Machine learning analysis of heart tones from phonocardiograms enables cost-effective, non-invasive diagnosis of pulmonary hypertension suitable for primary care.
Does machine learning-based analysis of phonocardiograms diagnose pulmonary hypertension?
Machine learning-based analysis of phonocardiograms is proposed as a cost-effective, non-invasive diagnostic tool for pulmonary hypertension, particularly useful in resource-limited settings.
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The authors propose a way of diagnosing pulmonary hypertension through machine learning-based analysis of phonocardiograms. The technique offers a cost-effective and non-invasive solution that allows it to be used in primary healthcare. It is particularly valuable for regions with limited access to specialized clinics and highly qualified cardiologists.
Ryabkov et al. (Thu,) reported a other. Machine learning analysis of heart tones from phonocardiograms enables cost-effective, non-invasive diagnosis of pulmonary hypertension suitable for primary care.