Artificial Intelligence in EUS for Diagnosing Pancreatic Solid Lesions
Utilization of Artificial Intelligence for the Development of an EUS-convolution Neural Network Model Trained to Differentiate Pancreatic Cancer From Other Pancreatic Solid Lesions
1 other identifier
observational
130
1 country
1
Brief Summary
We aim to develop an EUS-AI model which can facilitate clinical diagnosis by analyzing EUS pictures and clinical parameters of patients.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P50-P75 for all trials
Started Jul 2022
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
Click on a node to explore related trials.
Study Timeline
Key milestones and dates
Study Start
First participant enrolled
July 1, 2022
CompletedFirst Submitted
Initial submission to the registry
July 25, 2022
CompletedFirst Posted
Study publicly available on registry
July 27, 2022
CompletedPrimary Completion
Last participant's last visit for primary outcome
June 30, 2023
CompletedStudy Completion
Last participant's last visit for all outcomes
January 24, 2024
CompletedApril 3, 2024
April 1, 2024
12 months
July 25, 2022
April 2, 2024
Conditions
Keywords
Outcome Measures
Primary Outcomes (1)
The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion
Receiver operating characteristic (ROC) analyses, sensitivity, specificity, accuracy, positive predictive value and negative predictive value will be used to evaluate the efficacy of the model.
After the training process of the EUS-AI model is completed
Secondary Outcomes (1)
The model's ability to specify the pancreatic solid lesions such as pancreatic cancer, CP, AIP and NET
After the training process of the EUS-AI model is completed
Study Arms (1)
Pancreas-EUS
Patients since 2014 with EUS pictures of normal pancreas or pancreatic solid lesions have been included in this cohort.
Interventions
The test subset (approximately 20% of total patients) is reserved for the final evaluation of the EUS-AI model. Clinical parameters and EUS pictures of each patient in the test subset will be inputed into the trained EUS-AI model, and the most possible diagnosis will be given by the model.
Eligibility Criteria
The cohort will be selected from Tongji Hospital, Tongji Medical College, HUST.
You may qualify if:
- Patients who underwent EUS using a curved line array echoendoscope (GF-UCT260; Olympus Medical Systems) since 2014 in our affiliation.
- For each patient, all available native EUS pictures are included.
- Patients' diagnosis are validated by surgical outcomes or fine-needle aspiration (FNA) findings and have a compatible clinical course with a follow-up period of more than 6 months.
You may not qualify if:
- The image is of poor quality.
- The images contain unique marks which can potentially bias the model, such as the biopsy needle.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Tongji hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, 430030, China
Related Publications (1)
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.
PMID: 39028670DERIVED
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- professor
Study Record Dates
First Submitted
July 25, 2022
First Posted
July 27, 2022
Study Start
July 1, 2022
Primary Completion
June 30, 2023
Study Completion
January 24, 2024
Last Updated
April 3, 2024
Record last verified: 2024-04