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A cross-platform smartphone auscultation SDK and optimized filters for severe Aortic stenosis detection.
In: (58th Hawaii International Conference on System Sciences, HICSS 2025, 7-10 January 2025, Honolulu). 2025. 3563-3572 (Proceedings of the Annual Hawaii International Conference on System Sciences)
Initial studies suggest that valve replacement may also benefit asymptomatic patients with severe aortic stenosis, who don't typically seek medical attention and thus require screening. As echocardiography, the current gold standard, is time-intensive and hence costly, a more convenient and broadly accessible alternative would be desirable. We present a cross-platform smartphone auscultation software development kit (SDK) for Android and iOS that uses the built-in microphone to record heart sounds. Our initial exploration shows that such recordings can detect 89% of severe aortic stenosis patients, compared to 95% for a digital stethoscope. In addition, we tackle the issue of smartphone audio quality as an image-to-image translation problem between spectrograms of smartphone and stethoscope recordings. Both CycleGAN and CUT are able to significantly decrease background noise, bringing the perceptual quality quantitatively and qualitatively closer to that of a digital stethoscope.
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Publication type
Article: Conference contribution
Keywords
Audio Processing ; Digital Health ; Machine Learning ; Smartphone Auscultation ; Unpaired Image-to-image Translation
ISSN (print) / ISBN
1530-1605
Conference Title
58th Hawaii International Conference on System Sciences, HICSS 2025
Conference Date
7-10 January 2025
Conference Location
Honolulu
Quellenangaben
Pages: 3563-3572
Institute(s)
Institute of AI for Health (AIH)