Validation of Joint-AI in Diagnosing Pancreatic Solid Lesions

December 22, 2024 updated by: Bin Cheng, Huazhong University of Science and Technology

Validation of a Multimodal Artificial Intelligence Model in in Diagnosing Pancreatic Solid Lesions: a Prospective, Multicenter, Randomized, Controlled Trial

This clinical trial aims to learn if a multimodal artificial intelligence (AI) model can enhance the diagnosis of pancreatic solid lesions. The main questions it aims to answer are:

  1. Does the AI model enhance the diagnostic performance of endoscopists in diagnosing pancreatic solid lesions?
  2. Does the addition of interpretability analysis further improve the diagnostic performance of the assisted endoscopists? Researchers will compare the diagnostic performance of endoscopists with or without the assistance of the AI model.

Participants will:

  1. Their clinical data will be prospectively collected.
  2. They will be randomized to the AI-assist group and the conventional diagnosis group.

Study Overview

Status

Not yet recruiting

Conditions

Intervention / Treatment

Detailed Description

The investigators have previously developed a multimodal AI model (Joint-AI) based on endoscopic ultrasound images and clinical data to diagnose pancreatic solid lesions. This study aims to improve the Joint-AI model's performance with a prospectively collected dataset and validate it through a randomized controlled clinical trial.

Study Type

Interventional

Enrollment (Estimated)

716

Phase

  • Not Applicable

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Hubei
      • Wuhan, Hubei, China, 430030
        • Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
        • Contact:

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Imaging examinations (MRI, CT, B-ultrasound) show a solid mass in the pancreas, which requires endoscopic ultrasound guided-fine needle aspiration/biopsy (EUS-FNA/B) to clarify the nature of the lesion in patients.
  • Written consent provided

Exclusion Criteria:

  • Age under 18 years old

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Primary Purpose: Diagnostic
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: Double

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
No Intervention: Conventional diagnosis
Endoscopists diagnose pancreatic solid lesions according to endoscopic ultrasound images and clinical data.
Experimental: Joint-AI assisted diagnosis
Endoscopists diagnose pancreatic solid lesions based on endoscopic ultrasound images, clinical data, and predictions made by the Joint-AI model.
Diagnostic test: The assistance of the Joint-AI model
Predictions given by the Joint-AI model will be provided to the endoscopists during their diagnosis
Experimental: Interpretable Joint-AI assisted diagnosis
Endoscopists diagnose pancreatic solid lesions based on endoscopic ultrasound images, clinical data, predictions given by the Joint-AI, and interpretability analysis results used to improve the transparency of the decision-making process of the Joint-AI model.
Diagnostic test: The assistance of the interpretable Joint-AI model
Predictions given by the Joint-AI model and the results of the interpretability analysis will be provided to the endoscopists during their diagnosis

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Rate of correct diagnostic classification with assistance of the Joint-AI Model
Time Frame: Through study completion, an average of 1 year
The rate of correct diagnoses in discriminating pancreatic cancer from other non-cancer lesions, determined by comparing endoscopist diagnosis assisted by the Joint-AI model against the final histopathological diagnosis (reference standard).
Through study completion, an average of 1 year
Rate of correct diagnostic classification with assistance of the Interpretable Joint-AI Model
Time Frame: Through study completion, an average of 1 year
The rate of correct diagnoses in discriminating pancreatic cancer from other non-cancer lesions, determined by comparing endoscopist assessments assisted by the Interpretable Joint-AI model against the final histopathological diagnosis (reference standard)
Through study completion, an average of 1 year

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Rate of correct diagnostic classification of the Joint-AI model and the interpretable Joint-AI model
Time Frame: Through study completion, an average of 1 year
Diagnostic accuracy of the AI models in this prospectively collected dataset.
Through study completion, an average of 1 year
Endoscopist-reported confidence score in diagnosis with AI assistance (the score is on a scale of 0%-100%, where 0 represents "not confident at all" and 100 represents "completely confident")
Time Frame: Through study completion, an average of 1 year
Endoscopist-reported confidence in diagnosis will be measured on a scale ranging from 0 to 100, where 0 represents "not confident at all" and 100 represents "completely confident." Higher scores indicate greater diagnostic confidence. The confidence scores will be assessed separately for diagnoses made using the Joint-AI model and the interpretable Joint-AI model.
Through study completion, an average of 1 year
Rate of correct diagnostic classification of endoscopists without AI assistance
Time Frame: Through study completion, an average of 1 year
Through study completion, an average of 1 year

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Huazhong University of Science and Technology

Collaborators

Beijing Friendship Hospital
Qilu Hospital of Shandong University
Sir Run Run Shaw Hospital
Shanghai Longhua Hospital
Beijing Union Hosptial
Affiliated Drum Tower Hospital of Nanjing University Medical School

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

January 1, 2025

Primary Completion (Estimated)

January 1, 2026

Study Completion (Estimated)

January 1, 2026

Study Registration Dates

First Submitted

December 17, 2024

First Submitted That Met QC Criteria

December 22, 2024

First Posted (Actual)

March 25, 2025

Study Record Updates

Last Update Posted (Actual)

March 25, 2025

Last Update Submitted That Met QC Criteria

December 22, 2024

Last Verified

December 1, 2024

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • Joint-AI 2024

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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