The Follow The Dot project evaluates the use of Virtual Reality in EMDR (VR-based EMDR) in children with post-traumatic stress symptoms in a multicenter Randomized Controlled Trial (RCT). VR-based EMDR stimulates self-care in the family setting and is expected to reduce symptom recurrence, enhance patient satisfaction and improve quality of life.
The cancer grade, provided by pathologists, is the most important predictor of patient outcome, but suffers from inter- and intra-pathologist variability, reducing its usefulness for individual patients. An expert-level AI system will be provided to support pathologist and help reduce this variability and make their diagnostic practice more accurate and efficient.
The project LIF2.0 aims to develop a short term response to COVID-19 needs in industry and public organisations by offering a real time COVID-19 driven data platform covering European wide aggregated content from a vast amount of sources. Users will have insights for developing new echo period strategies.
The knowledge is missing that is essential for cost-effective roll-out of personalised treatment of insomnia. This project solves the bottleneck by creating a research platform for combined online behavioural change intervention and long-term monitoring of sleep, traits and health. A growing database will allow for optimised sleep interventions tailor to personalised needs, capacities, limitations and estimated benefits.
The project will test the Affective Bonding Theory and investigate how people relate and affectively bond to a robot and what the effects are on loneliness and quality of life of (lonely) older adults. A newly developed social robot will accompany senior citizens for several weeks to test this.
Image-guided external beam radiotherapy has the potential to considerably improve therapy delivery. This project is focused on dedicated image processing and dose accumulation algorithms that allow to exploit these possibilities in difficult anatomical areas.
A successful societal uptake of digital eye health requires a robust and safe telemonitoring platform that addresses the needs of its users. Patients request a simple tool that works intuitively with little external instructions. Doctors need a dataflow fully integrated in the electronic health record, and both need to develop trust in the validity and safety of remote monitoring. These requirements are addressed in this project.
Prognostic models providing clinicians with survival probabilities based on for example patient and diseasecharacteristics can help them to tailor care better to individual patients’ needs. In this project, several key issues hindering imbedding of prediction models in daily clinical practice will be addressed.
COVID-19 is a new phenomenon which has both a respiratory and a cardiac impact regarding the health of the patient. Rapid detection of the cardiovascular abnormalities in a COVID-19 patient will support treatment decisions. In this project a new rapid method will be developed to detect these cardiovascular characteristics in COVID-19 patients by only using non-invasive 12-lead ECG data.