The last years, artificial intelligence, and more concretely, deep learning, has proved to be a very useful tool for biomedical research, medical related problems and clinical assistance. In the current situation of health emergency a massive amount of data is being produced and need to be understood using the most powerful tools available. The DEEP project is contributing to fight the COVID-19 emergency on different fronts thanks to its capacity to process huge amounts of data, to develop and share deep learning applications in a quick and easy way, and to the resources available at the project testbed. Currently, we are involved in the following initiatives:
Genetic studies
DEEP has been requested to join a project coordinated by the Institut d’Investigacions Biomèdiques de Barcelona that aims at discovering any genetic traits explaining why some people without previous pathologies get severe forms of covid-19 leading them to the ICU or even to death. The study will take genetic material (together with populational and clinical information) of 200 patients who are under 60 years old and who do not have any previous or serious chronic diseases. We want to study the difference between those patients who evolve well and those who get worse and end up in the ICU by discovering whether, at the genetic level, these latter patients have a special susceptibility. In that case this will give us an indicator of which cases are the most vulnerable and should be protected. If this indicator is found, the patients without such genetic condition could get discharged earlier and we could protect those who, besides the elderly, are likely to have serious symptoms of the disease. DEEP will provide extensive data analysis, including the development of a deep learning model that will then be published and available at our Open Catalog, and dedicated testbed resources.
X-ray images classification
Building on our image clasification module, DEEP is collaborating with the University Hospital Marqués de Valdecilla in order to develop and share a new module trained to classify chest x-ray images that will act as an assistant for the physician and will help with the patients triage. In the current state of health alarm, huge amounts of simple chest x-rays are being produced daily. Due to the saturation of the medical systems, professionals with no x-ray experience are being forced to interpret the chest images, and must systematically resort to the advice of a radiologist who is overwhelmed with consequent delay in diagnosis. Under these circumstances, a reliable automatic triage system to assist diagnosis using simple chest x-rays would greatly expedite patient management.
Although this project focuses on patients with COVID-19, the developed tools will be equally applicable to other diseases with pneumonia and will be made available at the Open Catalog.
Data science to understand confinement effectiveness
European countries have adopted strict confinement measures to fight the COVID-19 spread. The Spanish National Research Council, in cooperation with the Spanish National Microbiology Center from the Health Institute Carlos III, is using data science and computing techniques in order to understand the effectiveness of these measures in Spain. The project is following a multidisciplinary approach involving computing, demography, physics and migration experts; studying high-resolution massive data to gain insights in how mobility and social contacts have changed since the measures were enforced and how these changes are influencing the COVID-19 incidence. These data are then leveraged by computational models (based both on artificial intelligence and mechanistic models), allowing to study different scenarios towards the end of the confinement measures. In this regard, the DEEP-Hybrid-DataCloud stack is being used to develop the AI models, that will be published in the Open Catalog and served through the DEEP as a Service component.
