Product Features

End-to-end mobile and cloud based software solution.

App records heart sounds and displays results in seconds.

Intelligent live artifact detection to optimize recording quality.

Detects and classifies murmurs with high accuracy.

Validated through blinded trials, multi-centered and internationally.

HIPAA compliant server on the cloud for storing recordings and findings.

Web portal for auditing/comparing results and recordings.

PDF reports for documenting, reviewing, billing and EMR integration.

Studies Synopsis

Clinical studies completed: 5

Clinical studies ongoing: 3

Data from patients analyzed: 1,000+

Heart sound recordings analyzed: 3,000+

Heart murmurs classified: 1,500+

Heart beats analyzed: 50,000+

Heart rates ranges detected: 50-180 bpm

Patient populations: newborns, children, youth, adults, elderly

Clinically observed sensitivity: 85.0%

Clinically observed specificity: 86.7%

Optimizing Care

Heart Auscultation

Peer Reviewed Publications

 

2017/11/14

Circulation 2017

Introduction: Automated software applications that analyze digital heart sound recordings, phonocardiograms (PCGs) could be used to improve the screening accuracy for valvular and congenital heart disease.
Hypothesis: The new software detection program... Read more
Introduction: Automated software applications that analyze digital heart sound recordings, phonocardiograms (PCGs) could be used to improve the screening accuracy for valvular and congenital heart disease.
Hypothesis: The new software detection program... Read more

 

2016/09/23

Computerized Automatic Diagnosis of Innocent and Pathologic Murmurs in Pediatrics: A Pilot Study.

Objective:Computer-aided auscultation in the differentiation of pathologic (AHA class I) from no or innocent murmurs (AHA class III) would be of great value to the general practitioner. This would allow objective screening for structural heart disease, standardized documentation of auscultation findings, and may avoid unnecessary referrals to pediatric cardiologists. Read moreObjective:Computer-aided auscultation in the differentiation of pathologic (AHA class I) from no or innocent murmurs (AHA class III) would be of great value to the general practitioner. This would allow objective screening for structural heart disease, standardized documentation of auscultation findings, and may avoid unnecessary referrals to pediatric cardiologists. Read more

 

2017/11/14

AHA Abstract 2017

Introduction: Signal processing algorithms designed to detect and analyze heart murmurs could dramatically improve screening for valvular and congenital heart disease (CHD), however development has been hampered by lack of objective validation strategies. Read moreIntroduction: Signal processing algorithms designed to detect and analyze heart murmurs could dramatically improve screening for valvular and congenital heart disease (CHD), however development has been hampered by lack of objective validation strategies. Read more

 

2017/07/16

IAC Abstract 2017

Objective: Evaluate clinical performance of a new heart murmur detection algorithm on an elderly population through a blinded trial.
Background: Algorithms that autonomously detect heart murmurs have the potential to improve auscultation accuracy, but ... Read more
Objective: Evaluate clinical performance of a new heart murmur detection algorithm on an elderly population through a blinded trial.
Background: Algorithms that autonomously detect heart murmurs have the potential to improve auscultation accuracy, but ... Read more