SCAI Lab’s ‘Data For Good’ challenge at SAS hackathon

The SAS global hackathon brings together data scientists, start-ups, companies, students and freelancers from around the worldL’hackathon globale promosso da SAS riunisce data scientist, start up, aziende, studenti e freelance da tutto il mondo

Cosenza, 2 April 2021 – The SCAI Lab team is the only Italian competitor out of 100 at the HackinSAS2021in the healthcare section. The global hackathon promoted by SAS, bringing together data scientists, start-ups, companies, students and freelancers from around the world to tackle analytics, software codes, the cloud and artificial intelligence. A Data For Good competition. The SCAI Lab team, with three Data Scientists Elisabetta Corigliano, Andrea Vennera and Simone Colace, constantly guided by a SAS mentor, got involved, creating a Convolutional Neural Network solution.

“It is precisely from this creative perspective that we like to work and find solutions: technology can and must be used to improve the quality of life,” says Andrea Vennera, Data Scientist at SCAI Lab “So we decided to participate in the SAS Hackathon to offer Artificial Intelligence solutions to support the healthcare world. We want to exploit the interoperability of Python, Jupyter and SAS to implement Deep Learning algorithms for the application of computer vision, with a focus on MRI diagnostic technologies. 

Hackathons are typical challenges in the ICT world. The event organised by SAS brings together participants with various profiles, from all over the world, tackling SAS analytics, Artificial Intelligence and open-source technologies, who have the opportunity to compete and propose technology solutions and respond to global challenges posed by the 2030 Agenda goals as well as global market trends. 

“Never before has healthcare been so much the focus of attention and the realisation that it is essential to invest resources in it to save as many lives as possible. However, healthcare infrastructure is not only about intensive care, but also about early detection and prevention equipment, such as magnetic resonance imaging, which we have all heard of at least once”. This is how Elisabetta Corigliano, Data Scientist at SCAI Lab introduces the project context. “As with any technology, giant steps have been taken in the medical field in a relatively short time. Suffice it to say that the first Magnetic Resonance Image has less than 50 years of history”. The possibility of visualising soft tissue, such as nerves and muscles, as well as hard tissue, bones and cartilage, through MRI makes this examination a test of absolute relevance in many fields of medicine: oncology, orthopaedics, gastroenterology, cardiology and others. However, like any other type of diagnostic imaging, MRI is also susceptible to artefacts. The artefact is sometimes the result of a malfunction of the equipment, other times it is the consequence of behaviours or processes of the human body.

“The technology we want to improve in terms of efficiency and effectiveness,” explains Elisabetta, “is precisely that of Magnetic Resonance Imaging (MRI). With our solution, we will intervene to develop innovative techniques concerning the detection and classification of possible artefacts (predictable or not) and the qualitative optimisation of MRI images. All this is done through Deep Learning algorithms based on Convolutional Neural Networks”.

Michele de Buono, CEO of SCAI Lab, comments: “The challenge is to improve people’s well-being. The urgency is to make technology available and applicable to improve diagnosis and make it faster. Artificial Intelligence offers tools to advance research, support the transition from a hospital-based to a citizen-focused model of healthcare and help improve the efficiency of the healthcare system”.

The teams selected for the final stage by a panel of competition judges made up of a diverse mix of international leaders from different industries, will be followed by SAS mentors for more advanced development of their proposed applications. The winners will then be announced at the Virtual SAS Global Forum 2021 in May.