Validation of Joint-AI in Diagnosing Pancreatic Solid Lesions

Huazhong University of Science and Technology

Status: Not yet enrolling

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

Treatments: Diagnostic Test: The assistance of the interpretable Joint-AI model; Diagnostic Test: The assistance of the Joint-AI model

Study type: Interventional

Funder types: Other

Identifiers

NCT06753318

Joint-AI 2024

Details and patient eligibility

About

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.

Full 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.

Enrollment: 716 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

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

Trial design

Primary purpose: Diagnostic

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Double Blind

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716 participants in 3 patient groups

Conventional diagnosis

No Intervention group

Description:

Endoscopists diagnose pancreatic solid lesions according to endoscopic ultrasound images and clinical data.

Joint-AI assisted diagnosis

Experimental group

Description:

Endoscopists diagnose pancreatic solid lesions based on endoscopic ultrasound images, clinical data, and predictions made by the Joint-AI model.

Treatment:

Diagnostic Test: The assistance of the Joint-AI model

Interpretable Joint-AI assisted diagnosis

Experimental group

Description:

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.

Treatment:

Diagnostic Test: The assistance of the interpretable Joint-AI model