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Not yet recruiting NCT06753318

Validation of Joint-AI in Diagnosing Pancreatic Solid Lesions

No phase Interventional Pancreatic Cancer Pancreatitis Pancreatic Neuroendocine Neoplasms (pNETs) Autoimmune Pancreatitis

For patients and families

In plain language

An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.

What is being studied
The protocol lists: The assistance of the Joint-AI model, The assistance of the interpretable Joint-AI model.
Who it may be relevant to
Registry conditions: Pancreatic Cancer, Pancreatitis, Pancreatic Neuroendocine Neoplasms (pNETs), Autoimmune Pancreatitis. Basic parameters: from 18 years · All.
What needs checking
Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
Where it takes place
China
Next step
Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →

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Official title

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

Overview

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.

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.

Interventions

  • 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

Primary outcome measures

  • Rate of correct diagnostic classification with assistance of the Joint-AI Model [Time frame: 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]
Secondary outcome measures (3)
  • 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]
  • 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]
  • Rate of correct diagnostic classification of endoscopists without AI assistance [Time frame: Through study completion, an average of 1 year]

Eligibility criteria

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

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Allocation
Randomized
Model
Parallel assignment
Masking
Double blind
Primary purpose
Diagnostic

Study locations

China · 1 center
  • Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology — Wuhan

Identifiers

NCT: NCT06753318 · Joint-AI 2024

Primary sources (government registries)

View this study on ClinicalTrials.gov ↗