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

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.

Study Overview

• Status: Recruiting
• Conditions: Pancreatic Cancer; Chronic Pancreatitis; Pancreatic Neuroendocrine Tumor; Pancreatic Cystic Lesions; Diagnose Disease; IPMN, Pancreatic; Acute Pancreatitis (AP)
• Intervention / Treatment: Diagnostic test: Diagnosis by Artificial Intelligence model

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:

1. INTERVENTION: Patients assessed by doctors as needing "Intervention" are recommended for further surgical evaluation or treatment.
2. INTENSIVE SURVEILLANCE: Patients assessed by doctors as needing "Intensive Surveillance" receive a personalized, high-frequency follow-up plan until the study endpoint.
3. 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: Observational
• Enrollment (Estimated): 2000

Contacts and Locations

• Study Contact: Name: Beilei Wang, Doctor; Phone Number: +86 13774238083; Email: lilly_wang@126.com

Study Locations

• China
  • Shanghai, China, 200433
    • Recruiting
    • Changhai Hospital
    • Contact: Beilei Wang, Doctor; Phone Number: +86 13774238083; Email: lilly_wang@126.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

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).

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?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
AI group; Diagnosis by Artificial Intelligence model	Diagnostic test: 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.
Clinicians group; Diagnosis by clinicians

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Classification accuracy	The percentage of cases correctly classified by AI out of the total number of cases.	From date of contrast-enhanced CT scan to 1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Agreement rate with clinical decisions	The proportion of total cases where AI and clinician classification results are in agreement.	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.	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.	From date of contrast-enhanced CT scan to 1 year

Collaborators and Investigators

• Sponsor: Changhai Hospital
• Collaborators: The First Affiliated Hospital with Nanjing Medical University; Shengjing Hospital; Xinhua Hospital, Shanghai Jiao Tong University School of Medicine; Shanghai Changzheng Hospital; The Second Affiliated Hospital of Jiaxing University; The Affiliated People's Hospital of Ningbo University; Shanghai Fourth People's Hospital Tongji University; The First Affiliated Hospital of Medical School of Zhejiang University; Shanghai Fudan University Cancer Center

Study record dates

Study Major Dates

• Study Start (Estimated): March 1, 2026
• Primary Completion (Estimated): October 31, 2029
• Study Completion (Estimated): October 31, 2029

Study Registration Dates

• First Submitted: February 23, 2026
• First Submitted That Met QC Criteria: February 23, 2026
• First Posted (Actual): February 27, 2026

Study Record Updates

• Last Update Posted (Actual): February 27, 2026
• Last Update Submitted That Met QC Criteria: February 23, 2026
• Last Verified: February 1, 2026

More Information

Terms related to this study

• Keywords: Artificial Intelligence (AI), Deep Learning, Contrast-Enhanced CT, Multicenter Clinical Trial, Real-World 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: Prospective PRISM

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No