High Fidelity 4D Data Modeling
High fidelity data modeling in a convenient format brings easy access to scientists wherever they choose to work.
The High Profile project leveraged MEDrecord’s platform to store and retrieve 1, 2, 3 and even 4D data.
We merged previously captured MRI and fMRI images with real-time EEG and EcOG data.
The result is a real-time 4D rendering that the neuroscientists can use to observe and diagnose patient conditions with a maximum of usable information.
We also worked with Barco to develop an application that calibrates the information to be displayed on a tablet screen.
This enables specialists to monitor and view scans on their tablets with the same pictures and contrast as on a larger (and more expensive) Barco screen.
The High Profile project was coordinated by Philips research with 17 European partners. It was funded for € 17,1 million by the ARTEMIS JOINT UNDERTAKING, SUB-PROGRAMME ASP2: Healthcare systems, Industrial Priority 3.1.2: Seamless connectivity and middleware.
The ARTEMIS Industrial Association represents Industry (large, small and medium sized companies), universities and research institutes. The ARTEMIS JU is an organisation based in Brussels, that was legally established in February 2008 and became autonomous in October 2009.
ARTEMIS aims to tackle the research and structural challenges faced by European industry by defining and implementing a coherent Research Agenda for Embedded Computing Systems.
We work with our partners to make an impact on people's lives
In addition to our highly experienced team of developers, that have been building mobile applications for over 10 years, MedRecord is lucky to work with leading medical and health technology companies on our projects.
End-to-end Solutions for Disease Specific Clinical Decision Support
SYMPHONY will create an open healthcare IT-ecosystem, providing care professionals with real-time, comprehensive insights in the patient’s status, integrating all relevant information for diagnosis, treatment selection as well as follow-up.
Developing AI ecosystems improving diagnosis and care of mental diseases
The DAIsy project will create an AI for the mental healthcare ecosystem by combining expertise on patient monitoring technology, data collection and aggregation technology, domain expertise from care professionals and AI experts.
A Smart Companion for People on Tube Feeding Regimens
The Food-Friend application assists people that are using tube feeding devices by making their nutritional and fluid intake accurate and easily trackable.