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Automated Phonocardiography Analysis in Adults

NCT03600051 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 90

Last updated 2018-07-26

No results posted yet for this study

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.

Conditions

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

Interventions

DEVICE

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.

Sponsors & Collaborators

  • CSD Labs GmbH

    lead OTHER

Principal Investigators

  • Rita Riedlbauer, MD · Medical University of Graz

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2015-12-10
Primary Completion
2017-01-18
Completion
2017-01-31

Countries

  • Austria

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03600051 on ClinicalTrials.gov