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The authors of this study aimed to determine the efficacy of a convolutional neural network (CNN) in final infarct volume prediction from computed tomography angiography (CTA), subsequently comparing the results to a CT perfusion (CTP)-based commercially available software. The stroke cases treated with thrombolytic therapy or receiving supportive care were retrospectively selected by the authors. The study found that a CTA-based CNN software can provide good infarct core volume estimates as observed in follow-up imaging studies.

Key points

  • A computed tomography angiography (CTA)-based convolutional neural network (CNN) can predict infarct volume in anterior circulation ischaemic stroke.
  • A CTA-based CNN estimates of ischaemic lesion volumes correlated well with infarct volumes measured from follow-up computed tomography images.
  • Our method had a good correlation with computed tomography perfusion-RAPID estimated infarct core volumes.

Article: Evaluation of a CTA-based convolutional neural network for infarct volume prediction in anterior cerebral circulation ischaemic stroke

Authors: Lasse Hokkinen, Teemu Mäkelä, Sauli Savolainen & Marko Kangasniemi

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This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.