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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
Study Type
Study Type
Enrollment (Actual)
Enrollment
Contacts and Locations
Study Locations
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Hubei
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Wuhan, Hubei, China, 430030
- Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- 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.
Exclusion Criteria:
- The image is of poor quality.
- The images contain unique marks which can potentially bias the model, such as the biopsy needle.
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
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Pancreas-EUS
Patients since 2014 with EUS pictures of normal pancreas or pancreatic solid lesions have been included in this cohort.
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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.
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What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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The model's ability to differentiate pancreatic cancer from other pancreatic solid lesion
Time Frame: After the training process of the EUS-AI model is completed
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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.
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After the training process of the EUS-AI model is completed
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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The model's ability to specify the pancreatic solid lesions such as pancreatic cancer, CP, AIP and NET
Time Frame: After the training process of the EUS-AI model is completed
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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.
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After the training process of the EUS-AI model is completed
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Actual)
Primary Completion
Study Completion (Actual)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
- Digestive System Diseases
- Pathologic Processes
- Immune System Diseases
- Neoplasms by Histologic Type
- Neoplasms
- Autoimmune Diseases
- Disease Attributes
- Neuroectodermal Tumors
- Neoplasms, Germ Cell and Embryonal
- Neoplasms, Nerve Tissue
- Pancreatic Diseases
- Chronic Disease
- Pancreatitis
- Pancreatitis, Chronic
- Neuroendocrine Tumors
- Autoimmune Pancreatitis
Other Study ID Numbers
Other Study ID Numbers
- EUS-AI 2022
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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