To Evaluate the Capability of an EUS Automatic Image Reporting System

To Evaluate the Capability of an Endoscopic Ultrasonography Automatic Image Reporting System

In this study, the EUS intelligent picture reporting system can automatically generate reports after reading videos of EUS examinations. This function can standardize the quality of endoscopic ultrasound image reporting and reduce the work burden of ultrasound endoscopists.

Study Overview

• Status: Completed
• Conditions: Artificial Intelligence; Endoscopic Ultrasonography

Detailed Description

A well-written report is the most important way of communication between clinicians, referring doctors and patients. Reports play a key role for quality improvement in digestive endoscopy, too. Unlike digestive endoscopy, the quality of reporting in endoscopic ultrasound (EUS) has not been thoroughly evaluated and a reference standard is lacking. According to the guidance statements regarding standard EUS reporting elements developed and reviewed at the Forum for Canadian Endoscopic Ultrasound 2019 Annual Meeting, appropriate photo documentation of all relevant lesions and anatomical landmarks should be included in EUS reports and stored for future reference. Systematic photo documentation in EUS is an indicator of procedure quality according to the ASGE. Systematic photo documentation can facilitate surveillance EUS evaluations. According to an international online survey, most endosonographers used a structured tree in the report describing either normal and abnormal findings (81%) or only abnormal findings (7%). Therefore, it is necessary to develop a standardized endoscopic ultrasound image report system.

The past decades have witnessed the remarkable progress of artificial intelligence (AI) in the medical field. Deep learning, a subset of AI, has shown great potential in elaborating image analysis. In the field of digestive endoscopy, deep learning has been widely studied, including identifying focal lesions, differentiating malignant and non-malignant lesions, and so on. However, rare study works on automatic photo documentation during endoscopic ultrasound.

Our previous work has successfully developed a deep learning EUS navigation system that can identify the standard stations of the pancreas and CBD in real time. In the present study, we further constructed an EUS automatic image reporting system (EUS-AIRS). The EUS-AIRS can automatically capture images of standard stations, lesions, and biopsy procedures, and label Types of lesions, thereby generating an image report with high completeness and quality during endoscopic ultrasonography.

We tested the performance of the EUS-AIRS by testing its performance on retrospective internal and external data, and we anticipate determining the utility of the EUS-AIRS in clinical practice by testing its performance in consecutive prospective patients.

• Study Type: Observational
• Enrollment (Actual): 114

Contacts and Locations

Study Locations

• China
  • Hubei
    • Wuhan, Hubei, China, Wuhan
      • Renmin Hospital of Wuhan University

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

The study population was patients undergoing endoscopic ultrasonography who met all inclusion criteria and did not meet all exclusion criteria.

Description

Inclusion Criteria:

1. patients aged 18 years or older;
2. patients with indications for endoscopic ultrasonography of the biliary pancreatic system and undergoing sedated EUS procedures;
3. ability to read, understand, and sign informed consent;

Exclusion Criteria:

1. patients with absolute contraindications to EUS examination;
2. history of previous gastric surgery;
3. pregnancy;
4. severe medical illness;
5. previous medical history of allergic reaction to anesthetics;
6. stricture or obstruction of the esophagus;
7. anatomical abnormalities of the upper gastrointestinal tract due to advanced neoplasia.

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
completeness of capturing standard stations	The number of standard stations correctly captured by EUS-AIRS is divided by the number of all standard stations in the endoscopic ultrasound procedures	2 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
completeness of capturing biopsy procedures	The number of correct biopsy procedures captured by EUS-AIRS was divided by the number of all biopsy procedures in the endoscopic ultrasound procedure	2 months
accuracy of capturing standard stations	The number of standard station images correctly captured by EUS-AIRS is divided by the number of all standard station images captured by EUS-AIRS	2 months
completeness of capturing detected lesions	The number of correct lesions captured by EUS-AIRS was divided by the number of all lesions in the endoscopic ultrasound procedure	2 months

Collaborators and Investigators

• Sponsor: Renmin Hospital of Wuhan University
• Investigators: Principal Investigator: Honggang Yu, Doctor, Renmin Hospital of Wuhan University

Study record dates

Study Major Dates

• Study Start (Actual): May 10, 2023
• Primary Completion (Actual): October 23, 2023
• Study Completion (Actual): December 20, 2023

Study Registration Dates

• First Submitted: May 5, 2023
• First Submitted That Met QC Criteria: May 5, 2023
• First Posted (Actual): May 15, 2023

Study Record Updates

• Last Update Posted (Actual): January 12, 2024
• Last Update Submitted That Met QC Criteria: January 10, 2024
• Last Verified: January 1, 2024

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Endoscopic Ultrasonography
• Other Study ID Numbers: EA-23-004

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No