Machine Learning-Based Risk Stratification for Fistula Formation After Perianal Abscess Drainage
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Perianal Abscess, Anal Fistula. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
Unsure about the terms? Read our patient guide →
Official title
A Prospective Cohort Study for Machine Learning-Based Prediction of Anal Fistula Formation After Perianal Abscess Drainage Based on Drainage Setting, Provider Experience, and MRI Interpretation (PRISM)
Overview
This prospective cohort study investigates the influence of provider experience and drainage location on fistula formation within 6 months following perianal abscess drainage. Additionally, the study explores the role of artificial intelligence (AI)-based interpretation of magnetic resonance (MR) images in early identification of fistula development.
Detailed description
Perianal abscess drainage is a common surgical procedure. However, subsequent fistula formation remains a significant complication. This study aims to determine whether the procedure setting (operating room, emergency department, or outpatient clinic) and the experience level of the performing clinician affect fistula development rates.
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.
Primary outcome measures
- Fistula formation within 6 months [Time frame: 6 months]
Secondary outcome measures (3)
- Correlation between drainage location and fistula rate [Time frame: 6 months]
- Correlation between provider experience and fistula complexity [Time frame: 6 months]
- Diagnostic accuracy of AI-based MR analysis vs radiologist [Time frame: 6 months]
Eligibility criteria
Inclusion criteria
- Age ≥ 18
- First-time perianal abscess
- Surgical drainage performed
Exclusion criteria
- Existing anal fistula history
- Crohn's disease
- Immunosuppressive treatment
- Incomplete 6-month follow-up
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT07019532 · FISTUL-ML-01