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• W4327601374 abstract "Earlier detection of aortic calcification can facilitate subsequent cardiovascular care planning. Opportunistic screening based on plain chest radiography is potentially feasible in various population. We used multiple deep convolutional neural network (CNN) transfer learning by fine-tuning pre-trained models followed by ensemble technique for aortic arch calcification on chest radiographs from a derivation and two external databases with distinct features. Our ensemble approach achieved 84.12% precision, 84.70% recall, and an area under the receiver-operating-characteristic curve (AUC) of 0.85 in the general population/older adult's dataset. We also obtained 87.5% precision, 85.56% recall, and an AUC of 0.86 in the pre-end-stage kidney disease (pre-ESKD) cohort. We identified discriminative regions for identifying aortic arch calcification between patients without and with pre-ESKD. These findings are expected to optimize cardiovascular risk prediction if our model is incorporated into the process of routine care." @default.
• W4327601374 created "2023-03-17" @default.
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• W4327601374 date "2023-04-01" @default.
• W4327601374 modified "2023-10-18" @default.
• W4327601374 title "Chest radiography deep radiomics-enabled aortic arch calcification interpretation across different populations" @default.
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• W4327601374 doi "https://doi.org/10.1016/j.isci.2023.106429" @default.
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