Clinical Trials Logo

Clinical Trial 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.


Clinical Trial 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. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT05457101
Study type Interventional
Source Renmin Hospital of Wuhan University
Contact Honggang Yu, Doctor
Phone +862788041911
Email whdxrmyy@126.com
Status Recruiting
Phase N/A
Start date July 1, 2022
Completion date July 30, 2023

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 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
Recruiting NCT06059378 - Real-life Implementation of an AI-based Optical Diagnosis N/A
Completed NCT05517889 - Repeatability and Stability of Healthy Skin Features on OCT
Completed NCT04816981 - AI-EBUS-Elastography for LN Staging N/A
Completed NCT05006092 - Surveillance Modified by Artificial Intelligence in Endoscopy (SMARTIE) 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