>> Narrator: From around the globe, it's theCUBE with digital coverage of AWS public sector online. (bright upbeat music) Brought to you by Amazon Web Services. >> Hi, and welcome back to theCUBE's coverage at AWS Public Sector Online, I'm your host Stu Miniman. Really excited always when we come to the AWS public sector show it's not only governments' but you've got nonprofits education, and lots of phenomenal use cases from the practitioners themselves. Really happy to welcome to the program, Dr. Sergio papa. He is a radio diagnostic specialist at the Centro Diagnostico Italiano, of course, in Italy, if you can't tell by the name there, and Dr. Papa, thank you so much for joining us. Why don't you start with a little bit, you know your role at CDI. >> Thank you for your invitation. And I am the director of the diagnostic imaging department and radiotherapy nuclear medicine. We are a very huge institution in the diagnostic area in the laboratory and diagnostic imaging, I think one of the biggest in Europe. >> Excellent. And, of course, one of the very relevant things to talk about, you have a project called Artificial Intelligent or AI for COVID. maybe explain to us a little bit about what led to this and how this what the goals are for this project. >> Yes, we, as you know, is you also are today we were imagining February, March, we're in the middle of the biggest bandemia we have ever experienced it in the past. So we were thinking about some new (mumbles) methodology to give an end for this portrait emergency (mumbles) is working from three or four years around (mumbles) in basic imaging. In particular, we are working very hard in radiomics. After if you need I can talk, I can speak about the radiomics methodology that we are using. So, we had the idea of fine radiomics method on diagnostic imagery and in particular chest X ray and with the with the purpose not to have the diagnosis for this patient, it doesn't matter for us. We were focusing on predicting the clinical outcome of this patient. And, I mean, all these, all the people already diagnosed with COVID-19 viewers where they had an X ray examination, then after we applied over this huge number of X ray examinations radiomic ometers to understand what could be the clear output of this patient. So, I mean, dividing the people going well and then people, otherwise, that we're going to averse of the illness, I mean, to critical therapy even to the death. So we were trying to we are trying to divide two groups of patients. >> Yeah, absolutely. It's so important, of course, one to have that diagnosis, to understand who needs the most treatment, making sure that, you know, hospitals can put the right resources in the right places. So really impressive to do something like machine learning on this on in a relatively short period of time from when this whole pandemic is started. Help us understand a little bit, you know, what are the underlying technologies? How does AWS have been into this whole discussion. >> Well the Support of AWS is in many different areas. The first is that we are really trying to develop a platform without AWS and that's useful for the hospitals, for the institution to store in unique imagery set, all the images can be from different institution, so we don't need any more to send the images in any way. This is the first thing that AWS can give to us. Second is the use of a machine learning all this to analyze this kind of images coming from x ray chest through AWS systems. The third I think could be an aid in the generating the structure of the reports for this patient and moreover, the identification of patterns, different patterns that we can find inside the images. This concerns the radiomics theory, I mean, inside the images, there are many, many more information than what radiologist can can get from. So, I think sector agents can help us, so, AWS can help us to detect all these kind of patterns that we want to collect for our study. And the fourth reason is for the AI that AWS can give us is to share this kind of modality with the other scientific centers of Research Sector and not only for this specific pandemia now but also in the future maybe we will have, we don't hope this but we could have the second wave of the pandemia, there are many signals so about this in China and also in Europe. So these will be useful in the future to find the circle and second wave of pandemia, and also the final reason is that we will share all our results of this study with all the scientific community, I mean, we will improve an open access model together with AWS to share this information with all the scientific community the world. >> It's wonderful that this information can be shared broadly across the community, so important for tackling, you know, this challenging pandemic. I'm curious has your unit or had you used artificial intelligence machine learning before, I'd love to just get a little bit of background on, you know, how much you've used this technology? how accessible it is to be able to leverage it for two use cases like this? >> Absolutely, because, I mean, I'm an radiologist. If I check an image in a CT scan or an X ray, I can see inside that image, the maximum that I can see is 10, 15 to the maximum different patterns, I mean, the volume, the dimension of growth, which is the way of taking contrast media or difference old wishy washout but with my eyes I can see 10, 15 different patterns. And if a machine system examine the same image, it can reach out hundreds of patterns that I cannot see. So, we can detect all these patterns in different images, we can collect this machine learning system can work on these and put together all the similar patterns. So it can divide even in different cluster and then the system has to compare all this difference casts or group or patterns with a very huge database that we built before comparing and try to understand which patterns are linked to different outcome. So we can say, okay, this image has 20, 30, 100 patterns that suggest to us the destiny of the nation will be in one specific while another lesion that for me is exactly the same with my eyes, systems will tell us there are the two lesions are really different, their destiny is really different. This is the radiomics theory and this is what we are applying in our study on X ray chest cavitation. As I said before we select only positive patient. I mean all people that is for sure they have positive to COVID and in the first X ray chest, entering the hospital, we try to evaluate from the first chest X ray what will be the real destiny of the patient better or worse, and then we can also predict, try to predict, obviously, how many intensive care beds are necessity in that institution, we can send the therapy and adjust the therapy for the the different kind, different group of patients, it could be a very big help to an institution, to an hospital especially in periods like in our March or April when every day in every hospital in northern Italy, they were entering 200 person per hospital. It was a dramatic situation. >> Excellent. One of the other things that this pandemic has done is really required some, you know, strong coordination between both public and private entities. If you could speak a little bit to that my understanding is that AWS also help support this with the donation of computational credits. I believe it's the AWS diagnostic development initiative. So help us understand, you know, how the finances and the partnerships between public and private help everyone really, you know, address this current challenge. >> Well, the support from AWS for us is very important because now we, in this way we can use a lot of computing systems much more than what you had in our institution. And moreover, I think that sharing our information without the scientific content at the end of our study, it would be very important thing to do. Now I know we are beginning to appear on our drawn as in our websites, some afford to share information about going. Our study could be really one of the most important of this. >> Great, final question I have for you, Dr. Papa, give us your ideal vision going forward. You talked a little bit about how you know the importance of this to be able to watch and be prepared for a potential wave two where else is this this research relevant and where do you see this project going forward? >> Well, our study is not as focused on pneumonia from COVID-19. But the methodology can be applied in every kind of interstitial pneumonia. I mean, this is one of the first to that, at least, this has made in radiomics to segment at one whole organ, usually in radiomics, we used to studies the single lesions or little areas, I mean, no deals or metastases or primary tumors. This is one of the first, very first important studies where the segmentation is dedicated to the whole organ, I mean, all the lung, both lung. In every patient we segmented to the right or left lung. And in order to study diffuse pathology, in this case, of pneumonia, interstitial pneumonia is very different from bacterial pneumonia. And this methodology at the end of the study will be shared with the scientific community and could be a very interesting advancement our job. >> Dr. Papa, thank you so much for joining us and thank you so much for the very important work that your organization is doing to help attack the global pandemic. >> Thank you too, thank you too. >> I'm Stu Miniman, thank you for watching theCUBE (bright upbeat music)