A Machine Learning
Approach for Classifying Ischemic Stroke Onset Time From Imaging
Abstract:
Current clinical practice relies on clinical
history to determine the time since stroke (TSS) onset. Imaging-based
determination of acute stroke onset time could provide critical information to
clinicians in deciding stroke treatment options, such as thrombolysis. The
patients with unknown or unwitnessed TSS are usually excluded from
thrombolysis, even if their symptoms began within the therapeutic window. In
this paper, we demonstrate a machine learning approach for TSS classification
using routinely acquired imaging sequences. We develop imaging features from
the magnetic resonance (MR) images and train machine learning models to
classify the TSS. We also propose a deep-learning model to extract hidden
representations for the MR perfusion-weighted images and demonstrate
classification improvement by incorporating these additional deep features. The
cross-validation results show that our best classifier achieved an area under
the curve of 0.765, with a sensitivity of 0.788 and a negative predictive value
of 0.609, outperforming existing methods. We show that the features generated
by our deep-learning algorithm correlate with the MR imaging features, and
validate the robustness of the model on imaging parameter variations (e.g.,
year of imaging). This paper advances magnetic resonance imaging analysis
one-step-closer to an operational decision support tool for stroke treatment
guidance.
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