Disease Classification of 129Xe Ventilation MRI using Artificial Intelligence

Motivation: Xenon ventilation MRI shows distinct defect patterns that appear disease-specific but are difficult to measure. Deep learning, via neural networks, can generate texture features to classify images. Image classification has applications in diagnostics, phenotyping and predicting outcomes.Goal(s): To determine if neural networks could determine disease classification from xenon MRI.Approach: 2D neural networks were trained on data from eight disease states (including healthy controls) and assessed on top-1, top-3 accuracy and recall.Results: The top performing network had a 54% top-1 and 86% top-3 accuracy.Impact: Artificial intelligence can classify disease from xenon MRI alone with moderate accuracy and differentiate between similar conditions. In the future, deep learning could be used diagnostically, for phenotyping disease subgroups and predicting outcomes.