Endoscopic Ultrasound (EUS)NCT07381192
An Artificial Intelligence System for Multimodal, Multi-class Diagnosing Solid Pancreatic Lesions Based on Endoscopic Ultrasound
An observational study of Endoscopic Ultrasound (EUS) and Solid Pancreatic Lesion, run by Qilu Hospital of Shandong University at 1 site in China.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Endoscopic Ultrasound (EUS), Solid Pancreatic Lesion
- What is being tested?
- iEUS-SPL(intelligent endoscopic ultrasound system-pancreatic solid lesion)
- Type of treatment
- Medical device
- How many people
- 383 participants
- Where
- 1 siteChina
- Study dates
- 1 Sep 2025to 30 Jun 2028
- How long will I take part?
- 6 Months
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Adult patients with suspected solid pancreatic lesions undergoing EUS.
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Qilu Hospital of Shandong University
Jinan, Shandong, 250012
Zhen Li, doctor, 18560086106, qilulizhen@sdu.edu.cn
About this study
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.
Read the 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.
- Being tested
- iEUS-SPL(intelligent endoscopic ultrasound system-pancreatic solid lesion)
- Keywords
- endoscopic ultrasound, solid pancreatic lesion
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
iEUS-SPL(intelligent endoscopic ultrasound system-pancreatic solid lesion)Device
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: iEUS-SPL(intelligent endoscopic ultrasound system-pancreatic solid lesion) |
What is being measured
The primary outcome is the main question the study is designed to answer.
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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: During procedure
Secondary outcomes (5)
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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: 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).
Measured: During procedure
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Feb 2026
- Study start1 Sep 2025
- Primary completion30 Jun 2028 (estimated)
- Study completion30 Jun 2028 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (18)Papers the sponsor has linked to this study.
- Other trials for Endoscopic Ultrasound (EUS)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 383 participants (estimated)
- Target duration
- 6 Months
Sponsor and investigators
- Lead sponsor
- Qilu Hospital of Shandong University
- Sponsor type
- University, hospital or other
- Collaborators
- Liaocheng People's Hospital, Taian City Central Hospital, Qilu Hospital of Shandong University (Qingdao), Binzhou People's Hospital, Shandong Provincial Hospital, The Affiliated Hospital of Qingdao University, Qianfoshan Hospital, Shengli Oilfield Hospital, Binzhou Medical University
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07381192
- Sponsor's protocol ID
- 2025-SDU-QILU-6
- Status last verified
- Sep 2025
- First submitted
- 24 Dec 2025
- Last updated
- 2 Feb 2026
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
PubMed 40953587Cui 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.
PubMed 39028670Wu 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.
PubMed 37775472Zhang 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.
PubMed 32387499Oh 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.
PubMed 34369001Dahiya 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.
PubMed 36556092Kim 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.
PubMed 33003602Qin 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.
PubMed 37455599Tian 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.
PubMed 38079604Oh 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.
PubMed 35009788Norton 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.
PubMed 11677484Nakamura 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.
PubMed 38434146Dhali 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.
PubMed 37800594Das 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.
PubMed 18179797Kuwahara 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.
PubMed 35688454
Study at a glance
From the registry record, last updated by the sponsor on 2 Feb 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- An Artificial Intelligence System for Multimodal, Multi-class Diagnosing Solid Pancreatic Lesions Based on Endoscopic Ultrasound
- ClinicalTrials.gov ID
- NCT07381192
- Acronym / sponsor ID
- 2025-SDU-QILU-6
- Keywords
- endoscopic ultrasound, solid pancreatic lesion
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 383 participants (estimated)
- Lead sponsor
- Qilu Hospital of Shandong UniversityUniversity, hospital or other
- Collaborators
- Liaocheng People's Hospital, Taian City Central Hospital, Qilu Hospital of Shandong University (Qingdao), Binzhou People's Hospital, Shandong Provincial Hospital, The Affiliated Hospital of Qingdao University, Qianfoshan Hospital, Shengli Oilfield Hospital, Binzhou Medical University
- Study start
- 1 Sep 2025 (actual)
- Primary completion
- 30 Jun 2028 (estimated)
- Study completion
- 30 Jun 2028 (estimated)
- First posted
- 2 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Target follow-up
- 6 Months
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Patients undergoing EUS | – | Device: iEUS-SPL(intelligent endoscopic ultrasound system-pancreatic solid lesion)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. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients with suspected solid pancreatic lesions undergoing EUS.
Key inclusion criteria (2 listed)
- 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.
Key exclusion criteria (7 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| 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. | Device | Patients undergoing EUS |
Primary outcome
The accuracy of iEUS-SPL for solid pancreatic lesions
Time frame: During procedure
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).
The sensitivity of iEUS-SPL for solid pancreatic lesions
Time frame: During procedure
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).
The specificicy of iEUS-SPL for solid pancreatic lesions
Time frame: During procedure
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).
The postive predictive value of iEUS-SPL for solid pancreatic lesions
Time frame: During procedure
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).
The negative predictive value of iEUS-SPL for solid pancreatic lesions
Time frame: During procedure
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).
the lesion detection rate of iEUS-SPL for detecting solid pancreatic lesions
Time frame: During procedure
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).
5 secondary outcomes
Comparison of the accuracy between iEUS-SPL and endosonographers
Time frame: During procedure
Comparison of the sensitivity between iEUS-SPL and endosonographers
Time frame: During procedure
Comparison of the specificity between iEUS-SPL and endosonographers
Time frame: During procedure
Comparison of the postive predictive value between iEUS-SPL and endosonographers
Time frame: During procedure
Comparison of the negative predictive value between iEUS-SPL and endosonographers
Time frame: During procedure
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 2 Feb 2026.
- Background: PubMed 40953587Bang 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.
- Background: PubMed 39028670Cui 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.
- Background: PubMed 37775472Wu 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.
- Background: PubMed 32387499Zhang 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.
- Background: PubMed 34369001Oh 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.
- Background: PubMed 36556092Dahiya 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.
- Background: PubMed 33003602Kim 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.
- Background: PubMed 37455599Qin 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.
- Background: PubMed 38079604Tian 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.
- Background: PubMed 35009788Oh 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.
- Background: PubMed 11677484Norton 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.
- Background: PubMed 38434146Nakamura 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Endoscopic Ultrasound (EUS) and Solid Pancreatic Lesion, run by Qilu Hospital of Shandong University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 7 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 2 Feb 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Zhen Li (18560086106, qilulizhen@sdu.edu.cn), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The accuracy of iEUS-SPL for solid pancreatic lesions (time frame: During procedure). The study lists 6 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07381192, which also lists 18 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 2 Feb 2026; AzTrials last refreshed its copy on 31 Oct 2026.