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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 diseases. We retrospectively enrolled 936 patients with cardiovascular diseases who had undergone right heart catheterization (RHC) and chest radiography and estimated PAWP by constructing a regression CNN based on the VGG16 model. We randomly categorized 80% of the data as training data (training group, n = 748) and 20% as test data (test group, n = 188). Moreover, we tuned the learning rate-one of the model parameters-by 5-hold cross-validation of the training group. Correlations between PAWP measured by RHC [ground truth (GT) PAWP] and PAWP derived from the regression CNN (estimated PAWP) were tested. To visualize how the regression CNN assessed the images, we created a regression activation map (RAM), a visualization technique for regression CNN. Estimated PAWP correlated significantly with GT PAWP in both the training (r = 0.76, P < 0.001) and test group (r = 0.62, P < 0.001). Bland-Altman plots found a mean (SEM) difference between GT and estimated PAWP of - 0.23 (0.16) mm Hg in the training and - 0.05 (0.41) mm Hg in the test group. The RAM showed that our regression CNN model estimated high PAWP by focusing on the cardiomegaly and pulmonary congestion. In the test group, the area under the curve (AUC) for detecting elevated PAWP (≥ 18 mm Hg) produced by the regression CNN model was similar to the AUC of an experienced cardiologist (0.86 vs 0.83, respectively; P = 0.24). This proof-of-concept study shows that regression CNN can quantitatively estimate PAWP from standard chest radiographs in cardiovascular diseases.

SUBMITTER: Saito Y

PROVIDER: S-EPMC9239946 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:AimsThis study aimed to evaluate a novel echocardiographic algorithm for quantitative estimation of pulmonary artery wedge pressure (PAWP) and pulmonary vascular resistance (PVR) in patients with heart failure and pulmonary hypertension (PH) scheduled to right heart catheterization (RHC).Methods and resultsIn this monocentric study, 795 consecutive patients (427 men; age 68.4 ± 12.1 years) undergoing echocardiography and RHC were evaluated. Multiple regression analysis was performed to identify echocardiographic predictors of PAWP and PVR measured by RHC in the derivation group (the first 200 patients). The diagnostic accuracy of the model was then tested in the validation group (the remaining 595 patients). PH was confirmed by RHC in 507 (63.8%) patients, with 192 (24.2%) cases of precapillary PH, 248 (31.2%) of postcapillary PH, and 67 (8.4%) of combined PH. At regression analysis, tricuspid regurgitation maximal velocity, mitral E/e' ratio, left ventricular ejection fraction, right ventricular fractional area change, inferior vena cava diameter, and left atrial volume index were included in the model (R = 0.8, P < 0.001). The model showed a high diagnostic accuracy in estimating elevated PAWP (area under the receiver operating characteristic curve = 0.97, 92% sensitivity, and 93% specificity, P < 0.001) and PVR (area under the receiver operating characteristic curve = 0.96, 89% sensitivity, and 92% specificity, P < 0.001), outperforming 2016 American Society of Echocardiography/European Association of Cardiovascular Imaging recommendations (P < 0.001) and Abbas' equation (P < 0.001). Bland-Altman analysis showed satisfactory limits of agreement between echocardiography and RHC for PAWP (bias 0.7, 95% confidence interval -7.3 to 8.7) and PVR (bias -0.1, 95% confidence interval -2.2 to 1.9 Wood units), without indeterminate cases.ConclusionsA novel quantitative echocardiographic approach for the estimation of PAWP and PVR has high diagnostic accuracy in patients with heart failure and PH.

Project description:BackgroundPneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest radiographs. The localization of the pneumothorax lesions will help to quickly diagnose, which will be benefit for the patients in the underdevelopment areas lack of the experienced radiologists. In recent years, with the development of large neural network architectures and medical imaging datasets, deep learning methods have become a methodology of choice for analyzing medical images. The objective of this study was to the construct convolutional neural networks to localize the pneumothorax lesions in chest radiographs.Methods and findingsWe developed a convolutional neural network, called CheXLocNet, for the segmentation of pneumothorax lesions. The SIIM-ACR Pneumothorax Segmentation dataset was used to train and validate CheXLocNets. The training dataset contained 2079 radiographs with the annotated lesion areas. We trained six CheXLocNets with various hyperparameters. Another 300 annotated radiographs were used to select parameters of these CheXLocNets as the validation set. We determined the optimal parameters by the AP50 (average precision at the intersection over union (IoU) equal to 0.50), a segmentation evaluation metric used by several well-known competitions. Then CheXLocNets were evaluated by a test set (1082 normal radiographs and 290 disease radiographs), based on the classification metrics: area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive predictive value (PPV); segmentation metrics: IoU and Dice score. For the classification, CheXLocNet with best sensitivity produced an AUC of 0.87, sensitivity of 0.78 (95% CI 0.73-0.83), and specificity of 0.78 (95% CI 0.76-0.81). CheXLocNet with best specificity produced an AUC of 0.79, sensitivity of 0.46 (95% CI 0.40-0.52), and specificity of 0.92 (95% CI 0.90-0.94). For the segmentation, CheXLocNet with best sensitivity produced an IoU of 0.69 and Dice score of 0.72. CheXLocNet with best specificity produced an IoU of 0.77 and Dice score of 0.79. We combined them to form an ensemble CheXLocNet. The ensemble CheXLocNet produced an IoU of 0.81 and Dice score of 0.82. Our CheXLocNet succeeded in automatically detecting pneumothorax lesions, without any human guidance.ConclusionsIn this study, we proposed a deep learning network, called, CheXLocNet, for the automatic segmentation of chest radiographs to detect pneumothorax. Our CheXLocNets generated accurate classification results and high-quality segmentation masks for the pneumothorax at the same time. This technology has the potential to improve healthcare delivery and increase access to chest radiograph expertise for the detection of diseases. Furthermore, the segmentation results can offer comprehensive geometric information of lesions, which can benefit monitoring the sequential development of lesions with high accuracy. Thus, CheXLocNets can be further extended to be a reliable clinical decision support tool. Although we used transfer learning in training CheXLocNet, the parameters of CheXLocNet was still large for the radiograph dataset. Further work is necessary to prune CheXLocNet suitable for the radiograph dataset.

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Quantitative estimation of pulmonary artery wedge pressure from chest radiographs by a regression convolutional neural network.

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