An Artificial Intelligence System for Multimodal, Multi-class Diagnosing Solid Pancreatic Lesions Based on Endoscopic Ultrasound
1 other identifier
observational
383
1 country
1
Brief 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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Sep 2025
Typical duration for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
September 1, 2025
CompletedFirst Submitted
Initial submission to the registry
December 24, 2025
CompletedFirst Posted
Study publicly available on registry
February 2, 2026
CompletedPrimary Completion
Last participant's last visit for primary outcome
June 30, 2028
ExpectedStudy Completion
Last participant's last visit for all outcomes
June 30, 2028
February 2, 2026
September 1, 2025
2.8 years
December 24, 2025
January 23, 2026
Conditions
Keywords
Outcome Measures
Primary Outcomes (6)
The accuracy of iEUS-SPL for solid pancreatic lesions
The primary outcome of the study is to evaluate the accuracy of the iEUS-SPL in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).
During procedure
The sensitivity of iEUS-SPL for solid pancreatic lesions
The primary outcome of the study is to evaluate the sensitivity of the iEUS-SPL in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).
During procedure
The specificicy of iEUS-SPL for solid pancreatic lesions
The primary outcome of the study is to evaluate the specificity of the iEUS-SPL in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).
During procedure
The postive predictive value of iEUS-SPL for solid pancreatic lesions
The primary outcome of the study is to evaluate the postive predictive value of the iEUS-SPL in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).
During procedure
The negative predictive value of iEUS-SPL for solid pancreatic lesions
The primary outcome of the study is to evaluate the negative predictive value of the iEUS-SPL in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).
During procedure
the lesion detection rate of iEUS-SPL for detecting solid pancreatic lesions
The primary outcome of the study is to evaluate the lesion detection rate of the iEUS-SPL in identifying the solid pancreatic lesions(defined as the number of detected lesions divided by the total number of lesions).
During procedure
Secondary Outcomes (5)
Comparison of the accuracy between iEUS-SPL and endosonographers
During procedure
Comparison of the sensitivity between iEUS-SPL and endosonographers
During procedure
Comparison of the specificity between iEUS-SPL and endosonographers
During procedure
Comparison of the postive predictive value between iEUS-SPL and endosonographers
During procedure
Comparison of the negative predictive value between iEUS-SPL and endosonographers
During procedure
Study Arms (1)
Patients undergoing EUS
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.
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.
Eligibility Criteria
Adult patients with suspected solid pancreatic lesions undergoing EUS.
You may qualify if:
- 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.
You may not qualify if:
- 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\<50Ă—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.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
- Qilu Hospital of Shandong Universitylead
- The Affiliated Hospital of Qingdao Universitycollaborator
- Liaocheng People's Hospitalcollaborator
- Taian City Central Hospitalcollaborator
- Qilu Hospital of Shandong University (Qingdao)collaborator
- Binzhou People's Hospitalcollaborator
- Shandong Provincial Hospitalcollaborator
- Qianfoshan Hospitalcollaborator
- Shengli Oilfield Hospitalcollaborator
- Binzhou Medical Universitycollaborator
Study Sites (1)
Qilu Hospital of Shandong University
Jinan, Shandong, 250012, China
Related Publications (18)
Bang JY, Saftoiu A, Udristoiu A, Gruionu L, Codruta Gheorghe E, Gruionu G, Ramesh J, Wilcox CM, Varadarajulu S. Prospective clinical validation of a novel artificial intelligence system for real-time detection of solid pancreatic masses during endoscopic ultrasonography. Endoscopy. 2026 Mar;58(3):223-232. doi: 10.1055/a-2701-6530. Epub 2025 Sep 15.
PMID: 40953587BACKGROUNDCui H, Zhao Y, Xiong S, Feng Y, Li P, Lv Y, Chen Q, Wang R, Xie P, Luo Z, Cheng S, Wang W, Li X, Xiong D, Cao X, Bai S, Yang A, Cheng B. Diagnosing Solid Lesions in the Pancreas With Multimodal Artificial Intelligence: A Randomized Crossover Trial. JAMA Netw Open. 2024 Jul 1;7(7):e2422454. doi: 10.1001/jamanetworkopen.2024.22454.
PMID: 39028670BACKGROUNDWu HL, Yao LW, Shi HY, Wu LL, Li X, Zhang CX, Chen BR, Zhang J, Tan W, Cui N, Zhou W, Zhang JX, Xiao B, Gong RR, Ding Z, Yu HG. Validation of a real-time biliopancreatic endoscopic ultrasonography analytical device in China: a prospective, single-centre, randomised, controlled trial. Lancet Digit Health. 2023 Nov;5(11):e812-e820. doi: 10.1016/S2589-7500(23)00160-7. Epub 2023 Sep 27.
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PMID: 32387499BACKGROUNDOh CK, Kim T, Cho YK, Cheung DY, Lee BI, Cho YS, Kim JI, Choi MG, Lee HH, Lee S. Convolutional neural network-based object detection model to identify gastrointestinal stromal tumors in endoscopic ultrasound images. J Gastroenterol Hepatol. 2021 Dec;36(12):3387-3394. doi: 10.1111/jgh.15653. Epub 2021 Aug 16.
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PMID: 36556092BACKGROUNDKim YH, Kim GH, Kim KB, Lee MW, Lee BE, Baek DH, Kim DH, Park JC. Application of A Convolutional Neural Network in The Diagnosis of Gastric Mesenchymal Tumors on Endoscopic Ultrasonography Images. J Clin Med. 2020 Sep 29;9(10):3162. doi: 10.3390/jcm9103162.
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PMID: 38079604BACKGROUNDOh S, Kim YJ, Park YT, Kim KG. Automatic Pancreatic Cyst Lesion Segmentation on EUS Images Using a Deep-Learning Approach. Sensors (Basel). 2021 Dec 30;22(1):245. doi: 10.3390/s22010245.
PMID: 35009788BACKGROUNDNorton ID, Zheng Y, Wiersema MS, Greenleaf J, Clain JE, Dimagno EP. Neural network analysis of EUS images to differentiate between pancreatic malignancy and pancreatitis. Gastrointest Endosc. 2001 Nov;54(5):625-9. doi: 10.1067/mge.2001.118644.
PMID: 11677484BACKGROUNDNakamura H, Fukuda M, Matsuda A, Makino N, Kimura H, Ohtaki Y, Nawa Y, Oyama S, Suzuki Y, Kobayashi T, Ishizawa T, Kakizaki Y, Ueno Y. Differentiating localized autoimmune pancreatitis and pancreatic ductal adenocarcinoma using endoscopic ultrasound images with deep learning. DEN Open. 2024 Mar 2;4(1):e344. doi: 10.1002/deo2.344. eCollection 2024 Apr.
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PMID: 18179797BACKGROUNDKuwahara T, Hara K, Mizuno N, Haba S, Okuno N, Kuraishi Y, Fumihara D, Yanaidani T, Ishikawa S, Yasuda T, Yamada M, Onishi S, Yamada K, Tanaka T, Tajika M, Niwa Y, Yamaguchi R, Shimizu Y. Artificial intelligence using deep learning analysis of endoscopic ultrasonography images for the differential diagnosis of pancreatic masses. Endoscopy. 2023 Feb;55(2):140-149. doi: 10.1055/a-1873-7920. Epub 2022 Jun 10.
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PMID: 35509425BACKGROUND
Biospecimen
Endoscopic ultrasound images, endoscopic ultrasound features, clinical data and imaging features from patients who underwent endoscopic ultrasound.
Central Study Contacts
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Target Duration
- 6 Months
- Sponsor Type
- OTHER
- Responsible Party
- SPONSOR
Study Record Dates
First Submitted
December 24, 2025
First Posted
February 2, 2026
Study Start
September 1, 2025
Primary Completion (Estimated)
June 30, 2028
Study Completion (Estimated)
June 30, 2028
Last Updated
February 2, 2026
Record last verified: 2025-09
Data Sharing
- IPD Sharing
- Will not share