An observational study in Pancreatic Cancer, Diagnose Disease and IPMN, Pancreatic, sponsored by Changhai Hospital. Recruiting at 1 site in China. Open to participants aged 18 Years to 80 Years. Per ClinicalTrials.gov, last updated 2026-02-27.
Sponsored by Changhai Hospital · Observational
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.
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:
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.
3,235 studies on the registry are indexed under Pancreatic Neoplasms; 899 are open to participants now.
This study's planned enrollment of 2,000 is above the median of 200 across 620 observational studies indexed under Pancreatic Neoplasms.
Browse Pancreatic Neoplasms studies →Changhai Hospital is the lead sponsor of 342 studies on the registry; 133 are open to participants now.
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The study enrolls patients with clinically suspected pancreatic disease who have available contrast-enhanced CT scans and provide informed consent. Patients are excluded if they have a history of pancreatic surgery, contraindications to contrast media, suboptimal image quality, or other conditions deemed unsuitable by the investigator (e.g., pregnancy, cognitive impairment, or concurrent trial participation).
Exclusion Criteria:
Diagnosis by Artificial Intelligence model
Diagnostic Test: Diagnosis by Artificial Intelligence model
Diagnosis by clinicians
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.
Classification accuracy
The percentage of cases correctly classified by AI out of the total number of cases.
Time frame: From date of contrast-enhanced CT scan to 1 year
Agreement rate with clinical decisions
The proportion of total cases where AI and clinician classification results are in agreement.
Time frame: From date of contrast-enhanced CT scan to 1 year
Percentage decrease in unnecessary surgical procedures
The percentage reduction in the unnecessary surgery rate achieved by AI decision-making compared to traditional decision-making.
Time frame: From date of contrast-enhanced CT scan to 1 year
Malignancy miss rate
The proportion of cases classified by AI as non-surgical that actually required surgery.
Time frame: From date of contrast-enhanced CT scan to 1 year
Plan to share: No
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