Machine Learning-Based Risk Stratification for Fistula Formation After Perianal Abscess Drainage
NCT ID: NCT07019532
Last Updated: 2025-06-13
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
450 participants
OBSERVATIONAL
2025-07-01
2026-06-01
Brief Summary
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Detailed Description
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Furthermore, the study evaluates the use of AI-assisted analysis of selected MR images to identify early signs of fistula formation. Selected image slices will be labeled based on radiological reports, and a machine learning model will be trained to predict fistula risk. The study will also compare AI-generated interpretations with expert radiologist assessments to validate performance.
The ultimate goal is to create a risk stratification tool to support clinical decision-making in surgical management of perianal abscesses.
Conditions
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Study Design
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COHORT
PROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
* First-time perianal abscess
* Surgical drainage performed
Exclusion Criteria
* Crohn's disease
* Immunosuppressive treatment
* Incomplete 6-month follow-up
18 Years
ALL
No
Sponsors
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Gumushane State Hospital
OTHER_GOV
Responsible Party
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Kayahan Eyüboğlu
General Surgery Specialist
Central Contacts
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Other Identifiers
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FISTUL-ML-01
Identifier Type: -
Identifier Source: org_study_id
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