Product maturation – the PyXy design was optimized, adding further functionalities to it and improving its ergonomics. Industrial, mechanical and electronic designs were finalized, and the development of the device’s SW has started. The new device incorporates sound capturing (ranging from infrasound to audible sound) with one-lead ECG and SpO2 measurement capabilities.
Cloud App, mobile and Web applications – A cloud app that includes dashboards for the care providers (also hospitals, clinics) was built and demonstrated to clinical partners within the consortium. Operating API for cloud app has been developed as well. This enables to integrate with existing IT systems used by the healthcare sector, be it electronic patient journals or e-health platform providers. When such external systems for test-integration are identified further, new parameters for exchange of data with 3rd party solutions might be introduced.
Clinical affairs – ethics approvals for data collection from COVID-19 and chronic heart and lungs patients were obtained in Germany, Spain, Israel and Norway. Data collection has begun in Germany, Spain and Israel, and is expected to commence in Norway in February. So far, heart and lung sounds were collected from over 200 patients overall. Delays in ethics approvals caused a slow start to the data collection process, but it is expected to quickly pick up in the coming months.
AI exacerbation detection algorithm – One of the main development targets for AI is to develop algorithms for indicating development in the lungs state for both COVID context (i.e. positive/negative progression in the condition's development to be able to identify onset of alarming symptoms) as well as patient-centered, resource-efficient chronic care management of lung and heart patients for indicating positive/negative development in their condition. So far, the focus has been on modifications of current Medsensio’s algorithms for data collected in this project using Sanolla’s VoqX. In addition, efforts were put in development of annotation guidelines for organizing annotation of data from WP1 which will be necessary for further ML training.
Exploitation and dissemination – a website and a LinkedIn page for the project were created. These platforms are continuously updated with project related news to bring up the awareness to the project.
