Demographic changes induce an ever-higher burden on the healthcare system. Early detection of complications and diseases and the determination of effective personalized treatments are essential to lower this burden, to prevent complications, and to lower healthcare costs while simultaneously improving patient outcome and quality of life. The MINT project seeks to leverage technological advances in pathophysiological modeling, unobtrusive sensing, monitoring and AI towards this end.

Optimization of technologies

Wearable photoplethysmographicic technology will be optimized for monitoring of sleep and sleep-disordered breathing; contrast-enhanced ultrasound techniques will be explored for diagnosis of placental dysfunction; electrophysiological monitoring techniques will be explored and optimized for monitoring and diagnosis of high-risk pregnancies; AI image analysis techniques will be explored for improved diagnosis of peritoneal metastases of gastric carcinoma; and AI models and wearable monitoring technologies will be explored and optimized for early recognition of clinical deterioration of patients in hospital wards.

General approach

The general approach in MINT is to explore and co-create novel technological solutions in a controlled clinical environment, to subsequently refine and validate them and where appropriate transfer them to extramural and at-home settings, towards deployment in the healthcare system with the aim to increase effectiveness at lower costs in combination with lower patient burden and higher patient quality of life.