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NCT Number: NCT07019532

Machine Learning-Based Risk Stratification for Fistula Formation After Perianal Abscess Drainage

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.

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Key information

Conditions

Perianal Abscess Anal Fistula Digestive System Diseases Digestive System Fistula Fistula Gastrointestinal Diseases Intestinal Diseases Intestinal Fistula Pathological Conditions, Anatomical Pathological Conditions, Signs and Symptoms Rectal Diseases Rectal Fistula

Age range

18 year and older

Sex eligibility

All sexes

Study type

Observational

About this study

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.

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

Primary outcomes

  1. Fistula formation within 6 months

    Time frame: 6 months

    Confirmed by clinical exam, surgical findings, or MR imaging

Secondary outcomes

  1. Correlation between drainage location and fistula rate

    Time frame: 6 months

  2. Correlation between provider experience and fistula complexity

    Time frame: 6 months

  3. Diagnostic accuracy of AI-based MR analysis vs radiologist

    Time frame: 6 months

Study contacts

Contact information is provided by the study sponsor or research team.

Kayahan Eyüboğlu, MD

CONTACT

[email protected]

+905546813327

Sponsors and collaborators

Lead sponsor

Gumushane State Hospital

Other Gov

Registry information

Official study 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)

Acronym: PRISM

Important dates

Study start
2025
Primary completion
2025
Study completion
2026
First posted
Jun 13, 2025
Registry last updated
Jun 13, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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