Early identification and diagnosis of fournier gangrene: a machine learning approach integrating serological characterization.

ABSTRACT:

Background

Fournier Gangrene (FG) and Generalized Perianal Abscess (GPA) have similar clinical features. But FG has a high mortality and disability rate and needs to be identified and treated as early as possible. This study utilized machine learning methods to integrate clinical and metabolic features to promote early diagnosis of FG.

Methods

Serological characteristics were screened for patients with FG (n = 20) and GPA (n = 16). The metabolomic changes of FG were described based on untargeted metabolomics. We used machine learning tools to combine demographic data, clinical serology, and metabolomics data to establish disease-specific boundary points.

Results

There were significant differences in the serum metabolic profiles between the FG and GPA groups. 118 diffe

SUBMITTER: Zhang J 

PROVIDER: S-EPMC12482003 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature