Name
Abstract | ||
|---|---|---|
Current clinical practice relies on clinical history to determine the time since stroke onset (TSS). Imaging-based determination of acute stroke onset time could provide critical information to clinicians in deciding stroke treatment options such as thrombolysis. Patients with unknown or unwitnessed TSS are usually excluded from thrombolysis, even if their symptoms began within the therapeutic window. In this work, 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 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 MR imaging features, and validate the robustness of the model on imaging parameter variations (e.g., year of imaging). This work advances magnetic resonance imaging (MRI) analysis one step closer to an operational decision support tool for stroke treatment guidance. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TMI.2019.2901445 | IEEE transactions on medical imaging |
Keywords | Field | DocType |
Deep learning,Stroke (medical condition),Feature extraction,Magnetic resonance imaging,Biomedical imaging | Thrombolysis,Medical imaging,Stroke,Feature extraction,Robustness (computer science),Artificial intelligence,Deep learning,Classifier (linguistics),Machine learning,Mathematics,Magnetic resonance imaging | Journal |
Volume | Issue | ISSN |
38 | 7 | 1558-254X |
Citations | PageRank | References |
2 | 0.39 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| King Chung Ho | 1 | 7 | 2.21 |
| William Speier | 2 | 38 | 7.39 |
| Haoyue Zhang | 3 | 2 | 1.40 |
| Fabien Scalzo | 4 | 68 | 15.42 |
| Suzie El-Saden | 5 | 152 | 15.31 |
| Corey W. Arnold | 6 | 9 | 3.56 |