Clinical trial NCT05858827: To Evaluate the Capability of an EUS Automatic Image Reporting System
Official title: To Evaluate the Capability of an Endoscopic Ultrasonography Automatic Image Reporting System
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Endoscopic Ultrasonography
Artificial Intelligence
Keywords: Endoscopic Ultrasonography
Artificial Intelligence
Lead sponsor: Renmin Hospital of Wuhan University
Sponsor class: OTHER
First posted: 2023-05-15
Enrollment: 114
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: OTHER
Time perspective: PROSPECTIVE
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.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: completeness of capturing standard stations
timeFrame: 2 months
Secondary outcomes:
measure: accuracy of capturing standard stations
timeFrame: 2 months
measure: completeness of capturing detected lesions
timeFrame: 2 months
measure: completeness of capturing biopsy procedures
timeFrame: 2 months
Other outcomes:
Locations:
city: Wuhan
country: China
facility: Renmin Hospital of Wuhan University
state: Hubei
Study officials:
affiliation: Renmin Hospital of Wuhan University
name: Honggang Yu, Doctor
role: PRINCIPAL_INVESTIGATOR
References: