Key result
Machine learning models applied to PCG signals improve early diagnostic accuracy for valvular heart disease.
Why the study?
Early detection of VHD reduces critical cardiac problems and sudden death, prompting an investigation into automated diagnosis from PCG signals using advanced ML and DL models.
Can machine learning and deep learning models accurately detect valvular heart disease from phonocardiogram signals?
Can machine learning and deep learning models accurately detect valvular heart disease from phonocardiogram signals?
Advanced machine learning and deep learning techniques applied to phonocardiogram signals show increasing accuracy for the early detection of valvular heart disease.
Answers are grounded in this paper and cite their sources.
Cautions against routine clinical adoption of phonocardiogram ML for valvular detection; leaves open prospective validation.
Artificial intelligence (AI) predictions are widely used to address challenges in the heart health sector, such as providing clinical decision support. Early detection of valvular heart disease (VHD) is effective in reducing critical cardiac problems and sudden death. This review proposes investigating methods for automatically diagnosing heart disease from phonocardiogram (PCG) signals using various advanced Machine Learning (ML) and Deep Learning (DL) models. This study also aimed to provide an overview of ongoing research on PCG signal processing and to pinpoint areas that warrant additional investigation. Several Scopus-indexed research forums, such as IEEE, Science Direct, Frontiers, MDPI, and Computing in Cardiology, as well as databases such as the Michigan Heart Sound Library (MHSL), Github, and Physio-Net on the classification of AI-related PCG signals, were considered to construct this review with 199 relevant research articles covering the period from 2016 to 2024. The early diagnosis and prediction of heart valve disease are the domains in which machine learning and deep learning models were most commonly used. The performance of earlier detection has increased significantly according to advanced techniques of PCG signal classification. A limited number of studies have compared and analyzed categorization measures such as F score, sensitivity, accuracy, precision, and specificity. However, a mean increase in the predicted accuracy was observed, depending on the various advanced techniques and classifiers used.
No takes yet. Share an insight, caveat, or question.
Kannan et al. (2025) conducted a review in Valvular heart disease. Machine Learning and Deep Learning models was evaluated on Predicted accuracy of heart disease diagnosis from PCG signals. Machine learning and deep learning models applied to phonocardiogram signals demonstrated a mean increase in predicted accuracy for the early detection of valvular heart disease.
Synapse has enriched 10 related papers. Consider them for comparative context: