Deep Learning-based Artificial Intelligence for the Diagnosis of Small Bowel Obstruction
NCT ID: NCT06481358
Last Updated: 2024-07-01
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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ACTIVE_NOT_RECRUITING
17 participants
OBSERVATIONAL
2022-09-01
2024-10-31
Brief Summary
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Detailed Description
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PARTICIPANTS: Residents and surgeons participated in the study. INTERVENTIONS: Residents and surgeons were divided into two groups: one group read using the AI technology, and the other group read without the AI technology.
MAIN OUTCOMES AND MEASURES: Participants indicated whether or not small bowel obstruction and obstruction location. The time for diagnosis was also collected. We applied a hierarchical Bayesian model.
Conditions
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Study Design
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OTHER
OTHER
Study Groups
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AI group
Participants read CT images with AI.
Artificial intelligence
AI extract intestinal region and reconstruct into 3D image.
Manual group
Participants read CT images without AI
No interventions assigned to this group
Interventions
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Artificial intelligence
AI extract intestinal region and reconstruct into 3D image.
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
ALL
Yes
Sponsors
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Nagoya University
OTHER
Responsible Party
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Aitaro Takimoto
Medical Staff
Principal Investigators
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Hieoo Uchida, PhD.
Role: STUDY_CHAIR
Nagoya University Graduate School of Medicine, Pediatric Surgery
Locations
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Nagoya University Graduate School of Medicine
Nagoya, Aichi-ken, Japan
Countries
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Other Identifiers
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2022-0188
Identifier Type: -
Identifier Source: org_study_id
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