Automated Phonocardiography Analysis in Adults
Automated Phonocardiography Analysis in Adults
Background: Computer aided auscultation in the differentiation of pathologic (AHA class I)
 from no- or innocent murmurs (AHA class III) via artificial intelligence algorithms could be
 a useful tool to assist healthcare providers in identifying pathological heart murmurs and
 may avoid unnecessary referrals to medical specialists.

 Objective: Assess the quality of the artificial intelligence (AI) algorithm that autonomously
 detects and classifies heart murmurs as either pathologic (AHA class I) or as no- or innocent
 (AHA class III).

 Hypothesis: The algorithm used in this study is able to analyze and identify pathologic heart
 murmurs (AHA class I) in an adult population with valve defects with a similar sensitivity
 compared to medical specialist.

 Methods: Each patient is auscultated and diagnosed independently by a medical specialist by
 means of standard auscultation. Auscultation findings are verified via gold-standard
 echocardiogram diagnosis. For each patient, a phonocardiogram (PCG) - a digital recording of
 the heart sounds - is acquired. The recordings are later analyzed using the AI algorithm. The
 algorithm results are compared to the findings of the medical professionals as well as to the
 echocardiogram findings.
Inclusion Criteria:

 -  Adults with a heart defect verified by echocardiography