2021/03/03 | Research | Artificial Intelligence
Reliably predicting progression of Covid-19
The Inselspital Radiology Department and the ARTORG Medical Image Analysis group are launching the world’s first multicenter, international study on AI-assisted prediction of severe progressions of Covid-19. The SNSF funded research uses artificial intelligence to evaluate extensive clinical, image-morphological and laboratory data to provide reliable predictions as to whether a specific case would lead to a severe progression of Covid-19.
Since the first appearance of Covid-19, our knowledge regarding SARS-CoV-2 and the different manifestations of the disease caused by the virus has increased. To date, there is a lack of clear understanding of why some patients only experience mild symptoms, while others have to be treated for severe (acute or chronic), and in the worst cases lethal, progressions. These questions are now being addressed in a project (NRP-78 «COVID») funded by the Swiss National Science Foundation.
The international team from Inselspital Bern, Bern University Hospital, the University of Bern, the University of Parma (IT) and Yale University (USA) is working on a system based on artificial intelligence (AI). The AI-based approach with complex input data aims to better understand the progression of a disease. The goal of the project is a faster and more accurate prognostic grouping of COVID-19 patients based on the proposed AI system. This would enable faster and more appropriate treatment for Covid-19 patients.
AI expert and Head of the ARTORG Medical Image Analysis group Mauricio Reyes: “We need to find ways to ensure that our deep learning systems function independently of specific hospital centers and device types or methods of analysis. With our project, we are pushing forward in two directions: We are using data from three of the world’s leading Covid research centers, thus increasing the amount of data. Moreover, we are integrating CT and classic X-ray images with a multi-omics approach, thereby broadening the underlying information from a technical point of view. In this way, we hope to arrive at new, more reliable and faster predictions of a Covid progression.”