M³Infekt – Decentralized monitoring of COVID-19 patients

Monitoring system to enable early intervention should a patient’s condition deteriorate.

Objective

M3Infekt is a cluster project that aims to develop a patient monitoring system to enable early intervention should a patient’s condition suddenly deteriorate, using the example of COVID-19 sufferers. By facilitating the required intervention at an early stage, the system helps to slow the progression of the disease, shorten the duration of treatment and make flexible use of intensive care units (ICUs). In the long term, the system addresses decentralized monitoring of patients on regular wards and in non-hospital environments.

Background

Up to now, continuous patient monitoring has been exclusive to the ICU. This means that there is often a delay in recognizing a sudden deterioration in what started out as a non‑serious condition, resulting in the patient being taken to the hospital too late. In addition to extending treatment time, this often also causes irreparable damage to the patient.

Use machine learning to diagnose disease progression at an early stage

Our solution

The planned system idea should be multimodal, modular and mobile.

  • Multimodality is ensured by reliable capturing of the relevant biosignals relating to the cardiovascular system (including heart rate, ECG and oxygen saturation) and breathing (including respiratory rate/volume) with the help of a range of technologies in various combinations.
  • Characterized by standardized open interfaces, the system’s modularity means it can be easily integrated into other platforms and adapted for use in treating other conditions such as influenza, pneumonia and sepsis.
  • Making this monitoring unit mobile guarantees it will find application also in non‑hospital settings, such as short- and long-term nursing care and at patients’ homes.
  • Evaluation of the signals captured is based on machine learning and artificial intelligence methods that pave the way for automated, valid diagnoses concerning a patient’s condition and how it will progress.
     

Added value of the monitoring system

  • Early detection of a deterioration in a patient’s condition ensures better progression predictions, situationally appropriate treatment concepts and a higher quality of life.
  • Patient monitoring outside the ICU guarantees more efficient utilization of hospital resources such as equipment, rooms and personnel.
  • Early intervention leads to shorter treatment times, which in turn saves hospitals money.
  • Sensitive health data is protected with the help of encryption technologies, certification in line with the prevailing legislation on medical devices, and a decentralized data platform.
     

Current situation

  • Designing the overall system and interfaces
  • Preparing to test sensor subsystems as part of clinical trials
  • Testing and developing additional sensors and technologies
Cardio
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M3Infekt Consortium

Partner

The M3Infekt consortium, led by Fraunhofer IIS, consists of ten Fraunhofer institutes and four medical partners, whose various professional competencies complement each other in an interdisciplinary manner.

  • Fraunhofer-Institut für Integrierte Schaltungen IIS, Bereich Smart Sensing and Electronics
  • Fraunhofer-Institut für Integrierte Schaltungen IIS, Institutsteil EAS
  • Fraunhofer-Institut für Photonische Mikrosysteme IPMS
  • Fraunhofer-Institut für Keramische Technologien und Systeme IKTS
  • Fraunhofer-Institut für Elektronische Nanosysteme ENAS
  • Fraunhofer-Institut für Hochfrequenzphysik Und Radertechnik FHR
  • Fraunhofer-Institut für Zerstörungsfreie Prüfverfahren IZFP
  • Fraunhofer-Institut für Betriebsfestigkeit und Systemzuverlässigkeit LBF
  • Fraunhofer-Institut für Mikrosysteme und Festkörper-Technologien EMFT
  • Fraunhofer-Projektzentrum Mikroelektronische und Optische Systeme für die Biomedizin MEOS

Clinical Partner

  • Klinikum Magdeburg
  • Charité – Universitätsmedizin Berlin
  • Universitätsklinikum Erlangen
  • Universitätsklinikum Dresden


The M3Infekt project is funded as part of the Fraunhofer-Gesellschaft’s internal programs.