Development of A Deep Learning Method to Identify Acute Ischemic Stroke Lesions on Brain CT

Alessandro Fontanella (Lead / Corresponding author), Wenwen Li, Grant Mair, Antreas Antoniou, Eleanor Platt, Paul Armitage, Emanuele Trucco, Joanna M. Wardlaw, Amos Storkey

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Abstract

Background: Computed Tomography (CT) is commonly used to image patients with ischemic 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 datasets. We aimed to develop a deep learning (DL) method using CT brain scans that were labelled but not annotated for the presence of ischemic lesions.

Methods: We designed a convolutional neural network-based DL algorithm to detect ischemic lesions on CT. Our alogirithm was trained using routinely-aquired 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 the impact of ischemic lesion features, background brain appearances, and timing of CT (baseline or 24-48 hour follow-up) on DL performance.

Results: From 5772 CT scans of 2347 patients (median age 82), 54% had visible ischemic lesions according to experts. Our DL method achieved 72% accuracy for detecting ischemic lesions. Detection was better for larger (80% accuracy) or multiple (87% accuracy for two, 100% for three or more) lesions, and with follow-up scans (76% accuracy versus 67% at baseline). Chronic brain conditions reduced accuracy, particularly non-stroke lesions and old stroke lesions (32% and 31% error rates respectively).

Conclusion: DL methods can be designed for ischemic lesion detection on CT using the vast quantities of routinely-collected brain scans without the need for lesion annotation. Ultimately, this should lead to more robust and widely applicable methods.
Original languageEnglish
Number of pages9
JournalStroke and Vascular Neurology
Early online date25 Dec 2024
DOIs
Publication statusE-pub ahead of print - 25 Dec 2024

Keywords

  • Brian
  • CT
  • Stroke

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