the standard scanning procedure. The principle of completing the station approach is to find the anatomical landmarks of this station, Such as organs (kidney, spleen), blood vessels (such as splenic artery, splenic vein, portal vein), ducts (pancreatic duct, bile duct), etc.The scanning of these anatomical landmarks is the basis for an accurate assessment of the entire pancreas。 At the same time, the type of pancreatic lesions and the development of the course have abnormal imaging findings of different anatomical structure. For example, ultrasound images of pancreatic cancer will show vascular invasion, deformation of the biliopancreatic duct, and metastasis of adjacent organs. The guidelines clearly require that the choice of surgical approach for pancreatic cancer needs to be based on the degree of invasion of the cancer to adjacent important anatomical structures, to maximize the volume sparing of functional pancreatic parenchyma. Complete anatomical scanning can assist in the diagnosis of pancreatic lesions and guide patient treatment and prognosis.

In recent years, artificial intelligence (AI) has been successfully applied in multiple medical fields. At present, there have been studies of AI-based endoscopic ultrasonography for the identification of pancreatic lesions, However, there are no studies of AI-based navigation system for pancreatic endoscopic ultrasonography. Previously, we have successfully developed a standard station scanning navigation system for the pancreas and bile ducts. This system can improve the recognition accuracy of endoscopists for standard stations and enhance the cognitive ability of endoscopic ultrasonography images.

Based on the previous, we constructed a deep learning-based pancreatic scanning navigation system in EUS, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. and verify its auxiliary performance for endoscopists in clinical practice. In order to improve the quality of EUS and reduce the missed diagnosis of pancreatic lesions.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Pancreatic Cancer

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

SCREENING

Blinding Strategy

DOUBLE

Participants Outcome Assessors

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

EUS AI navigation system augmentation

The endoscopists in the experimental group will be assisted by EndoAngel, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. The system is an non-invasive AI system .

Group Type EXPERIMENTAL

Artificial intelligence assistant system

Intervention Type OTHER

The endoscopists in the experimental group will be assisted by EndoAngel, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. The system is an non-invasive AI system .

without EUS AI navigation system augmentation

The endoscopists in the contrpl group performs the examination routinely without special prompts.

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Artificial intelligence assistant system

The endoscopists in the experimental group will be assisted by EndoAngel, which can assist in identifying important anatomical structures adjacent to the pancreas in real time. The system is an non-invasive AI system .

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* All patients meeting the following criteria will be considered for participation in the study:

1. Male or female aged 18 or above;
2. EUS is needed to further clarify the characteristics of biliopancreatic diseases;
3. Patients able to give informed consent were eligible to participate.
4. Able and willing to comply with all study process.

Exclusion Criteria

* All patients meeting the following criteria will not be considered for participation in the study:

1. Has participated in other clinical trials, signed informed consent and was in the follow-up period of other clinical trials.
2. Has participated in clinical trials of the drug and is in the elution period of the experimental drug or control drug.
3. Drug or alcohol abuse or psychological disorder in the last 5 years.
4. Patients in pregnancy or lactation.
5. A history of Upper Gastrointestinal surgery.
6. Patients with anatomical abnormalities of the upper gastrointestinal tract due to advanced neoplasia
7. Patients in whom the presence of clearly defined vital anatomical structures cannot be observed
8. Researchers believe that the patient is not suitable to participate in the trial.

Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

Yes