Autoimmune Pancreatitis, Pancreatic Cancer, Pancreatic Neuroendocine Neoplasms (pNETs), Pancreatitis, Solid Pseudopapillary Neoplasm of the Pancreas
Conditions
Keywords
pancreatic cancer, artificial intelligence, endoscopic ultrasound
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.
Interventions
DIAGNOSTIC_TESTThe assistance of the Joint-AI model
Predictions given by the Joint-AI model will be provided to the endoscopists during their diagnosis
DIAGNOSTIC_TESTThe assistance of the interpretable Joint-AI model
Predictions given by the Joint-AI model and the results of the interpretability analysis will be provided to the endoscopists during their diagnosis
Sponsors
Beijing Union Hosptial
Affiliated Drum Tower Hospital of Nanjing University Medical School
Shanghai Longhua Hospital
Beijing Friendship Hospital
Qilu Hospital of Shandong University
Sir Run Run Shaw Hospital
Huazhong University of Science and Technology
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
DIAGNOSTIC
Masking
DOUBLE (Subject, Outcomes Assessor)
Masking description
During the endoscopic ultrasound procedure, the allocation of participants will be masked to the endoscopists
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.
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
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
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| 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
| Measure | Time frame | Description |
|---|---|---|
| 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 | — |
Countries
China
Contacts
Primary ContactBin Cheng
Outcome results
None listed