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Clinical Trial Details — Status: Completed

Administrative data

NCT number NCT03600051
Other study ID # GRZ03 (PbE)
Secondary ID
Status Completed
Phase
First received
Last updated
Start date December 10, 2015
Est. completion date January 31, 2017

Study information

Verified date July 2018
Source CSD Labs GmbH
Contact n/a
Is FDA regulated No
Health authority
Study type Observational

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.


Recruitment information / eligibility

Status Completed
Enrollment 90
Est. completion date January 31, 2017
Est. primary completion date January 18, 2017
Accepts healthy volunteers No
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria:

- Adults with a heart defect verified by echocardiography

Study Design


Intervention

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.

Locations

Country Name City State
Austria University Hospital Graz Styria

Sponsors (1)

Lead Sponsor Collaborator
CSD Labs GmbH

Country where clinical trial is conducted

Austria, 

Outcome

Type Measure Description Time frame Safety issue
Primary Sensitivity for pathological heart murmur detection Ability to detect a pathological heart murmur in digital heart sound recordings obtained from an elderly population with heart valve disease. 2 months
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