Automated Phonocardiography Analysis in Adults

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Total Enrollment

90 participants

Study Classification

OBSERVATIONAL

Study Start Date

2015-12-10

Study Completion Date

2017-01-31

Brief Summary

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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.

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Detailed Description

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Conditions

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Aortic Insufficiency Aortic Stenosis Mitral Insufficiency Mitral Insufficiency and Aortic Stenosis Tricuspid Regurgitation Insufficiency, Pulmonary Insufficiency, Tricuspid

Study Design

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Observational Model Type

COHORT

Study Time Perspective

CROSS_SECTIONAL

Interventions

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Automated Heart Murmur Detection AI

Automated AI algorithm-based analysis of digital heart sound recordings to detect pathological heart murmurs. Heart sound recordings were fully blinded before undergoing one-time automated analysis. Algorithm results for each recording included: AHA classification (I "pathologic" versus III "innocent/no murmur"), murmur timing, murmur grade, heart rate and S1/S2 identification.

Intervention Type DEVICE