Description Module
The Description Module contains narrative descriptions of the clinical trial, including a brief summary and detailed description. These descriptions provide important information about the study's purpose, methodology, and key details in language accessible to both researchers and the general public.
Description Module path is as follows:
Study -> Protocol Section -> Description Module
Description Module
Ignite Creation Date:
2025-12-25 @ 5:08 AM
Ignite Modification Date:
2025-12-25 @ 5:08 AM
NCT ID:
NCT05858827
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.
Study:
NCT05858827
Study Brief:
Protocol Section:
NCT05858827