Our medical device software connects an interface for voice and data collection with a deep-learning method trained on a labelled dataset (supervised learning).

The technicalities of our solution

As a first approach, we have implemented a cough classification model based on cough sound samples that were previously labelled wet/dry by one of the doctors in our team.

Over the past month, we achieved 90% accuracy on the cough classification model.
Currently, we are collecting data in pilot studies with partnerships with hospitals and research centres.

Our pilot study includes confirmed COVID-19 patients, patients diagnosed with other breathing related diseases (and who tested negative for COVID-19) and healthy people, as a control. Therefore, the audio data is labelled according to the confirmed infection status of the patients and acquired by trained healthcare professionals in a controlled setting. Additionally, we will rely on real-world-voice-data acquired through the web app and in partnership with real-world COVID-19 monitoring solutions (for example, the geoHealthApp from Germany).

How it works

The collected sound data, through your contribution, will allow us to train an artificial intelligence to estimate the probability of someone being affected by COVID-19 from the analysis of their vocal biomarkers and accompanying symptoms. Our product builds on medical research that has provided proof of concept for the detection, with a high level of accuracy, of diseases, including COVID-19, through cough and other vocal biomarkers. 

The interface for data collection is implemented in the form of a web app and an automated phone call. In particular, the phone line consists of a highly scalable (million simultaneous users) Interactive Voice Response (IVR) system that collects cough, voice and breath audio data. Alternatively, our software is also available through an application programming interface (API).