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Clinical Trial Summary

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.


Clinical Trial Description

n/a


Study Design


Related Conditions & MeSH terms


NCT number NCT03600051
Study type Observational
Source CSD Labs GmbH
Contact
Status Completed
Phase
Start date December 10, 2015
Completion date January 31, 2017

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