Research Area: Machine Learning
Treatment options for patients with acute ischemic stroke depend on the volume of salvageable tissue. This volume assessment is currently based on fixed thresholds and single imagine modalities, limiting accuracy. We wish to develop and validate a predictive model capable of automatically identifying and combining acute imaging features to accurately predict final lesion volume.Using acute magnetic resonance imaging, we developed and trained a deep convolutional neural network (CNNdeep) to predict final imaging outcome. A total of 222 patients were included, of which 187 were treated with rtPA (recombinant tissue-type plasminogen activator). The performance of CNNdeep was compared with a shallow CNN based on the perfusion-weighted imaging biomarker Tmax (CNNTmax), a shallow CNN based on a combination of 9 different biomarkers (CNNshallow), a generalized linear model, and thresholding of the diffusion-weighted imaging biomarker apparent diffusion coefficient (ADC) at 600×10−6 mm2/s (ADCthres). To assess whether CNNdeep is capable of differentiating outcomes of ±intravenous rtPA, patients not receiving intravenous rtPA were included to train CNNdeep,−rtpa to access a treatment effect. The networks performances were evaluated using visual inspection, area under the receiver operating characteristic curve (AUC), and contrast.
Keywords:
Biomarkers
Stroke
Follow-up studies
Area under curve
Humans
Magnetic resonance imaging
Author(s) Name: Anne Nielsen, Mikkel Bo Hansen, Anna Tietze and Kim Mouridsen
Journal name: Stroke
Conferrence name:
Publisher name: American Heart Association
DOI: 10.1161/STROKEAHA.117.019740
Volume Information: Volume 49
