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Abstract #1884

Hyperpolarized Ventilation MRI and Ensemble Machine Learning Predict Airflow Limitation Worsening in Ex-smokers

Cathy Ong-Ly1,2, Andrew Westcott1,2, Inderdeep Dhaliwal3, Aaron Fenster1,2, Miranda Kirby4, and Grace Parraga1,2,3

1Robarts Research Institute, London, ON, Canada, 2Medical Biophysics, Western University, London, ON, Canada, 3Division of Respirology, Department of Medicine, Western University, London, ON, Canada, 4Physics, Ryerson University, Toronto, ON, Canada

Hyperpolarized noble-gas pulmonary imaging provides a way to measure ventilation and perfusion in patients. The potential of highly sensitive MRI biomarkers of lung function has not yet been exploited using machine-learning. Ensemble machine-learning merges diverse classifiers to improve classification accuracy and reduce the potential for misclassification. Our aim was to evaluate the performance of ensemble machine-learning algorithms and hyperpolarized gas MRI features for predicting worsening airflow measured using spirometry. This proof-of-concept study revealed that MRI ventilation combined with ensemble machine-learning predicted small changes in airflow limitation (∆FEV1%pred=5%) over relatively short time period (2.5 yr) in ex-smokers with and without COPD.

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