Healthcare can significantly benefit from digital innovations in cloud and edge computing. These technologies hold great potential for improvement, especially when processing large volumes of data. A prime example is the analysis of long-term ECG recordings, where approximately 100,000 heartbeats are measured per session.
The FACE project, led by , is developing solutions that leverage the advantages of artificial intelligence (AI), edge computing, and cloud computing to make ECG analysis more efficient and improve patient care. The project’s innovative approach lies in a two-stage analysis process that intelligently distributes the workload between edge and cloud systems. This involves creating interfaces that ensure seamless integration of ECG data between edge and cloud models, enabling fast and precise data transfer and continuous model improvement.
By employing state-of-the-art machine learning models, the project supports a continuous learning process that aids experts in evaluating and interpreting ECG findings. To ensure that the developed models are not only technologically advanced but also clinically relevant, the project collaborates closely with medical professionals and research institutions.
Project Consortium:
- GmbH & Co. KG
- AG
