Machine learning models for predicting transstenotic pressure gradient based on computed tomography angiography features.

ABSTRACT:

Background

The transstenotic pressure gradient (TPG) is closely associated with the pathogenesis, diagnosis, and treatment strategies of conditions such as idiopathic intracranial hypertension and pulsatile tinnitus. Currently, TPG assessment relies on invasive, complex, and costly digital subtraction angiography (DSA), and an efficient, noninvasive evaluation method remains lacking. We thus aimed to develop a machine learning model to predict the transverse sinus TPG using features derived from computed tomography angiography (CTA).

Methods

We included 139 patients who underwent DSA for transverse sinus TPG measurement. Six feature indicators were extracted from their CTA data. Using TPG as the dependent variable, we applied six machine learning algorithms-logistic regressi

SUBMITTER: Huang Y 

PROVIDER: S-EPMC12514606 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature