Support Vector Machine-based Spontaneous Intracranial Hypotension Detection on Brain MRI.

ABSTRACT:

Background and purpose

To develop a fully automatic algorithm for the magnetic resonance imaging (MRI) identification of patients with spontaneous intracranial hypotension (SIH).

Material and methods

A support vector machine (SVM) was trained with structured reports of 140 patients with clinically suspected SIH. Venous sinuses and basal cisterns were segmented on contrast-enhanced T1-weighted MPRAGE (Magnetization Prepared-Rapid Gradient Echo) sequences using a convolutional neural network (CNN). For the segmented sinuses and cisterns, 56 radiomic features were extracted, which served as input data for the SVM. The algorithm was validated with an independent cohort of 34 patients with proven cerebrospinal fluid (CSF) leaks and 27 patients who had MPRAGE scans for unrelated r

SUBMITTER: Arnold PG 

PROVIDER: S-EPMC8894221 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature