Efficiency of a deep learning-based artificial intelligence diagnostic system in spontaneous intracerebral hemorrhage volume measurement.

ABSTRACT:

Background

Accurate measurement of hemorrhage volume is critical for both the prediction of prognosis and the selection of appropriate clinical treatment after spontaneous intracerebral hemorrhage (ICH). This study aimed to evaluate the performance and accuracy of a deep learning-based automated segmentation algorithm in segmenting spontaneous intracerebral hemorrhage (ICH) volume either with or without intraventricular hemorrhage (IVH) extension. We compared this automated pipeline with two manual segmentation techniques.

Methods

We retrospectively reviewed 105 patients with acute spontaneous ICH. Depending on the presence of IVH extension, patients were divided into two groups: ICH without (n = 56) and with IVH (n = 49). ICH volume of the two groups were segmented and meas

SUBMITTER: Wang T 

PROVIDER: S-EPMC8364089 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature