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Phonolyser:

Smart heart sound analyzer based on Doppler technology

Development Stage: Final product developed

Industry: Healthcare, Medical Devices, Diagnostics

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Phonolyser™ is a smart heart sound analyzer based on Doppler technology that has been invented and developed to combine AI (Signal processing and analyzing), doppler-technology, and sound analysis to comprehensively investigate and assess the probability of congenital heart diseases through blood flow velocity and turbulence with confidence.

Phonolyser detects and records the heart murmurs and runs an analysis on them based on prior AI-based developed patterns and displays the results for diagnosis. These features lead to diagnosing CHD specifically for the neonates.
Moreover, it is important to know that CHD subtypes like VSD, ASD, and PDA with higher prevalence today can be easily detected and diagnosed by Phonolyser™.

 

 

Value Proposition

Having confident CHD diagnosis at the right time is a significant need to prevent heart diseases from further development. The most important values that Phonolyser™ delivers are availability, accuracy, proper time procedure, reasonable cost, applicability.

Problems to solve

The global prevalence of congenital heart disease at birth, in 2017, is estimated to be nearly 1.8 cases per 100 live births.

Not detecting low pitched murmurs in babies by stethoscope.
Limited access to diagnostic devices in non-developed countries.
Limited access to expert physicians and/or cardiologists.
Time-consuming and costly hi-tech diagnosis methods.

 

Features

  • Phonolyser™ visualizes low gradient differences that cannot be heard through a stethoscope.
  • Along with using visual acuity, physicians will be able to examine the structural and functional status of the heart with greater care.
  • At the patient’s bedside, Heart sound is extracted from the heat’s mechanical motion, blood flow velocity and turbulence using Doppler technology.
  • Extracting the characteristics of the signal to distinguish normal sound from abnormalities and extracting murmur parameters and displaying on the screen.