Objective: This study aims to assess the ability of state-of-the-art machine learning algorithms to detect valvular heart disease (VHD) from digital heart sound recordings in a general population that includes asymptomatic cases and intermediate stages of disease progression.
Methods: We trained a recurrent neural network to predict murmurs from heart sound audio using annotated recordings collected with digital stethoscopes from four auscultation positions in 2,124 participants from the Tromsø7 study. The predicted murmurs were used to predict VHD as determined by echocardiography.
Objective: The objective of this study was to determine the diagnostic accuracy in detecting valvular heart disease (VHD) by heart auscultation, performed by medical doctors.
Design/methods: A systematic literature search for diagnostic studies comparing heart auscultation to echocardiography or angiography, to evaluate VHD in adults, was performed in MEDLINE (1947-November 2021) and EMBASE (1947-November 2021). Two reviewers screened all references by title and abstract, to select studies to be included.
Objective: To investigate interrater and intrarater agreement between physicians and medical students on heart sound classification from audio recordings, and factors predicting agreement with a reference classification.
Design: Intra- and interrater agreement study.
Subjects: Seventeen GPs and eight cardiologists from Norway and the Netherlands, eight medical students from Norway.