To Evaluate the Capability of an EUS Automatic Image Reporting System

Status Completed

Clinical Trial Summary

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.

Clinical Trial 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 Design

Related Conditions & MeSH terms

Clinical Trial Details

NCT number	NCT05858827
Study type	Observational
Source	Renmin Hospital of Wuhan University
Contact
Status	Completed
Phase
Start date	May 10, 2023
Completion date	December 20, 2023

View Details

See also
Status	Clinical Trial	Phase
Completed	`NCT04589078` - Polyp REcognition Assisted by a Device Interactive Characterization Tool - The PREDICT Study
Completed	`NCT03857438` - Correlation of Audiovisual Features With Clinical Variables and Neurocognitive Functions in Bipolar Disorder, Mania
Completed	`NCT04735055` - Artificial Intelligence Prediction for the Severity of Acute Pancreatitis
Not yet recruiting	`NCT05452993` - Screening for Diabetic Retinopathy in Pharmacies With Artificial Intelligence Enhanced Retinophotography	N/A
Not yet recruiting	`NCT04337229` - Evaluation of Comfort Behavior Levels of Newborns With Artificial Intelligence Techniques	N/A
Completed	`NCT05687318` - A Clinical Trial of the Effectiveness and Safety of Software Assisting Diagnose the Intestinal Polyp Digestive Endoscopy by Analysis of Colonoscopy Medical Images From Electronic Digestive Endoscopy Equipment	N/A
Recruiting	`NCT06051682` - Optimization of the Diagnosis of Bone Fractures in Patients Treated in the Emergency Department by Using Artificial Intelligence for Reading Radiological Images in Comparison With Traditional Reading by the Emergency Doctor.	N/A
Not yet recruiting	`NCT06039917` - Effect of the Automatic Surveillance System on Surveillance Rate of Patients With Gastric Premalignant Lesions	N/A
Not yet recruiting	`NCT06362629` - AI App for Management of Atopic Dermatitis	N/A
Recruiting	`NCT06059378` - Real-life Implementation of an AI-based Optical Diagnosis	N/A
Recruiting	`NCT06164002` - A I in the Prediction of Clinical Performance, Marginal Fit and Fracture Resistance of Vertical Versus Horizontal Margin Designs Fabricated With 2 Ceramic Materials	N/A
Completed	`NCT05517889` - Repeatability and Stability of Healthy Skin Features on OCT
Completed	`NCT05006092` - Surveillance Modified by Artificial Intelligence in Endoscopy (SMARTIE)	N/A
Completed	`NCT04816981` - AI-EBUS-Elastography for LN Staging	N/A
Recruiting	`NCT04535466` - Diagnosis Predictive Modle for Dense Density Breast Tissue Based on Radiomics
Enrolling by invitation	`NCT04719117` - Retrograde Cholangiopancreatography AI Assisted System Validation on Effectiveness and Safety
Completed	`NCT04399590` - Comparing the Number of False Activations Between Two Artificial Intelligence CADe Systems: the NOISE Study
Recruiting	`NCT04126265` - Artificial Intelligence-assisted Colonoscopy for Detection of Colon Polyps	N/A
Recruiting	`NCT06255808` - Development of Assist Tool for Breast Examination Using the Principle of Ultrasonic Sensor
Recruiting	`NCT04131530` - Automatic Evaluation of Inflammation Activity in Ulcerative Colitis Using pCLE With Artificial Intelligence