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The stethoscope is one of the most important diagnostic tools used by healthcare professionals, through a process called auscultation, to screen patients for abnormalities of the heart and lungs. While there are digital stethoscopes on the market which ease this process, it still takes years of training to properly use these devices to listen for abnormal sounds within the body. We present ARSteth, an intelligent stethoscope platform that improves the accessibility of stethoscopes for the general population, allowing anyone to perform auscultation in the comfort of their own homes. Our platform utilizes a combination of augmented reality (AR), acoustic intelligence, and human-machine interaction to dynamically guide users on where to place the stethoscope on different parts of the body (auscultation points), through visual and audio cues. Through user studies, we show that ARSteth, on average, can guide users within 13.2 mm from optimal auscultation points marked by licensed physicians in 13.09 seconds for each auscultation point. By guiding users towards more effective auscultation points, make preventative health screening more accessible and effective for everyone we are able to achieve higher confidence on classifying heart murmurs.
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AI Stethoscope for Home Self-Diagnosis with AR Guidance
Cardiopulmonary ailments are a major cause of mortality. Stethoscopes are one of the most important tools that healthcare professionals use to screen patients for a variety of ailments, especially those related to the heart and lungs. Despite the growth of digital stethoscopes on the market, it takes years of training to properly use stethoscopes to listen for abnormal sounds within the body. In this demonstration, we present an intelligent stethoscope platform that makes stethoscopes more accessible to the general population. Our platform utilizes augmented reality (AR) to provide real-time guidance on where to properly place the stethoscope on the body, enabling the general population to screen themselves for ailments.
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- PAR ID:
- 10416026
- Date Published:
- Journal Name:
- SenSys '22: Proceedings of the 20th ACM Conference on Embedded Networked Sensor Systems
- Page Range / eLocation ID:
- 762 to 763
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3560905.3568082
