NCT07019532

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

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)

• Status: Not Yet Recruiting
• Phase: —
• Study type: Observational
• Enrollment: 450 (estimated)
• Sponsor: Gumushane State Hospital · Other Government
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

Summary

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.

Conditions

• Perianal Abscess
• Anal Fistula

Timeline

• Start date: 2025-07-01
• Primary completion: 2025-12-01
• Completion: 2026-06-01
• First posted: 2025-06-13
• Last updated: 2025-06-13