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.
Study Type
INTERVENTIONAL
Allocation
RANDOMIZED
Purpose
DIAGNOSTIC
Masking
DOUBLE
Enrollment
716
Predictions given by the Joint-AI model will be provided to the endoscopists during their diagnosis
Predictions given by the Joint-AI model and the results of the interpretability analysis will be provided to the endoscopists during their diagnosis
Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, China
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).
Time frame: 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)
Time frame: Through study completion, an average of 1 year
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.
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")
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.
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
This platform is for informational purposes only and does not constitute medical advice. Always consult a qualified healthcare professional.