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AUdio models for RespiratOry and cardiac diagnostics and clinical tRAining

CORDIS · observation · Publication date unknown

aining. The clinical training is generally based on shadowing experienced clinicians and repeating auscultation: this has scalability limits and imposes a considerable burden onto the health system. Machine learning for human sounds has beHuman sounds, being them bodily sounds (e.g., heart sounds) or voice, have been used by doctors for centuries as diagnostics signals for health and disease progression. However, while stethoscopes and microphones are very affordable, diagnosing through sound is a challenging task for medical professionals and requires years of training. The clinical training is generally based on shadowing experienced clinicians and repeating auscultation: this has scalability limits and imposes a considerable burden onto the health system. Machine learning for human sounds has been explored by the research community and is showing promise. In project ERC EAR we have advanced the state of the art in this respect for cardiac and respiratory health tasks. We showed that models could complement the skills of clinicians when diagnosing and constructed powerful and pio

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recordType

award

status

SIGNED

region

EU

value

150000

unit

EUR