ThrombUS+ Project: Toward Wearable Continuous Point-of-Care Monitoring for Deep Vein Thrombosis of the Lower Limb.
Authors
Affiliations (18)
Affiliations (18)
- Kaunas University of Technology, Kaunas 44029, Lithuania.
- ATHENA RC, Xanthi 67100, Greece.
- Vermon Sa, Tours 37000, France.
- Fraunhofer Institute for Photonic Microsystems IPMS, Dresden 01109, Germany.
- Telemed UAB, Vilnius 02301, Lithuania.
- Echonous Inc., Redmond, WA 98052, USA.
- Medis Medizinische Messtechnik Gmbh, Ilmenau 98693, Germany.
- Comftech Srl, Monza 20900, Italy.
- Tampere University, Tampere 33100, Finland.
- Lithuanian University of Health Sciences, Kaunas 44307, Lithuania.
- General Hospital Papageorgiou, Thessaloniki 54603, Greece.
- Fondazione Casa Sollievo Della Sofferenza, San Giovanni Rotondo FG 71013, Italy.
- Groupement Hospitalier Eaubonne Montmorency Simone Veil, Montmorency 95160, France.
- VDE Association for Electrical, Electronic and Information Technologies, Offenbach Am Main 63069, Germany.
- Medea Srl, Massa (MS) 54100, Italy.
- Phaze SA, Athens 15451, Greece.
- PredictBy Research and Consulting S.L, Barcelona 08018, Spain.
- SciGen SA, Xanthi 67100, Greece.
Abstract
Deep vein thrombosis (DVT) is characterized by the formation of blood clots in the deep veins, most commonly in the lower limbs, obstructing blood flow. In about 50% of cases, the clot dislodges and travels to the lungs, causing potentially fatal pulmonary embolism. Diagnosing DVT is challenging, as up to half of cases are asymptomatic. Early and accurate diagnosis is critical, but the current state of the art in ultrasound and plethysmography is insufficient for continuous, operator-independent, point-of-care monitoring. This paper introduces the concept and advances of the ThrombUS+ Horizon Europe project, which aims to innovate toward modular and wearable system for continuous, point-of-care DVT monitoring. The system leverages a multimodal approach, incorporating compression ultrasonography, electrical impedance plethysmography, and light reflection rheography, by re-engineering these clinical technologies for seamless integration into a wearable architecture. Manual compression of an ultrasound transducer is replaced by a sophisticated actuator, enabling precise pressure control via a piezoelectric micropump with feedback signals. The same actuator supports plethysmography modules, providing additional data for DVT assessment. These inputs, combined with physical movement monitoring, are processed by an artificial intelligence-driven central intelligence unit for DVT detection, risk assessment, and prevention. Preliminary laboratory testing of the individual modules highlights the system's potential for autonomous DVT monitoring and detection. The ThrombUS+ uses big datasets for machine learning training collected in the project via 3 large-scale clinical studies. The entire system will be validated in the clinical setting via an early feasibility study and a multicenter clinical trial.