43 collaborating researchers from TU/e, WUR, UU and UMC Utrecht received 260.000 euros of seed funding for cross-disciplinary collaborations within seven projects. The aim of the funding is to drive forward the development and application of Artificial Intelligence (AI) to the benefit of society. The main focus is on the priority areas of the alliance, preventive health and circular society. The projects are innovative and interdisciplinary.
The alliance offers researchers and lecturers the opportunity to explore new interdisciplinary connections between the partner institutions. This call stimulates the development of research projects that will contribute to the innovative development and application of trustworthy AI. The money will be invested in initiatives strengthening the transition to a healthy and sustainable society.
The three awarded AI projects are:
SMASH
Automated segmentation of stomach contents in MRI images with the use of deep learning
The project objective is to build and validate an AI-based software tool that can perform automatic segmentation of the stomach contents on abdominal MRI images. This AI solution contributes to more knowledge on gastric digestion and thereby the development of optimized foods for target groups such as elderly, athletes and infants. It also sets the stage for better characterization of gastrointestinal physiology and disorders involving altered gastric emptying rate and gastric juice production.
Research team: Dr. Paul Smeets (WUR), Elise van Eijnatten, MSc (WUR), Dr. Guido Camps, MVD (WUR), Dr. Maureen van Eijnatten (TU/e), Dr.ir. Wilbert Bartels (UMCU), Jan Monkelbaan, MD (UMCU), Dr. Duco Veen (UU)
Amount: € 40.000
PROMISE
PRediction of Outcome with Machine Learning in Infants: a Synergistic Exploration
At the neonatal intensive care unit of the UMC Utrecht, up to 80 newborns per year are born extremely preterm (<28 weeks of gestation), with high risk for brain damage and long-term consequences such as behavioral impairment. There is a need for early personalized prognosis of behavioral outcomes for the individual patient.
This research project aims to develop a prediction model for preterm born infants at high risk of behavioural impairment. And to ensure that this is conform state-of-the-art values of trustworthy AI. Once validated, this prediction model will be made accessible to a larger network of clinicians through a web-based service.
Research team: Asst. Prof. Maria Luisa Tataranno (UMCU), Prof. Manon Benders (UMCU), Dr. Bauke van der Velde (UMCU), Drs. Bob Walraad (UMCU), Prof. Chantal Kemner (UU), Prof. Albert Salah (UU), Prof. Mykola Pechenizkiy (TU/e), Prof. Rick Bezemer (TU/e), Assc. Prof. Clara Belzer (WUR)
Amount: € 40.000
Trustworthy AI for MRI safety and conductivity mapping as novel cancer biomarker
Quantitative MRI (qMRI) techniques are the foundation of a paradigm change in MRI imaging, as they provide objective tissue parameters to be used as imaging biomarkers. This projects focusses on tissue conductivity, a property of biological tissues measurable with MRI. This property has the potential to distinguish healthy tissue from pathological tissue (for example cancerous tissues) and may allow non-invasive assessment of cellular viability in response to therapy much earlier than clinical, qualitative MRI methods. This conductivity mapping helps with better and objective characterization of pathologies. Furthermore it results in shorter, thus cheaper MRI examinations; as few qMRI images may be sufficient in diagnostic settings instead of long qualitative MRI exams. It also facilitates precise and personalized treatments.
Research team: Dr. Stefano Mandija (UMCU), Prof. Nico van den Berg (UMCU), Ir. Ettore Flavio Meliado (UMCU), Dr. Alexander Raaijmakers (TU/e), Dr. Ir. Riccardo Levato (UU)
Amount: € 20.000
Four projects, from the Circular Society and i4PH working groups also received funding:
AI@HomeCare: Preventing adverse patient outcomes in home care nursing through predictive process
“Savor the Flavor”: towards a better understanding of the link between sensory profiles of older adults with olfactory dysfunction and diet quality, to prevent non-communicable diseases”
TakePart: An AI-driven Digital Twin Platform for Circular Green Infrastructure
Learning-based Design and Control of Green energy System for Vertical Farm Production