Prospective clinical validation of a novel artificial intelligence system for real-time detection of solid pancreatic masses during endoscopic ultrasonography.
Ji Young Bang, Adrian Săftoiu, Anca Udriștoiu...
https://pubmed.ncbi.nlm.nih.gov/40953587Actively Recruiting
Age: 18Years +
All Genders
Healthy Volunteers
ID07381192
An Artificial Intelligence System for Multimodal, Multi-class Diagnosing Solid Pancreatic Lesions Based on Endoscopic Ultrasound
Led by Qilu Hospital of Shandong University · Updated on 2026-02-02
383
Participants Needed
1
Research Sites
N/A
Total Duration
On this page
Sponsors
Q
Qilu Hospital of Shandong University
Lead Sponsor
L
Liaocheng People's Hospital
Collaborating Sponsor
AI-Summary
What this Trial Is About
Researchers are evaluating an artificial intelligence system called iEUS-SPL (intelligent endoscopic ultrasound system-solid pancreatic lesion) designed to detect and classify solid pancreatic lesions during endoscopic ultrasound (EUS) examinations. This observational, prospective cohort study aims to validate how well iEUS-SPL performs in diagnosing various types of solid pancreatic lesions by analyzing EUS images, clinical data, and imaging features collected from patients. Participants are patients aged 18 years or older who are scheduled for EUS due to suspected solid pancreatic lesions based on symptoms, medical history, lab tests, or imaging. The study uses the iEUS-SPL device to automatically detect lesions in real-time EUS scanning videos and categorize them into five types: pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, and chronic pancreatitis. During the study, participants undergo EUS while iEUS-SPL analyzes the images and data to detect and classify lesions. Researchers will measure the system's accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and lesion detection rate during the procedure. They will also compare these results with those of expert endosonographers. Participant involvement includes consenting to the procedure and allowing their data to be used for evaluation, with the study continuing until June 2028.
CONDITIONS
Brief Title
An Artificial Intelligence System for Multimodal, Multi-class Diagnosing Solid Pancreatic Lesions Based on Endoscopic Ultrasound
Who Can Participate
Age: 18Years +
All Genders
Healthy Volunteers
Eligibility Criteria
You may qualify if you...
- Patients aged 18 years or older scheduled for endoscopic ultrasound (EUS) with suspected solid pancreatic lesions based on symptoms, medical history, lab tests, or imaging
- Patients who agree to participate and can sign informed consent
- Patients with no prior treatment for pancreatic lesions
You will not qualify if you...
- Patients with absolute contraindications to endoscopic ultrasound (EUS) examination
- Pregnant or lactating individuals
- Patients with uncorrectable blood clotting problems (PTT > 50 seconds or INR > 1.5) or very low platelet count (<50 x 10^9/L)
- Patients with upper gastrointestinal obstruction
- Patients who had pancreatic surgery, anatomical changes from other organ lesions, or congenital abnormalities
- Patients with biliary or pancreatic duct stent placement
- Patients who refuse to sign informed consent
AI-Screening
AI-Powered Screening
Complete this quick 3-step screening to check your eligibility
1
2
3
Your Study Journey
Screening
Duration - 2 to 4 weeks
Participants are screened for eligibility to participate in the trial.
1 visit (in-person)
Diagnostic Evaluation
Duration - 1 day
Participants undergo endoscopic ultrasound (EUS) where the iEUS-SPL device automatically detects and classifies solid pancreatic lesions using multimodal data during the procedure.
1 procedure visit (in-person)
Long-term Monitoring
Duration - Up to 6 years
Participants are observed following the diagnostic procedure to assess outcomes and lesion detection performance of the AI system.
Follow-up visits as needed per clinical routine
Trial Site Locations
Total: 1 location
1
Qilu Hospital of Shandong University
Jinan, Shandong, China, 250012
Actively Recruiting
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Research Team
Z
Zhen Li
How is the study designed?
Study Type
OBSERVATIONAL
Masking
N/A
Allocation
N/A
Model
N/A
Primary Purpose
N/A
Number of Arms
1
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Published Research Related To This Trial
Diagnosing Solid Lesions in the Pancreas With Multimodal Artificial Intelligence: A Randomized Crossover Trial.
Haochen Cui, Yuchong Zhao, Si Xiong...
https://pubmed.ncbi.nlm.nih.gov/39028670Validation of a real-time biliopancreatic endoscopic ultrasonography analytical device in China: a prospective, single-centre, randomised, controlled trial.
Hui Ling Wu, Li Wen Yao, Hui Ying Shi...
https://pubmed.ncbi.nlm.nih.gov/37775472Deep learning-based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video).
Jun Zhang, Liangru Zhu, Liwen Yao...
https://pubmed.ncbi.nlm.nih.gov/32387499Convolutional neural network-based object detection model to identify gastrointestinal stromal tumors in endoscopic ultrasound images.
Chang Kyo Oh, Taewan Kim, Yu Kyung Cho...
https://pubmed.ncbi.nlm.nih.gov/34369001Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail?
Dushyant Singh Dahiya, Mohammad Al-Haddad, Saurabh Chandan...
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Yoon Ho Kim, Gwang Ha Kim, Kwang Baek Kim...
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Xianzheng Qin, Minmin Zhang, Chunhua Zhou...
https://pubmed.ncbi.nlm.nih.gov/37455599Artificial intelligence-based diagnosis of standard endoscopic ultrasonography scanning sites in the biliopancreatic system: a multicenter retrospective study.
Shuxin Tian, Huiying Shi, Weigang Chen...
https://pubmed.ncbi.nlm.nih.gov/38079604Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach.
Seok Oh, Young-Jae Kim, Young-Taek Park...
https://pubmed.ncbi.nlm.nih.gov/35009788