Clinical trial NCT06753318: Validation of Joint-AI in Diagnosing Pancreatic Solid Lesions
Official title: Validation of a Multimodal Artificial Intelligence Model in in Diagnosing Pancreatic Solid Lesions: a Prospective, Multicenter, Randomized, Controlled Trial
Study type: INTERVENTIONAL
Overall status: NOT_YET_RECRUITING
Phase: NA
Conditions: Pancreatic Cancer
Pancreatitis
Pancreatic Neuroendocine Neoplasms (pNETs)
Autoimmune Pancreatitis
Solid Pseudopapillary Neoplasm of the Pancreas
Keywords: pancreatic cancer
artificial intelligence
endoscopic ultrasound
Lead sponsor: Huazhong University of Science and Technology
Sponsor class: OTHER
Collaborators: name: Beijing Union Hosptial
name: Affiliated Drum Tower Hospital of Nanjing University Medical School
name: Shanghai Longhua Hospital
name: Beijing Friendship Hospital
name: Qilu Hospital of Shandong University
name: Sir Run Run Shaw Hospital
First posted: 2024-12-31
Enrollment: 716
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Intervention model description: 1. First, participants are randomized into three parallel groups: conventional diagnosis group, Joint-AI assistance group, and Interpretable Joint-AI assistance group.
2. For participants within the Joint-AI assistance group and Interpretable Joint-AI assistance group, their groups will be switched after a washout period.
Primary purpose: DIAGNOSTIC

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.

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

Primary outcomes:
measure: Rate of correct diagnostic classification with assistance of the Joint-AI Model
timeFrame: Through study completion, an average of 1 year
measure: Rate of correct diagnostic classification with assistance of the Interpretable Joint-AI Model
timeFrame: Through study completion, an average of 1 year
Secondary outcomes:
measure: Rate of correct diagnostic classification of the Joint-AI model and the interpretable Joint-AI model
timeFrame: Through study completion, an average of 1 year
measure: 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")
timeFrame: Through study completion, an average of 1 year
measure: Rate of correct diagnostic classification of endoscopists without AI assistance
timeFrame: Through study completion, an average of 1 year
Other outcomes:
Locations:
city: Wuhan
country: China
facility: Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
state: Hubei
Study officials:
References: