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.
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
Age under 18 years old
Parallel
The assistance of the Joint-AI model
Diagnostic testPredictions given by the Joint-AI model will be provided to the endoscopists during their diagnosis
The assistance of the interpretable Joint-AI model
Diagnostic testPredictions given by the Joint-AI model and the results of the interpretability analysis will be provided to the endoscopists during their diagnosis
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).
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)
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.
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.
Rate of correct diagnostic classification of endoscopists without AI assistance
Click on the lead sponsor to view all of their trials.
Huazhong University of Science and Technology
Lead sponsor
Beijing Union Hosptial
Collaborator
Affiliated Drum Tower Hospital of Nanjing University Medical School
Collaborator
Shanghai Longhua Hospital
Collaborator
Beijing Friendship Hospital
Collaborator
Qilu Hospital of Shandong University
Collaborator
Sir Run Run Shaw Hospital
Collaborator
This trial is not recruiting at the moment. You can still explore other options:
