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.

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.

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

Adult patients (≥18 years old) undergoing surgical drainage for their first perianal abscess at a university or state hospital. Participants must not have a history of anal fistula, Crohn's disease, or be under immunosuppressive therapy. Follow-up duration is 6 months after drainage.

De-identified MR images and anonymized patient-level data will be available for collaborative analysis upon request.