To Evaluate the Capability of an EUS Automatic Image Reporting System

Study Overview
=================
Brief 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.

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.

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

Conditions
-----------------
Endoscopic Ultrasonography, Artificial Intelligence

Participation Criteria
=================
Eligibility Criteria
-----------------
Inclusion Criteria: patients aged 18 years or older; patients with indications for endoscopic ultrasonography of the biliary pancreatic system and undergoing sedated EUS procedures; ability to read, understand, and sign informed consent; Exclusion Criteria: patients with absolute contraindications to EUS examination; history of previous gastric surgery; pregnancy; severe medical illness; previous medical history of allergic reaction to anesthetics; stricture or obstruction of the esophagus; anatomical abnormalities of the upper gastrointestinal tract due to advanced neoplasia.

Ages Eligible for Study
-----------------
Minimum Age: 18 Years

Sexes Eligible for Study
-----------------
All

Accepts Healthy Volunteers
-----------------
No

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 correct standard stations 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 |
| --- | --- | --- |
| accuracy of capturing standard stations | The number of correct standard stations captured by EUS-AIRS is divided by the number of all standard stations captured | 2 months | 
| completeness of capturing 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 | 
| 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 | 
| proportion of invalid images | The number of invalid images captured by EUS-AIRS divided by the number of all images captured by EUS-AIRS | 2 months | 
| proportion of duplicate images | The number of duplicate images captured by EUS-AIRS divided by the number of all images captured by EUS-AIRS | 2 months | 
| intersection over union of lesion segmentation | the relative area of overlap between the predicted bounding box and the ground-truth bounding box | 2 months | 

 Terms related to the study
=================
Keywords Provided by Centre Hospitalier Valida
-----------------
Endoscopic Ultrasonography, Artificial Intelligence