Validation of Joint-AI in Diagnosing Pancreatic Solid Lesions

Not Applicable

Not yet recruiting

• Conditions: Pancreatic Cancer; Pancreatitis; Pancreatic Neuroendocine Neoplasms (pNETs); Autoimmune Pancreatitis; Solid Pseudopapillary Neoplasm of the Pancreas

• Registration Number: NCT06753318
• Lead Sponsor: Huazhong University of Science and Technology

Brief Summary

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.

Study Timeline

• Status Verified: 2024-12
• Study First Submitted: 2024-12-17
• Study First Submitted QC: 2024-12-22
• Last Update Submitted: 2024-12-22
• Study First Posted: 2024-12-31
• Last Update Posted: 2024-12-31
• Study Start: 2025-01
• Primary Completion: 2026-01
• Study Completion: 2026-01

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 716

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 & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Primary Outcome Measures

Name	Time	Method
Rate of correct diagnostic classification with assistance of the Joint-AI Model	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).
Rate of correct diagnostic classification with assistance of the Interpretable Joint-AI Model	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)

Secondary Outcome Measures

Name	Time	Method
Rate of correct diagnostic classification of the Joint-AI model and the interpretable Joint-AI model	Through study completion, an average of 1 year	Diagnostic accuracy of the AI models in this prospectively collected dataset.
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")	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.
Rate of correct diagnostic classification of endoscopists without AI assistance	Through study completion, an average of 1 year

Trial Locations

Locations (1)

Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology; 🇨🇳 Wuhan, Hubei, China