Ignite Creation Date:
2025-12-24 @ 9:26 PM
Ignite Modification Date:
2026-01-01 @ 8:03 PM
Study NCT ID:
NCT07019532
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-06-13
First Post:
2025-06-05
Is NOT Gene Therapy:
True
Has Adverse Events:
False
Brief Title:
Machine Learning-Based Risk Stratification for Fistula Formation After Perianal Abscess Drainage
Sponsor:
Gumushane State Hospital
Organization:
Study Overview
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)
Status:
NOT_YET_RECRUITING
Status Verified Date:
2025-06
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
False
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
PRISM
Brief 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.
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.
Study Oversight
Has Oversight DMC:
True
Is a FDA Regulated Drug?:
False
Is a FDA Regulated Device?:
False
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?: