@Article{chd.2024.048314,
AUTHOR = {Yang Wang, Xun Yang, Mingtang Ye, Yuhang Zhao, Runsen Chen, Min Da, Zhiqi Wang, Xuming Mo, Jirong Qi},
TITLE = {Machine Learning-Based Intelligent Auscultation Techniques in Congenital Heart Disease: Application and Development},
JOURNAL = {Structural and Congenital Heart Disease},
VOLUME = {19},
YEAR = {2024},
NUMBER = {2},
PAGES = {219--231},
URL = {http://www.techscience.com/schd/v19n2/56478},
ISSN = {3071-1738},
ABSTRACT = {Congenital heart disease (CHD), the most prevalent congenital ailment, has seen advancements in the “dual indicator” screening program. This facilitates the early-stage diagnosis and treatment of children with CHD, subsequently enhancing their survival rates. While cardiac auscultation offers an objective reflection of cardiac abnormalities and function, its evaluation is significantly influenced by personal experience and external factors, rendering it susceptible to misdiagnosis and omission. In recent years, continuous progress in artificial intelligence (AI) has enabled the digital acquisition, storage, and analysis of heart sound signals, paving the way for intelligent CHD auscultation-assisted diagnostic technology. Although there has been a surge in studies based on machine learning (ML) within CHD auscultation and diagnostic technology, most remain in the algorithmic research phase, relying on the implementation of specific datasets that still await verification in the clinical environment. This paper provides an overview of the current stage of AI-assisted cardiac sounds (CS) auscultation technology, outlining the applications and limitations of AI auscultation technology in the CHD domain. The aim is to foster further development and refinement of AI auscultation technology for enhanced applications in CHD.},
DOI = {10.32604/chd.2024.048314}
}