DIGIPREDICT project outcomes

This page presents the main scientific outcomes of the DIGIPREDICT project, reflecting its impact as a pionneer in the field of digital twin for healthcare applications.

Project introductory video

Below you will find a short video explaining the main features of the DIGIPREDICT project.

Stacks Image 139

watch on

share on

DIGIPREDICT video series

DIGIPREDICT brings together various fields of expertise with one single goal: digital twin to predict the progression of disease and the need for early intervention in infectious and cardiovascular diseases.

Discover the various aspects of the DIGIPREDICT research through our video series.

Protein detection based on Si Nanowire FET Sensor Arrays.
Luca Capua, EPFL
Stacks Image 157

watch on

share on

download

Design and simulation of a CNT strain sensor with low mechanical cross-sensitivity.
Maximilian Aue
Stacks Image 236

watch on

share on

download

Multimodal CMOS MEA chip for OoC applications
Mar Cóndor, IMEC-BE
Stacks Image 353

watch on

share on

download

Towards designing a Digital Twin for ICU patients
Hojjat Karami, EPFL
Stacks Image 327

watch on

share on

download

Development of an automatic, modularized and multiplexed heart-on-a-chip platform
Shao-Hsuan Kuo, UTWENTE
Stacks Image 340

watch on

share on

download

Quadro-channel organ-on-chip for modelling and studying the blood-brain barrier
Mariia Zakharova, UTWENTE
Stacks Image 288

watch on

share on

download

Respiration rate V&V – DIGIPREDICT Physiopatch
Roberto Garcia, IMEC-NL
Stacks Image 301

watch on

share on

download

Bioimpedance spectroscopy to assess (chronic) inflammation
Lucas Lindeboom, IMEC-NL
Stacks Image 314

watch on

share on

download

Scientific publications

1.
Label-Free C-Reactive Protein Si Nanowire FET Sensor Arrays With Super-Nernstian Back-Gate Operation.
IEEE Transactions on Electron Devices 1–7 (2022). doi: 10.1109/TED.2022.3144108. Archive: Infoscience
2.
Double-Gate Si Nanowire FET Sensor Arrays For Label-Free C-Reactive Protein detection enabled by antibodies fragments and pseudo-super-Nernstian back-gate operation.
in 2021 IEEE International Electron Devices Meeting (IEDM) 16.2.1–16.2.4 (IEEE, 2021). doi: 10.1109/IEDM19574.2021.9720670. Archive: Infoscience
3.
An Improved Analysis of Gradient Tracking for Decentralized Machine Learning.
in Advances in Neural Information Processing Systems 34, (NIPS, 2021). Archive: arXiv
4.
RelaySum for Decentralized Deep Learning on Heterogeneous Data.
in Advances in Neural Information Processing Systems 34, (NIPS, 2021). Archive: arXiv
5.
Consensus Control for Decentralized Deep Learning.
in Proceedings of the 38th International Conference on Machine Learning 139, 5686–5696 (PMLR, 2021). Archive: arXiv
6.
Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data.
in Proceedings of the 38th International Conference on Machine Learning 139, 6654–6665 (PMLR, 2021). Archive: Infoscience
This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101017915 (DIGIPREDICT).
©2021 DIGIPREDICT Project – Developed by SCIPROM —