Validation of an AI-based Biliopancreatic EUS Navigation System for Real-time Quality Improvement: A Prospective, Single-center, Randomized Controlled Trial

NCT ID: NCT05457101

Last Updated: 2023-06-22

Basic Information

• Recruitment Status: UNKNOWN
• Clinical Phase: NA
• Total Enrollment: 264 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2022-07-01
• Study Completion Date: 2023-07-30

Brief Summary

Endoscopic ultrasonography (EUS) is a key procedure for diagnosing biliopancreatic diseases. However, the performance among EUS endoscopists varies greatly and leads to blind areas during operation, which impaired the health outcome of patients. We previously developed an artificial intelligence (AI) device that accurately identifies EUS standard stations and significantly reduces the difficulty of ultrasound image interpretation. In this study, we updated the device (named EUS-IREAD) and assessed its performance in improving the quality of EUS examination in a single-center randomized controlled trial.

Detailed Description

In recent years, endoscopic ultrasonography (EUS) has developed into a preferred imaging modality for the diagnosis of biliopancreatic diseases, especially small (< 3 cm) pancreatic tumors and small (< 4 mm) bile duct stones. Therefore, EUS is often chosen as the main tool for screening early biliopancreatic diseases among high-risk individuals. However, a plenty of studies have shown that the detection rate of biliopancreatic diseases under EUS varies from 70% to 93% among different endoscopists due to examination quality and operators differences, which suggest that there are missed diagnosis of lesions. The missed diagnosis of pancreatic cancer makes patients lose the opportunity of radical surgery, and the five-year survival rate is reduced to 7.2%; and the missed diagnosis of choledocholithiasis causes severe acute diseases such asacute cholangitis and acute pancreatitis; it has serious consequences on the prognosis and quality of life of patients. Therefore it is important to reduce the missed diagnosis of lesions while further expanding the application of EUS.

Ensuring the examination quality is a seminal prerequisite for discovering biliopancreatic lesions in EUS. There are two main reasons affecting the quality of biliopancreatic EUS examination: First, non-standard operation by endoscopists; excellent biliopancreatic EUS examinations require the continuity and integrity of the scan. According to the experience of the Japanese Society of Gastrointestinal Endoscopy and European and American experts, multi-station approach in biliopancreatic EUS has been established as the standard scanning procedure. And these standard stations include anatomical landmarks that can be used to locate the transducer and identify areas that are not scanned. The American Society for Gastrointestinal Endoscopy (ASGE) and the American Association for Gastrointestinal Endoscopy (ACG) Endoscopic Quality Working Group have also issued quality indicators that should be completed for EUS examination. But they are often not well followed because of a lack of supervision and availability of practical tools, and there are a large number of blind areas in current daily EUS scans. Secondly, it is difficult in understanding US images with gray and white texture. Even experienced endoscopists have some challenges in identifying anatomical structures in EUS images. Therefore, it is critical to develop a practical tool that can monitor the blind area of EUS examination in real time, reduce the difficulty of ultrasonographic interpretation, and standardize the quality of EUS examination.

Deep learning has been successfully applied to many areas of medicine. In the field of endoscopic ultrasonography, most researches are dedicated to the use of computer tools to assist in the diagnosis of lesions in static images, while rare work studied the role of deep learning in monitoring the blind area of EUS examinations and exploring assistance on real-time ultrasonographic interpretation. Previously, we have successfully developed and validated an EUS navigation system that can identify the standard stations of pancreas and bile duct EUS in real time. Although encouraging preliminary results have been published regarding the use of artificial intelligence in reducing the difficulty of EUS images, this system has not been validated in a real-world clinical setting, and it is unclear whether it can be successfully applied in clinical practice and improve the quality of EUS examination.

Therefore, in this study, we updated the EUS-intelligent and real-time endoscopy analytical device (named EUS-IREAD) based on the aforementioned biliopancreatic EUS station recognition models and further trained an anatomical landmark identification function to better locate the transducer position and diagnose biliopancreatic lesions. We then conducted a single-center randomized controlled trial to assess its adjunctive performance to EUS endoscopists in a clinical setting.

Conditions

Endoscopic Ultrasonography; Pancreatic Disease; Bile Duct Diseases; Artificial Intelligence

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: SCREENING
• Blinding Strategy: DOUBLE

Study Groups

with AI-based biliopancreatic EUS navigation system

The endoscopists in the experimental group will be assisted by EndoAngel, which can in real-time prompt standard stations and anatomical structures during EUS.

Group Type: EXPERIMENTAL

AI-based biliopancreatic EUS navigation system

Intervention Type: OTHER

The endoscopists in the experimental group will be assisted by EndoAngel, which can in real-time prompt standard stations and anatomical structures during EUS. The system is an non-invasive AI system .

without AI-based biliopancreatic EUS navigation system

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

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

AI-based biliopancreatic EUS navigation system

The endoscopists in the experimental group will be assisted by EndoAngel, which can in real-time prompt standard stations and anatomical structures during EUS. The system is an non-invasive AI system .

Intervention Type: OTHER

Eligibility Criteria

Inclusion Criteria

1. Male or female aged 18 or above;

2. Patients able to give informed consent were eligible to participate.

3. Able and willing to comply with all study process.

4. history of previous biliopancreatic disease

5. Biliopancreatic lesions suspected due to clinical symptoms and/or radiological findings and/or laboratory findings

6. Patients at high risk of pancreatic cancer : Known genetic mutations associated with pancreatic cancer risk (BRCA2, BRCA1, PALB2, ATM, CDKNA/p16); Familial pancreatic ductal adenocarcinoma without known germline mutation; Peutz-Jeghers syndrome (STK11); Lynch syndrome (MLH1/MSH2/MSH6, EPCAM, PMS2); Familial adenomatous polyposis (APC). etc.

Exclusion Criteria

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. patients with absolute contraindications to EUS examination;

4. Drug or alcohol abuse or psychological disorder in the last 5 years.

5. Patients in pregnancy or lactation.

6. bleeding diathesis or thrombocytopenia

7. history of previous digestive surgery.

8. severe medical illness

9. upper GI tract obstruction

10. previous medical history of allergic reaction to anesthetics

11. anatomical abnormalities of the upper gastrointestinal tract due to advanced neoplasia

12. 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

Sponsors

Renmin Hospital of Wuhan University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Honggang Yu, Doctor

Role: PRINCIPAL_INVESTIGATOR

Renmin Hospital of Wuhan University

Locations

Renmin Hospital of Wuhan University

Wuhan, Hubei, China

Site Status: RECRUITING

Countries

China

Central Contacts

Honggang Yu, Doctor

Role: CONTACT

whdxrmyy@126.com

+862788041911

Facility Contacts

Yu Honggang, Doctor

Role: primary

yuhonggang@whu.edu.cn

13871281899

References

Wu HL, Yao LW, Shi HY, Wu LL, Li X, Zhang CX, Chen BR, Zhang J, Tan W, Cui N, Zhou W, Zhang JX, Xiao B, Gong RR, Ding Z, Yu HG. Validation of a real-time biliopancreatic endoscopic ultrasonography analytical device in China: a prospective, single-centre, randomised, controlled trial. Lancet Digit Health. 2023 Nov;5(11):e812-e820. doi: 10.1016/S2589-7500(23)00160-7. Epub 2023 Sep 27.

Reference Type: DERIVED

PMID: 37775472 (View on PubMed)

Other Identifiers

EA-19-003-26

Identifier Type: -

Identifier Source: org_study_id