2022
DOI: 10.1007/s00380-022-02043-w
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Quantitative estimation of pulmonary artery wedge pressure from chest radiographs by a regression convolutional neural network

Abstract: Recent studies reported that a convolutional neural network (CNN; a deep learning model) can detect elevated pulmonary artery wedge pressure (PAWP) from chest radiographs, the diagnostic images most commonly used for assessing pulmonary congestion in heart failure. However, no method has been published for quantitatively estimating PAWP from such radiographs. We hypothesized that a regression CNN, an alternative type of deep learning, could be a useful tool for quantitatively estimating PAWP in cardiovascular … Show more

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Cited by 10 publications
(29 citation statements)
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“…In this study, an all-analysis procedure, we used this R-CNN and the same dataset from [6]. We confirmed almost all RAMs generated by the R-CNN contained the cardiac region (see Appendix section) using the k-means clustering approach.…”
Section: Developed R-cnnmentioning
confidence: 52%
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“…In this study, an all-analysis procedure, we used this R-CNN and the same dataset from [6]. We confirmed almost all RAMs generated by the R-CNN contained the cardiac region (see Appendix section) using the k-means clustering approach.…”
Section: Developed R-cnnmentioning
confidence: 52%
“…The size of the RAM was 8 × 8, and was resized to 256 × 256 (input image size). The details of other training conditions are explained in [6].…”
Section: Developed R-cnnmentioning
confidence: 99%
See 3 more Smart Citations