AI-Powered Precision Decision-Making for Pancreatic Diseases
A Multicenter Clinical Study on AI-Powered Precision Decision-Making Management for Pancreatic Diseases Using Contrast-Enhanced CT
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
MEHTOD: This multicenter clinical trial evaluates the reliability and effectiveness of an AI system for patients with pancreatic diseases in a real-world clinical environment. The study calculates the AI system's classification accuracy using pathological diagnosis (biopsy/surgery results) or long-term follow-up as the "gold standard" for comparison. Additionally, the safety and clinical utility of the management strategies recommended by the AI are assessed by measuring the risk of missing malignant lesions, the rate of unnecessary surgeries for pancreatic diseases, and the level of agreement with traditional clinical decisions.
STUDY DESIGN
All contrast-enhanced CT images from patients with pancreatic diseases are analyzed by the AI system to generate a classification result (Intervention, Intensive Surveillance, or Routine Surveillance). Simultaneously, clinical doctors review the same data and categorize patients into these three groups to determine their actual care plan:
- INTERVENTION: Patients assessed by doctors as needing "Intervention" are recommended for further surgical evaluation or treatment.
- INTENSIVE SURVEILLANCE: Patients assessed by doctors as needing "Intensive Surveillance" receive a personalized, high-frequency follow-up plan until the study endpoint.
- ROUTINE SURVEILLANCE: Patients assessed by doctors as needing "Routine Surveillance" undergo follow-up for at least one year. If abnormalities arise during this period, the patient is transferred to the appropriate "Intervention" or "Intensive Surveillance" protocol.
OUTCOMES: The study compares the performance of the AI system against clinical doctors regarding classification accuracy, the risk of missed diagnoses, unnecessary surgery rates, and decision consistency. These metrics are used to validate the AI system's value, safety, and utility in the clinical management of pancreatic diseases.
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Beilei Wang, Doctor
- Phone Number: +86 13774238083
- Email: lilly_wang@126.com
Study Locations
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Shanghai, China, 200433
- Recruiting
- Changhai Hospital
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Contact:
- Beilei Wang, Doctor
- Phone Number: +86 13774238083
- Email: lilly_wang@126.com
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Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Adult
- Older Adult
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Clinically suspected pancreatic disease.
- Scheduled to undergo contrast-enhanced CT.
- Signed informed consent form indicating agreement to participate.
Exclusion Criteria:
- History of pancreatic surgery.
- Contraindications to contrast-enhanced CT, including known hypersensitivity to iodinated contrast media or severe renal/hepatic dysfunction.
- Suboptimal image quality affecting diagnosis.
- Concurrent participation in another interventional clinical trial.
- Unsuitability for participation as determined by the investigator, including but not limited to: pregnancy or lactation, severe psychiatric disorders or cognitive impairment, significant comorbidities that may interfere with study results or patient safety.
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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AI group
Diagnosis by Artificial Intelligence model
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To develop an artificial intelligence-based classification management system for pancreatic diseases, achieving automated and precise classification.
Contrast-enhanced CT images from all study subjects will be analyzed by the AI system to generate classification results, categorizing patients into three groups: INTERVENTIOM, INTENSIVE SURVEILLANCE or ROUTINE SURVEILLANCE.
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Clinicians group
Diagnosis by clinicians
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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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Classification accuracy
Time Frame: From date of contrast-enhanced CT scan to 1 year
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The percentage of cases correctly classified by AI out of the total number of cases.
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From date of contrast-enhanced CT scan to 1 year
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Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
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Agreement rate with clinical decisions
Time Frame: From date of contrast-enhanced CT scan to 1 year
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The proportion of total cases where AI and clinician classification results are in agreement.
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From date of contrast-enhanced CT scan to 1 year
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Percentage decrease in unnecessary surgical procedures
Time Frame: From date of contrast-enhanced CT scan to 1 year
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The percentage reduction in the unnecessary surgery rate achieved by AI decision-making compared to traditional decision-making.
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From date of contrast-enhanced CT scan to 1 year
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Malignancy miss rate
Time Frame: From date of contrast-enhanced CT scan to 1 year
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The proportion of cases classified by AI as non-surgical that actually required surgery.
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From date of contrast-enhanced CT scan to 1 year
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Collaborators and Investigators
Sponsor
Sponsor
Collaborators
Collaborators
Study record dates
Study Major Dates
Study Start (Estimated)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
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
- Endocrine System Diseases
- Pathologic Processes
- Neoplasms by Site
- Neoplasms
- Chronic Disease
- Disease Attributes
- Neoplasms by Histologic Type
- Digestive System Neoplasms
- Digestive System Diseases
- Endocrine Gland Neoplasms
- Pancreatic Diseases
- Neoplasms, Glandular and Epithelial
- Adenoma
- Neoplasms, Ductal, Lobular, and Medullary
- Pathological Conditions, Signs and Symptoms
- Pancreatic Intraductal Neoplasms
- Pancreatitis
- Pancreatic Neoplasms
- Pancreatitis, Chronic
- Adenoma, Islet Cell
Other Study ID Numbers
Other Study ID Numbers
- Prospective PRISM
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
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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