Introduction:
Chest X-ray (CXR) is a useful and economical modality for the detection of congestive heart failure. However, the accuracy is limited by the subjective nature of its interpretation. Deep learning (DL) can be used to recognize diseases or findings objectively in various imaging modalities, and may outperform previous diagnostic techniques.
Hypothesis:
We hypothesized that DL-based analysis of CXR detect the presence of elevated pulmonary arterial wedge pressure (PAWP) in patients with suspected heart failure.
Methods:
We enrolled 1,013 patients with paired right heart catheterization and CXR performed from October 2009 to February 2020 in our hospital. DL algorithm for the detection of elevated PAWP was developed using the training dataset, based on a single CXR image. Independent evaluation cohort of 115 patients was performed using CXR-based DL model and echocardiographic data to detect the presence of high PAWP. The area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of the DL-based models compared with echocardiographic data.
Results:
The study included 1,013 patients (mean age, 67±13 years; 569 males [56%]). The mean PAWP was 12.5±6.4 mmHg and 218 patients (22%) had more than 18mmHg. To detect high PAWP, the AUC produced by DL algorithms was effective, and the DL algorithm with the largest AUC was ResNet50. In an evaluation cohort, to detect high PAWP, the AUC using the DL model with CXR was similar to the AUC produced by the echocardiographic left ventricular diastolic dysfunction algorithm (0.77 vs. 0.70; respectively; p=0.27), and significantly higher than the AUC by measurements of echocardiographic parameters (ResNet50 vs. other parameters; all compared p <0.05)
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Conclusions:
The present results demonstrated that DL based on analysis of CXR can detect the presence of high PAWP. This finding suggests that the DL based approach may support an objective evaluation of CXR in the clinical setting.
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