Meeting Banner
Abstract #4794

A pilot study on the application of explainable deep learning to ADC maps for predicting functional outcome of ischemic stroke patients

Esra Zihni1, Bryony L. McGarry1,2, Jen Guo3, Rani Gupta Sah3, George Tadros3, Philip A. Barber3, and John D. Kelleher1,4
1PRECISE4Q Predictive Modelling in Stroke, Technological University Dublin, Dublin, Ireland, 2School of Psychological Science, University of Bristol, Bristol, United Kingdom, 3Calgary Stroke Program, Department of Clinical Neurosciences, University of Calgary, Calgary, AB, Canada, 4ADAPT Research Centre, ICE Research Institute, Technological University Dublin, Dublin, Ireland


Applying deep learning models to MRI scans of acute stroke patients to extract features indicative of functional outcome could assist a clinician’s treatment decisions. Here, we trained convolutional neural network models on ADC maps from hyper-acute ischemic stroke patients to predict 3-month mRS and used an interpretability technique to highlight regions in the ADC maps that were most important in the prediction of good and poor outcomes. Although the models had poor predictive power, the visual explanations supported our previous findings that predictions might be based not on ischemic regions, but on other relevant information inherent in the image.

This abstract and the presentation materials are available to members only; a login is required.

Join Here