Previous work shows evidence that cerebrospinal fluid (CSF) plays a crucial role in brain waste clearance processes, and that altered flow patterns are associated with various diseases of the central nervous system. In this study, we investigate the potential of deep learning to predict the distribution in human brain of a gadolinium-based CSF contrast agent (tracer) administered intrathecal. For this, T1-weighted magnetic resonance imaging (MRI) scans taken at multiple time points before and after intrathecal injection were utilized. We propose a U-net-based supervised learning model to predict pixel-wise signal increases at their peak after 24 hours. Its performance is evaluated based on different tracer distribution stages provided during training, including predictions from baseline scans taken before injection. Our findings indicate that using imaging data from just the first two hours post-injection for training yields tracer flow predictions comparable to those trained with additional later-stage scans. The model was further validated by comparing ventricular reflux gradings provided by neuroradiologists, and inter-rater grading among medical experts and the model showed excellent agreement. Our results demonstrate the potential of deep learning-based methods for CSF flow prediction, suggesting that fewer MRI scans could be sufficient for clinical analysis, which might significantly improve clinical efficiency, patient well-being, and lower healthcare costs.

The high seas are probably the most lawless place left on Earth. They're a portal back in time to the way the world looked for most of our history: fierce and open competition for resources and contested territories. Pirating continues to be a way to make a living. It's not a complete free-for-all--most countries require registration of fishing vessels and enforce environmental protocols. Cooperative agreements between countries oversee fisheries in international waters.

Some of Norway's largest companies are joining forces in establishing a national powerhouse for artificial intelligence. Its aim is to improve the quality and capacity for research, education and innovation in the field. Norway has a huge potential to be a pioneer in Artificial Intelligence (AI), but it needs resources and collaboration in order not to lag behind. To strengthen national efforts on artificial intelligence, Telenor, NTNU and SINTEF are inviting Norwegian businesses to partner on the new Norwegian Open AI Lab. While the Norwegian Open AI Lab will develop solutions specific to the partners' industries, it will also consider opportunities where Norway can take positions internationally.