Stethoscopes are conventionally analogue products. With the rise of digitalization in recent years, more and more digital stethoscopes flood the market, which offer physicians the possibility to store auscultation recordings, to digitize interesting diagnosis for teaching purposes or to apply certain filters for a better sound quality depending on the auscultation of different body regions. However, stethoscopes are usually personal belonging of the physicians and they tend not to switch to a more expensive, digital product despite its advantages. Besides, many physicians prefer the analogue sound over digitized sound due to the fact, that certain frequencies might be damped to the sampling of the sound signal.
In this project, the Digioscope team develops a modular attachment to conventional analogue stethoscopes, which is capable of digitizing the auscultation results while maintaining the analogue sound. With the digitized sound, machine learning techniques can be applied to provide different auscultation relevant metrics, which can support the physicians in their diagnosis.
Students: Dorothee Lippold, Juan Carlos Suarez, Arash Bagherzadehyazdi
Scrum Master: Markus Zrenner