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Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes

Journal of Cardiovascular Computed Tomography(2022)

Cited 5|Views33
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Abstract
Table of Contents Summary: We aimed to validate a deep learning model to automatically quantify left atrial (LA) volumes from routine non-contrast chest CT and evaluate prediction for cardiovascular outcomes. We evaluated 273 patients (median age 69 years, 55.5% male) who underwent a routine non-ECG gated NCCT for lung cancer screening. LA volumes were quantified by three expert cardiothoracic radiologists and a prototype AI algorithm. There was excellent correlation between AI and expert results. AI-derived LA volumes were associated with increased risk of major adverse cardiac events, heart failure hospitalization, and new-onset atrial fibrillation within five years.
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Key words
Low-dose computed tomography,Artificial intelligence,Deep learning,Left atrium volume,Heart failure,Atrial fibrillation,Major adverse cardiac and cerebrovascular events,Lung cancer screening
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