An Artificial Intelligence System for Multimodal, Multi-class Diagnosing Solid Pancreatic Lesions Based on Endoscopic Ultrasound

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.

Study Overview

• Status: Recruiting
• Conditions: Endoscopic Ultrasound (EUS); Solid Pancreatic Lesion
• Intervention / Treatment: Device: iEUS-SPL(intelligent endoscopic ultrasound system-pancreatic solid lesion)

Detailed Description

This is an observational study with a prospective, cohort design. We have developed an artificial intelligence system named iEUS-SPL for multimodal, multi-class diagnosing solid pancreatic lesions using endoscopic ultrasound images, endoscopic ultrasound features, clinical data and imaging features from retrospectively collected patients who underwent EUS. The lesion detection rate and diagnostic performance of iEUS-SPL in identifying solid pancreatic lesions will be evaluated in real-time EUS scanning videos over prospective enrolled cases.

• Study Type: Observational
• Enrollment (Estimated): 383

Contacts and Locations

• Study Contact: Name: Zhen Li; Phone Number: 18560086106; Email: qilulizhen@sdu.edu.cn

Study Locations

• China
  • Shandong
    • Jinan, Shandong, China, 250012
      • Recruiting
      • Qilu Hospital of Shandong University
      • Contact: Zhen Li, doctor; Phone Number: 18560086106; Email: qilulizhen@sdu.edu.cn

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Non-Probability Sample

Study Population

Adult patients with suspected solid pancreatic lesions undergoing EUS.

Description

Inclusion Criteria:

1. 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.
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<50×109/L).
4. Upper gastrointestinal obstruction.
5. 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.
6. Patients who have undergone biliary/pancreatic duct stent placement.
7. Patients who refuse to sign the informed consent.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

Intervention / Treatment

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.

Device: iEUS-SPL(intelligent endoscopic ultrasound system-pancreatic solid lesion); 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.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
Comparison of the accuracy between iEUS-SPL and endosonographers	The secondary outcome of the study is to comparing the accuracy between iEUS-SPL and different-level endosonographers in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).	During procedure
Comparison of the sensitivity between iEUS-SPL and endosonographers	The secondary outcome of the study is to comparing the sensitivity between iEUS-SPL and different-level endosonographers in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).	During procedure
Comparison of the specificity between iEUS-SPL and endosonographers	The secondary outcome of the study is to comparing the specificity between iEUS-SPL and different-level endosonographers in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).	During procedure
Comparison of the postive predictive value between iEUS-SPL and endosonographers	The secondary outcome of the study is to comparing the postive predictive value between iEUS-SPL and different-level endosonographers in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).	During procedure
Comparison of the negative predictive value between iEUS-SPL and endosonographers	The secondary outcome of the study is to comparing the negative predictive value between iEUS-SPL and different-level endosonographers in identifying the solid pancreatic lesions (including pancreatic cancer, pancreatic neuroendocrine tumor, solid pseudopapillary tumor, autoimmune pancreatitis, chronic pancreatitis).	During procedure

Collaborators and Investigators

• Sponsor: Qilu Hospital of Shandong University
• Collaborators: The Affiliated Hospital of Qingdao University; Shandong Provincial Hospital; Qianfoshan Hospital; Liaocheng People's Hospital; Taian City Central Hospital; Shengli Oilfield Hospital; Binzhou People's Hospital; Qilu Hospital of Shandong University (Qingdao); Binzhou Medical University

Publications and helpful links

General Publications

• Wu 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.
• Oh 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.
• 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. 2025 Oct 13. doi: 10.1055/a-2701-6530. Online ahead of print.
• Cui 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.
• Zhang J, Zhu L, Yao L, Ding X, Chen D, Wu H, Lu Z, Zhou W, Zhang L, An P, Xu B, Tan W, Hu S, Cheng F, Yu H. Deep learning-based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video). Gastrointest Endosc. 2020 Oct;92(4):874-885.e3. doi: 10.1016/j.gie.2020.04.071. Epub 2020 May 6.
• Dahiya DS, Al-Haddad M, Chandan S, Gangwani MK, Aziz M, Mohan BP, Ramai D, Canakis A, Bapaye J, Sharma N. Artificial Intelligence in Endoscopic Ultrasound for Pancreatic Cancer: Where Are We Now and What Does the Future Entail? J Clin Med. 2022 Dec 16;11(24):7476. doi: 10.3390/jcm11247476.
• Kim 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.
• Qin X, Zhang M, Zhou C, Ran T, Pan Y, Deng Y, Xie X, Zhang Y, Gong T, Zhang B, Zhang L, Wang Y, Li Q, Wang D, Gao L, Zou D. A deep learning model using hyperspectral image for EUS-FNA cytology diagnosis in pancreatic ductal adenocarcinoma. Cancer Med. 2023 Aug;12(16):17005-17017. doi: 10.1002/cam4.6335. Epub 2023 Jul 17.
• Tian S, Shi H, Chen W, Li S, Han C, Du F, Wang W, Wen H, Lei Y, Deng L, Tang J, Zhang J, Lin J, Shi L, Ning B, Zhao K, Miao J, Wang G, Hou H, Huang X, Kong W, Jin X, Ding Z, Lin R. Artificial intelligence-based diagnosis of standard endoscopic ultrasonography scanning sites in the biliopancreatic system: a multicenter retrospective study. Int J Surg. 2024 Mar 1;110(3):1637-1644. doi: 10.1097/JS9.0000000000000995.
• Oh 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.
• Norton 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.
• Nakamura 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.
• Dhali A, Kipkorir V, Srichawla BS, Kumar H, Rathna RB, Ongidi I, Chaudhry T, Morara G, Nurani K, Cheruto D, Biswas J, Chieng LR, Dhali GK. Artificial intelligence assisted endoscopic ultrasound for detection of pancreatic space-occupying lesion: a systematic review and meta-analysis. Int J Surg. 2023 Dec 1;109(12):4298-4308. doi: 10.1097/JS9.0000000000000717.
• Das A, Nguyen CC, Li F, Li B. Digital image analysis of EUS images accurately differentiates pancreatic cancer from chronic pancreatitis and normal tissue. Gastrointest Endosc. 2008 May;67(6):861-7. doi: 10.1016/j.gie.2007.08.036. Epub 2008 Jan 7.
• Kuwahara 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.
• Tian G, Xu D, He Y, Chai W, Deng Z, Cheng C, Jin X, Wei G, Zhao Q, Jiang T. Deep learning for real-time auxiliary diagnosis of pancreatic cancer in endoscopic ultrasonography. Front Oncol. 2022 Oct 7;12:973652. doi: 10.3389/fonc.2022.973652. eCollection 2022.
• Qin X, Ran T, Chen Y, Zhang Y, Wang D, Zhou C, Zou D. Artificial Intelligence in Endoscopic Ultrasonography-Guided Fine-Needle Aspiration/Biopsy (EUS-FNA/B) for Solid Pancreatic Lesions: Opportunities and Challenges. Diagnostics (Basel). 2023 Sep 26;13(19):3054. doi: 10.3390/diagnostics13193054.
• Goyal H, Sherazi SAA, Gupta S, Perisetti A, Achebe I, Ali A, Tharian B, Thosani N, Sharma NR. Application of artificial intelligence in diagnosis of pancreatic malignancies by endoscopic ultrasound: a systemic review. Therap Adv Gastroenterol. 2022 Apr 29;15:17562848221093873. doi: 10.1177/17562848221093873. eCollection 2022.

Study record dates

Study Major Dates

• Study Start (Actual): September 1, 2025
• Primary Completion (Estimated): June 30, 2028
• Study Completion (Estimated): June 30, 2028

Study Registration Dates

• First Submitted: December 24, 2025
• First Submitted That Met QC Criteria: January 23, 2026
• First Posted (Actual): February 2, 2026

Study Record Updates

• Last Update Posted (Actual): February 2, 2026
• Last Update Submitted That Met QC Criteria: January 23, 2026
• Last Verified: September 1, 2025

More Information

Terms related to this study

• Keywords: endoscopic ultrasound; solid pancreatic lesion
• Other Study ID Numbers: 2025-SDU-QILU-6

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No