An Artificial Intelligence System for Multimodal, Multi-class Diagnosing Solid Pancreatic Lesions Based on Endoscopic Ultrasound
Qilu Hospital of Shandong University
383 participants
Sep 1, 2025
OBSERVATIONAL
Conditions
Summary
The aim of this study is to validate an artificial intelligence system named iEUS-SPL(intelligent endoscopic ultrasound system-solid pancreatic lesion) for detecting and multimodal, multi-class diagnosing solid pancreatic lesions during endoscopic ultrasound(EUS) examination.
Eligibility
Inclusion Criteria2
- Patients aged ≥18 years scheduled for EUS with suspected solid pancreatic lesions based on clinical symptoms, medical history, laboratory tests or radiological examinations agree to participate in the research and be able to sign informed consent.
- Patients with no prior history of treatment for pancreatic lesions.
Exclusion Criteria7
- Patients with absolute contraindications to EUS examination.
- Pregnancy or lactating.
- Uncorrectable coagulopathy(PTT>50 seconds or INR>1.5) and/or uncorrectable thrombocytopenia(platelet count<50×109/L).
- Upper gastrointestinal obstruction.
- Patients who underwent surgical treatment or anatomical alterations of the pancreas due to lesions in other thoracic and/or abdominal organs, as well as patients with congenital anatomical abnormalities.
- Patients who have undergone biliary/pancreatic duct stent placement.
- Patients who refuse to sign the informed consent.
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Interventions
The iEUS-SPL will automaticly detect solid pancreatic lesions and integrate the patients' endoscopic ultrasound images, endoscopic ultrasound features, clinical data and imaging features to perform a five-category classification for the lesions, categorizing them as pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis and chronic pancreatitis.
Locations(1)
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NCT07381192