Project description
Faster detection of chronic diseases
Early detection of chronic diseases is vital for better treatment outcomes, but current methods are slow and expensive. In vivo diagnostic approaches can speed up some processes, but they still have long delays and are invasive. A major challenge is reducing turnaround times, which can take over 10 000 minutes. Supported by the Marie Skłodowska-Curie Actions programme, the BeNiFIt project will use tiny, in-body nanoscale communications (IBNC). This technology will make diagnostics faster, cheaper, and less invasive by reducing delays to just a few minutes. BeNiFIt will also increase detection accuracy, using nanomachines with biosensors to identify biomarkers with high precision. This breakthrough has the potential to transform healthcare by providing real-time, efficient diagnostics.
Objective
Early detection is crucial in improving treatment outcomes and alleviating the burden of chronic diseases, as emphasized by the EU Mission: Cancer. In vivo dianosgtic approaches have been shown to eliminate a couple of post-processing stages, but no significant reduction in turnaround delays has been reported, in addition to being costly, bulky, and invasive. The next natural step in innovation would be to reduce turnaround delays from the present best of 10,000 minutes to within a few tens of minutes, a 1000X improvement, which BeNiFIt aims to introduce. The goal of the BeNiFIt project is to show that intra-body nanoscale communications (IBNC) can be used to make in-vivo diagnostics possible. This will pave the way for more advanced early detection and real-time monitoring methods that are minimally invasive, low-cost, and have small turnaround delays. Through BeNiFIt, I will demonstrate the competence of IBNC for in vivo detection of biomarkers with high accuracy by using nanomachines equipped with biosensors. A cooperative sensing approach will be developed to maximize the sensitivity and specificity of the biosensors, thus reducing the number of false positives/negatives caused by the presence of other molecules. In addition, I will design an approach to integrating the sensing and communication hardware to meet the hardware requirements of nanomachines. A near-field magnetism-based system and passive communication architecture will be implemented to ensure ultra-high safety and energy efficiency. Lastly, an open-source system-level simulator will be developed, spanning microscopic to macroscopic scales, to analyze the impact of the design choices on the system’s performance. The KPI targets of BeNiFIt include >95% detection accuracy and a 10X reduction in treatment costs, besides the 1000X reduction in turnaround delays. These outstanding performances are set to disrupt the healthcare industry.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
- engineering and technologyelectrical engineering, electronic engineering, information engineeringelectronic engineeringsensorsbiosensors
- medical and health sciencesclinical medicineoncology
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Keywords
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinator
08034 Barcelona
Spain