Development and clinical application of a deep learning model to identify acute infarct on magnetic resonance imaging.

ABSTRACT: Stroke is a leading cause of death and disability. The ability to quickly identify the presence of acute infarct and quantify the volume on magnetic resonance imaging (MRI) has important treatment implications. We developed a machine learning model that used the apparent diffusion coefficient and diffusion weighted imaging series. It was trained on 6,657 MRI studies from Massachusetts General Hospital (MGH; Boston, USA). All studies were labelled positive or negative for infarct (classification annotation) with 377 having the region of interest outlined (segmentation annotation). The different annotation types facilitated training on more studies while not requiring the extensive time to manually segment every study. We initially validated the model on studies sequestered from the training

SUBMITTER: Bridge CP 

PROVIDER: S-EPMC8828773 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature