AI-Powered Precision Decision-Making for Pancreatic Diseases

Condition(s) studied

Investigational drug(s) / intervention(s)

Diagnosis by Artificial Intelligence model: 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.

Study summary

This multicenter clinical trial evaluates an artificial intelligence (AI) system designed to assist in the diagnosis and management of pancreatic diseases. Using contrast-enhanced CT scans, the study compares the AI's recommendations against the decisions of experienced clinicians to verify the system's accuracy and safety in a real-world setting. Patients are categorized into three management groups: Intervention (surgery/treatment), Intensive Surveillance (close monitoring), or Routine Surveillance (standard follow-up). The primary goal is to determine if the AI system can reliably classify patients, reduce the risk of missing malignant lesions, and prevent unnecessary surgeries, thereby improving clinical decision-making for pancreatic conditions.

Eligibility

Primary outcome measure(s)

• Classification accuracy — From date of contrast-enhanced CT scan to 1 year
  The percentage of cases correctly classified by AI out of the total number of cases.

Trial sites (1)

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