A biooptical method for microcirculation assessment as an early predictor of cardiovascular disease
|Coordinator||Linköpings universitet - Institutionen för medicinsk teknik (IMT)|
|Funding from Vinnova||SEK 2 000 000|
|Project duration||September 2016 - August 2018|
|Venture||Medtech4health: Medicintekniska innovationer|
|Call||Medicintekniska innovationer inom vård och omsorg Medtech4Health - 2016 vår|
Purpose and goal
The aim is to further develop a system for comprehensive microcirculation assessment by measuring blood flow, blood concentration and oxygen saturation in physiologically relevant units. Measurement accuracy is validated using advanced optical phantoms. The usability is evaluated for nurseled clinical research measurements. The system will be used in clinical research to study the relationship to cardiovascular diseases within the national SCAPIS study (Swedish Cardiopulmonary bioImage Study; Heart-Lung foundation). Perimed AB will bring the system to a research market.
Expected results and effects
Measurement accuracy is good, and the systems is user friendly when operated by health care staff. The measurement failure rate is very low. The software gives valuable feedback on signal quality and the data analysis. We have performed a very large and unique study on the relationship to cardiovascular disease. Several scientific articles have been published and popular science presentations have been given. The system is marketed by Perimed and several units have been sold.
Planned approach and implementation
We have used two systems in the project, one for studies of measurement accuracy in collaboration with the world-leading Beckman Laser Institute, CA, USA. In parallel, we have undertaken an extensive clinical study within the national SCAPIS study. One full time nurse has performed data recording for three years. The company Perimed and the researchers have supported the system and the measurements. The data is analyzed in several clinical studies. Perimed aims to further develop the analysis software using machine learning for improved robustness.