Validation of Joint-AI in Diagnosing Pancreatic Solid Lesions
NCT ID: NCT06753318
Last Updated: 2024-12-31
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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NOT_YET_RECRUITING
NA
716 participants
INTERVENTIONAL
2025-01-31
2026-01-31
Brief Summary
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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
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Conditions
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Keywords
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Study Design
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RANDOMIZED
PARALLEL
2. For participants within the Joint-AI assistance group and Interpretable Joint-AI assistance group, their groups will be switched after a washout period.
DIAGNOSTIC
DOUBLE
Study Groups
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Conventional diagnosis
Endoscopists diagnose pancreatic solid lesions according to endoscopic ultrasound images and clinical data.
No interventions assigned to this group
Joint-AI assisted diagnosis
Endoscopists diagnose pancreatic solid lesions based on endoscopic ultrasound images, clinical data, and predictions made by the Joint-AI model.
The assistance of the Joint-AI model
Predictions given by the Joint-AI model will be provided to the endoscopists during their diagnosis
Interpretable Joint-AI assisted diagnosis
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.
The 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
Interventions
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The assistance of the Joint-AI model
Predictions given by the Joint-AI model will be provided to the endoscopists during their diagnosis
The 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
Eligibility Criteria
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Inclusion Criteria
* Written consent provided
Exclusion Criteria
18 Years
ALL
No
Sponsors
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Beijing Union Hosptial
UNKNOWN
Affiliated Drum Tower Hospital of Nanjing University Medical School
UNKNOWN
Shanghai Longhua Hospital
UNKNOWN
Beijing Friendship Hospital
OTHER
Qilu Hospital of Shandong University
OTHER
Sir Run Run Shaw Hospital
OTHER
Huazhong University of Science and Technology
OTHER
Responsible Party
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Bin Cheng
Professor
Locations
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Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology
Wuhan, Hubei, China
Countries
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Central Contacts
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Facility Contacts
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Bin Cheng
Role: primary
Other Identifiers
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Joint-AI 2024
Identifier Type: -
Identifier Source: org_study_id