Rochester Institute of Technology and University of Rochester Medical Center researchers have built a prototype digital stethoscope for grading heart ailments and heart-pump performance, using a combination of precision sensors, electrocardiogram (ECD) technology, and machine learning apps.
The team determined that the ability to capture natural heart sounds, as well as the sounds of an implanted left ventricular assist device, could hold the key to diagnosing suspected device dysfunction.
The Advanced Digital Stethoscope features a microphone for recording data collected through the stethoscope, combined with three-dimensionally-printed ECD leads. The machine learning algorithms are incorporated into the larger system to collect signals, and to learn and identify defects.
Methods incorporated into the system to enhance acoustic diagnostics include spectral analysis, advanced automated neural networks, and combined smartphone-based, interactive software that enables the clinician to make a diagnosis when integrating the advanced acoustic analyses with other routine clinical data.
From RIT News
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