Validation Of Joint-AI In Diagnosing Pancreatic Solid Lesions

Study Overview

Validation of Joint-AI in Diagnosing Pancreatic Solid Lesions

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.

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.

Pancreatic Cancer
Pancreatitis
Pancreatic Neuroendocine Neoplasms (pNETs)
Autoimmune Pancreatitis
Solid Pseudopapillary Neoplasm of the Pancreas

DIAGNOSTIC_TEST: The assistance of the Joint-AI model
DIAGNOSTIC_TEST: The assistance of the interpretable Joint-AI model

Joint-AI 2024

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 Registration Dates	Results Reporting Dates	Study Record Updates
First Submitted 2024-12-17	Results First Submitted N/A	Last Update Submitted that met QC Criteria 2024-12-22
First Submitted that Met QC Criteria 2024-12-22	Results First Submitted that Met QC Criteria N/A	Last Update Posted (ACTUAL) 2024-12-31
First Posted (ACTUAL) 2024-12-31	Results First Posted N/A	Last Verified 2024-12

Study Plan

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

Design Details

Primary Purpose:
Diagnostic

Allocation:
Randomized

Interventional Model:
Parallel

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 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
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 m	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

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

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

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 m

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

Primary Outcome Measures	Measure Description	Time Frame
Rate of correct diagnostic classification with assistance of the Joint-AI Model	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	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	Measure Description	Time Frame
Rate of correct diagnostic classification of the Joint-AI model and the interpretable Joint-AI model	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")	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		Through study completion, an average of 1 year

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

Name: Bin Cheng

Phone Number: 86-13986097542

Email: b.cheng@tjh.tjmu.edu.cn

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.

Ages Eligible for Study:
ALL

Sexes Eligible for Study:
18 Years

Accepts Healthy Volunteers:

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

Age under 18 years old

Collaborators and Investigators

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

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

Investigators

Publications

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

No publications available

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Resources

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Caregivers

Clinical Trial Record