Development of a deep learning method to identify acute ischaemic stroke lesions on brain CT.

ABSTRACT:

Background

CT is commonly used to image patients with ischaemic stroke but radiologist interpretation may be delayed. Machine learning techniques can provide rapid automated CT assessment but are usually developed from annotated images which necessarily limits the size and representation of development data sets. We aimed to develop a deep learning (DL) method using CT brain scans that were labelled but not annotated for the presence of ischaemic lesions.

Methods

We designed a convolutional neural network-based DL algorithm to detect ischaemic lesions on CT. Our algorithm was trained using routinely acquired CT brain scans collected for a large multicentre international trial. These scans had previously been labelled by experts for acute and chronic appearances. We explored

SUBMITTER: Fontanella A 

PROVIDER: S-EPMC12415648 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature