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| Name | Class |
|---|---|
| Beijing Union Hosptial | UNKNOWN |
| Affiliated Drum Tower Hospital of Nanjing University Medical School | UNKNOWN |
| Shanghai Longhua Hospital | UNKNOWN |
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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:
Participants will:
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Conventional diagnosis | No Intervention | Endoscopists diagnose pancreatic solid lesions according to endoscopic ultrasound images and clinical data. | |
| Joint-AI assisted diagnosis | Experimental | Endoscopists diagnose pancreatic solid lesions based on endoscopic ultrasound images, clinical data, and predictions made by the Joint-AI model. |
|
| Interpretable Joint-AI assisted diagnosis | Experimental | 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. |
|
| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| The assistance of the Joint-AI model | Diagnostic Test | Predictions given by the Joint-AI model will be provided to the endoscopists during their diagnosis |
|
| Measure | Description | Time Frame |
|---|---|---|
| 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). | 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) | Through study completion, an average of 1 year |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | 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") |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Bin Cheng | Contact | 86-13986097542 | b.cheng@tjh.tjmu.edu.cn |
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology | Wuhan | Hubei | 430030 | China |
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| Beijing Friendship Hospital |
| OTHER |
| Qilu Hospital of Shandong University | OTHER |
| Sir Run Run Shaw Hospital | OTHER |
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During the endoscopic ultrasound procedure, the allocation of participants will be masked to the endoscopists
| The assistance of the interpretable Joint-AI model | Diagnostic Test | Predictions given by the Joint-AI model and the results of the interpretability analysis will be provided to the endoscopists during their diagnosis |
|
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. |
| Through study completion, an average of 1 year |
| Rate of correct diagnostic classification of endoscopists without AI assistance | Through study completion, an average of 1 year |
| ID | Term |
|---|---|
| D010190 | Pancreatic Neoplasms |
| D010195 | Pancreatitis |
| D000081012 | Autoimmune Pancreatitis |
| ID | Term |
|---|---|
| D004067 | Digestive System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D004701 | Endocrine Gland Neoplasms |
| D004066 | Digestive System Diseases |
| D010182 | Pancreatic Diseases |
| D004700 | Endocrine System Diseases |
| D050500 | Pancreatitis, Chronic |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |
| D002908 | Chronic Disease |
| D020969 | Disease Attributes |
| D010335 | Pathologic Processes |
| D013568 | Pathological Conditions, Signs and Symptoms |
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