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NCT07543263
An Artificial Intelligence System for Multimodal, Multi-class Diagnosis of Pancreatic Cystic Lesions Based on Endoscopic Ultrasonography
Conditions: Pancreatic Cystic Lesion (PCL)
Sex: All
Ages: 18 Years – N/A
Healthy volunteers: No
Enrollment: 176
Sponsor: Qilu Hospital of Shandong University
Location: Qilu Hospital of Shandong University Jinan Shandong
Summary
The aim of this study is to develop and validate an artificial intelligence system named iEUS-PCL (intelligent endoscopic ultrasound system-pancreatic cystic lesions） for detecting and multimodal, multi-class diagnosing pancreatic cystic lesions (PCL) during endoscopic ultrasound (EUS) examination.
Eligibility Criteria
Inclusion Criteria:
1. Patients aged ≥18 years scheduled for EUS with suspected pancreatic cystic lesions based on clinical symptoms, medical history, laboratory tests or radiological examinations, and who agree to participate in the research and voluntarily sign the informed consent.
2. Patients with no prior history of treatment for pancreatic lesions.
Exclusion Criteria:
1. Patients with absolute contraindications to EUS examination.
2. Pregnancy or lactating.
3. Uncorrectable coagulopathy (PTT\>50 seconds or INR\>1.5) and/or uncorrectable thrombocytopenia(platelet count\
Source: ClinicalTrials.gov (NCT07543263). StuddyBuddy aggregates publicly available trial information.
