Towards Improving Ischemic Stroke Functional Outcome Prediction with Computed Tomography Brain Scans Using Deep Learning

Stroke is the second leading cause of death and disability, of all the non transmissible diseases, in the world. Quick diagnosis and prognosis is of paramount importance given the rapid degradation of the affected brain and short time frame available for the recommended treatments. The collection of computed tomography brain scans is part of the standard patient care. However, their examination is manually done by experts. Also, despite containing strong patient functional outcome predictor features, these are rarely considered by the currently used clinical models that mostly only use demographic and clinical patient variables. This work explores three different approaches to improve on these models, obtaining results comparable to the state of the art in their respective categories. In the tabular approach, machine learning classifiers use the same type of variables used by the clinical models to predict the functional outcome. In the imaging approach, the outcome is directly predicted solely from the patient's brain scans, using deep artificial neural networks. Here several architectures never before tried in this task are explored, including multiple instance learning models and Siamese networks that leverage a useful brain hemisphere symmetry bias. Finally, in the hybrid approach, both important clinical features and imaging information are leveraged and combined in a simpler and more interpretable manner than that of existing models.