To help in the battle against COVID-19, a novel compact CT scanner will be developed to rapidly screen for disease patterns and monitor disease progression. This is done by developing a new type of compact mobile CT scanner, equipped with artificial intelligence software to detect disease patterns accurately.
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.
COVID-19 is taking a huge toll among healthcare workers worldwide: researchers are warning that the coronavirus pandemic could inflict posttraumatic stress disorder on an unprecedented global scale. Here, the aim is to implement a novel treatment strategy – that yields long-term cure within a single treatment-session – for traumatised healthcare workers during COVID-19.
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.
Photon attenuation correction methods for PET-MRI are currently developed using CT transmission images obtained prior to the PET-MRI scan. Their accuracy is limited by differences due to patient movement. A transmission scan mechanism will be integrated into the PET-MRI system, capable of acquiring transmission images simultaneously during the PET-MRI scan.
The application of simultaneous MRI-PET in the planning of (radio)therapy is currently hampered by the concessions done in the design of current MRI-PET scanners. In this project, a prototype MRI-PET system was designed and built which delivers improved PET sensitivity, spatial resolution, and bore diameter without compromising MRI performance.
To improve individual healthcare artificial intelligence will be used for objective quantitation of patient imaging and pathology outcomes. Subsequently these algorithms can be validated and integrated into a tumor dashboard setting to support multidisciplinary clinical decision making.
To treat or not to treat with adjuvant chemotherapy following surgery is a key question for stage II and III colon cancer patients. This project aims to better guide this decision by extensive DNA mutation profiling of colon cancer tissue as prognostic (disease recurrence) and predictive (treatment response) biomarker.
Artificial intelligence is currently revolutionising many fields of research, and it is proposed to use it to drastically reduce the number of animals used for medical experimentation. This will be done by developing artificial intelligence methods to recognise automatically anatomical structures in animals, which is important in many disease models.
To advance the analysis of multiple data of different kinds from patients with cancer, CCA develops a professional and advanced high-performance computational analysis platform in collaboration with SAS, a market leader in the field of data analytics with strong expertise in the development of novel algorithms for data analysis.