Artificial intelligence will calculate for different subjects the odds of developing severe forms of Covid 19.
This is the goal of AI-SCoRE project (acronym of Artificial Intelligence – Sars Covid Risk Evaluation) created by professors Carlo Tacchetti and Antonio Esposito, both teachers of the Vita-Salute San Raffaele University, respectively director and vice-director of the Experimental Imaging Center of the IRCCS San Raffaele Hospital in Milan .
The project was developed in collaboration with two global information technology giants such as Microsoft and Nvidia, with the Omics Sciences Center of the IRCCS San Raffaele Hospital directed by Dr. Giovanni Tonon.
The development team also includes Orobix srl, a company active in the engineering, production and governance of AI systems with decades of international experience in the healthcare field, and Porini, Microsoft’s center of excellence and international partner on Cloud Azure platforms and solutions of Advanced Analytics.
Probability calculation for severe forms of Covid-19
It is an autonomous learning platform capable of calculating for each individual – based on a series of clinical and diagnostic indicators – the probability of developing the more severe forms of Covid-19thus allowing targeted and timely health interventions and reducing the impact on the health system.
The data collection of over 2000 patients – recruited from San Raffaele Hospital, Bolognini Hospital of Seriate and Monzino Cardiology Center – has already started, as well as the construction of the software infrastructure on which the algorithm will rest.
AI-SCoRE will not only allow you to face in a way Phase 2 is more efficient and effective of the Covid-19 pandemic, but could have implications in many other contexts where stratification of health risk is critical, including epidemics and pandemics in the near future.
“Up to now we have been unable to correctly identify the most fragile people among the patients with the first symptoms of the disease,” explains Carlo Tacchetti, coordinator of the project. “We want to be able to do it precisely and quickly, because only in this way will we be able to understand who the subjects are that, once infected, need timely treatment, even in the absence of serious symptoms. Obviously, our dream is to push beyond this potential and take advantage of this opportunity to develop transversal algorithms capable of identifying the subjects most at risk also in the general population, and not only in those with suspicion Covid-19. ”
Objective and phases of the AI-ScoRE project
The new coronavirus is highly contagious. However, despite the widespread spread of the virus and its ability to bring entire healthcare systems to their knees, only a small percentage of patients – around 5-10% – develop the most serious (and sometimes fatal) forms of the disease. Often these forms have a rapid and unpredictable course that lead the patient to go from a mild symptomatology to a serious respiratory insufficiency in a very short time.
The aim of the project is twofold: on the one hand to recognize in the general population the people at greatest risk of developing severe forms of Covid-19 if infected with the virus, those to be protected the most; on the other, to recognize among patients who show the first symptoms of Covid-19 those who will have the worst prognosis.
The project will start from this second objective, with an AI algorithm that will integrate diagnostic images, clinical and laboratory parameters, inflammatory status, and genetic profile of the patient and the virus.
Three phases of the AI-Score project
The project has three main phases: a first phase of data collection and homogenization of over 2000 patients hospitalized in the past weeks and whose prognosis is known; a second phase of development and implementation of the algorithm, which will be “trained” to learn how to combine them in an “intelligent” way to predict the risk of the individual patient, and a third phase of testing and validation of the product on a second cohort of patients and in any prospective studies.