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Abstract #2269

Explainable AI-based classification of asthma and COPD from 129Xe-MRI with regional interpretability

Joshua Russell Astley1,2, Helen Marshall1,2, Laurie J Smith1,2, Alberto M Biancardi1,2, Rod Hughes3, Bilal A Tahir1,2, and Jim M Wild1,2
1POLARIS, University of Sheffield, Sheffield, United Kingdom, 2Insigneo Institute, University of Sheffield, Sheffield, United Kingdom, 3Early Development Respiratory Medicine, AstraZeneca, Cambridge, United Kingdom

Synopsis

Keywords: Lung, Hyperpolarized MR (Gas), Explainable AI

Motivation: Asthma and/or COPD diagnosis is challenging due to overlapping characteristics; hyperpolarized xenon-129 (129Xe)-MRI provides regional ventilation information. Classifying asthma and/or COPD using 129Xe-MRI could potentially inform diagnosis and patient management.

Goal(s): Classify asthma and/or COPD from 129Xe-MRI using explainable AI to gain regional insights.

Approach: 129Xe-MRI from 160 asthma and/or COPD patients were classified using a convolutional neural network. Occlusion sensitivity maps highlighted regional features associated with each diagnosis.

Results: We demonstrate accurate classification of asthma and/or COPD patients. Occlusion sensitivity maps show 129Xe-MRI ventilation regions associated with disease grouping. Percentage COPD likelihood was moderately negatively correlated with TLCO.

Impact: Novel use of xenon-129 (129Xe)-MRI to classify patients with asthma and/or COPD. Explainable AI techniques provide insights into regional patterns of ventilation associated with classifications; large-scale regional patterns that occur in 129Xe-MRI show qualitative differences in asthma and/or COPD phenotypes.

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Keywords