>> live from Boston, Massachusetts. It's the you covering your red hat. Some twenty nineteen rots. You buy bread hat. >> Well, good afternoon. Welcome back here on the Cube as we continue our coverage of the Red Hat Summit and you know, every once in a while you come across one of these fascinating topics. It's what's doing I get so excited about when we do the Cube interviews is that you never know where >> you're >> going to go, the direction you're going to take. And I think this next interview has been a fit into one of those wow interviews for you at home. Along was to minimum. I am John Walls, and we're joined by Dr Ellen Grant, who was the director of the fetal neo NATO Neuroimaging and Developmental Science Center of Boston Children's Hospital. So far, so good, right? And the professor, Radiology and pediatrics at the Harvard Medical School's Dr Grant. Thank you for joining us here on the Cube and Dr Rudolph Pienaar, who is the technical director at the F n N D. S. C. And an instructor of radiology at the Harvard Medical School. So Dr Rudolph Pienaar, thank you for joining us as well. Thank you very much. All right. Good. So we're talking about what? The Chris Project, which was technically based. Project Boston Children's Hospital. I'm going to let you take from their doctor Grant. If you would just talk about the genesis of this program, the project, what its goal, wass And now how it's been carried out. And then we'LL bring in Dr PNR after that. So if you would place >> sure, it's so The goal of the Chris Project was to bring innovated imaging, announces to the bedside to the front end where clinicians are not like high are working all the time but aren't sophisticated enough or don't have enough memory to remember how to do, you know, line code in Lenox. So this is where initially started when I was reading clinical studies and I wanted to run a complex analysis, but there was no way to do it easily. I'd have tio call up someone to log into a different computer, bring the images over again lots of conflict steps to run that analysis, and even to do any of these analysis, you have to download the program set up your environment again. Many many steps, said someone. As a physician, I would rather deal with the interpretation and understanding the meaning of those images. Then all that infrastructure steps to bring it together. So that was the genesis of Chris's trying to have a simple Windows point and click way for a physician such as myself, to be able to rapidly do something interesting and then able to show it to a clinician in a conference or in the at the bedside >> and who's working on it, then, I mean, who was supplying what kind of manpower, If you will root off of the project >> kind of in the beginning, I would say maybe one way to characterize it is that we wanted to bring this research software, which lives mostly online, ex onto a Windows world, right? So the people developing that software researchers or computational researchers who do a lot of amazing stuff with image processing. But those tools just never make it really from the research lab outside of that. And one of the reasons is because someone like Ellen might not ever want to fire paternal and typing these commands. So people working on it are all this huge population of researchers making these tools on what we try to do. What I try to help with, How do we get those tools really easily usable in excess of one and, you know, to make a difference? Obviously. So that was a genesis. I was kind of need that we had in the beginning, so it started out, really, as a bunch of scrips, shell scripts, you slight a type of couple stuff, but not so many things on gradually, with time, we try to move to the Web, and then it began to grow and then kind of from the Web stretching to the cloud. And that's kind of the trajectory in the natural. As each step moved along, more and more people kind of came in to play. >> Dr Grant, I think back, you know, I work for a very large storage company and member object storage was going to transform because we have the giant files. We need to be able to store them and manage them and hold them up. But let's talk about the patient side of things. What does this really mean? You know, we had a talk about order of magnitude that cloud can make things faster and easier. But what? What does this mean to patient care? Quality service? >> Well, I think what it means or the goal for patient care is really getting to specialized medicine or individualized medicine on to be able to not just rely on my memory as to what a normal or abnormal images or the patients I may have seen just in my institution. But can we pull together all the knowledge across multiple institutions throughout the country and use more rigorous data announces to support my memory? So I want to have these big bridal in front lobes that air there, the cloud that helped me remember things into tidies connections and not have to remind just rely on my visual gestalt memory, which is obviously going to have some flaws in it. So and if I've never seen a specific disorder, say, for example, at my institution, if they've seen it at other institutions who run these comparisons all of sudden, I made be aware of a new treatment that otherwise I may not have known about >> All right, so one of my understanding is this is tied into the mass open cloud which I've had the pleasure of talking on the program at another show back here in Boston. Talk about a little bit about you know how this is enable I mean massive amounts of data you need to make sure you get that. You know the right data and it's valuable information and to the right people, and it gets updated all the time, so give us a little bit of the inner workings. >> Exactly. So thie inner workings, That's it can be a pretty big story, but kind of the short >> story time Theo Short >> story is that if we can get data in one place, and not just from one institution, from many places, that we can start to do things that are not really possible otherwise so, that's kind of the grand vision. So we're moving along those steps on the mass Open cloud for us makes perfect sense because it's there's a academic linked to Boston University. And then there's thie, Red Hat, being one of the academic sponsors as well in that for this kind of synergy that came together really almost perfectly at the right time, as the cloud was developing as where that was moving in it as we were trying to move to the cloud. It just began to link all together. And that's very much how we got there at the moment on what we're trying to do, which is get data so that we can cause medicine. Really, it's amazing to me. In some ways there's all these amazing devices, but computational e medicine lag so far behind the rest of the industry. There's so little integration. There's so little advanced processing going on. There's so much you can do with so little effort, you could do so much. So that's part of the >> vision as well. So help me out here a little bit, Yeah, I mean, maybe it before and after. Let's look at the situation may be clinically speaking here, where a finding or a revelation that you developed is now possible where it wasn't before and kind of what those consequences might have been. And then maybe, how the result has changed now. So maybe that would help paint up a practical picture of what we're talking about. >> I could use one example we're working on, but we haven't got fully to the clouds. All of these things are in their infancy because we still have to deal with the encryption part, which is a work in progress. But for example, we have mind our clinical databases to get examples of normal images and using that I can run comparisons of a case. It comes up to say whether this looks normal or abnormal sweat flags. The condition is to whether it's normal or abnormal, and that helps when there's trainees are people, not is experienced in reading those kinds of images. So again we're at the very beginnings of this. It's one set of pictures. There's many sets of pictures that we get, so there's a long road to get to fully female type are characterized anyone brain. But we're starting at the beginning those steps to very to digitally characterize each brain so we can then start to run. Comparisons against large libraries of other normals are large libraries of genetic disorders and start to match them up. And >> this is insecure. You working in fetal neural imaging as well. So you're saying you could take a an image of ah baby in a mother's womb and many hundreds thousands, whatever it is and you developed this basically a catalogue of what a healthy brain might look like. And now you're offering an opportunity to take a image here on early May of twenty nineteen. And compared to that catalogue, look and determine whether might be anabel normality that otherwise could have been spotted before. >> Correct and put a number to that in terms of a similarity value our probability values so that it's not just Mia's a collision, say Well, I think it's a little abnormal because it is hard to interpret that in terms of how severe is the spectrum of normal. How how? Sure you. So we put all these dated together. We can start to get more predictive value because we couldn't follow more kids and understand if it's that a a sima that too similar what's most likely disorder? What's the best treatment? So it gives you better FINA typing of the disorders that appear early and fetal life, some of which are linked to we think he treated, say, for example, with upcoming gene therapies and other nutritional intervention so we could do this characterization early on. We hope we can identify early therapies that our target to targeted to the abnormalities we detect. >> So intervene well ahead of time. Absolutely. >> I don't know. The other thing is, I mean Ellen has often times said how many images she looks at in the day on other radiologist, and it's it's amazing. It's she said, the number hundred thousand one point so you can imagine the human fatigue, right? So it Matt, imagine if you could do a quick pre processing on just flag ones that really are abnormal by you know they could be grossly abnormal. But at least let's get those on the top of the queue when you can look at it when you are much more able to, you know, think, think, think these things through. So there's one good reason of having these things sitting on an automated system. Stay out of the cloud over it might be >> Where are we with the roll out of this? This and kind of expansion toe, maybe other partners. >> So a lot of stuff has been happening over the last year. I mean, the the entire platform is still, I would say, somewhat prototypical, but we have a ll the pipelines kind of connected, so data can flow from a place like the hospital flowed to the cloud. Of course, this is all you know, protected and encrypted on the cloud weaken Do kind of weaken. Do any analysis we want to do Provided the analysis already exists, we can get the results back. Two definition we have the interface is the weapon to faces built their growing. So you can at this point, almost run the entire system without ever touching a command line. A year ago, it was partially there. A year ago, you had to use a command line. Now you don't have to. Next year will be even more streamlined. So this is the way it's moving right now and was great for me personally. About the cloud as well is that it's not just here in Boston where you, Khun benefit from using these technologies, you know, for the price of a cellphone on DH cell signal. You can use this kind of technology anywhere. You could be in the bush in Africa for argument's sake, and you can have access to these libraries of databases imaging that might exist. You, khun compare Images are collected wherever it might be just for the price of connecting to the Internet. >> You just need a broadband connection >> just right. Just exactly. >> Sometimes when you think about again about you know, we've talked about mobile technology five g coming on as it is here in the U. S. Rural health care leveling that and Third World, I was thinking more along the lines of here in the States and with some memories that just don't have access to the kind of, like, obviously platinum carry you get here in the Boston area. But all those possibilities would exist or could exist based on the findings that you're getting right now with Chris Project. So >> where does the Chris project go from here? >> Well, what we'd like to do is get more hospitals on board, uh, thinking pediatrics, we have a lot of challenge because there are so many different rare disorders that it's hard to study any one of them from one hospital. So we have to work together. There's been some effort to bring together some genetic databases, but we really need to being also the imaging bait databases together. So hopefully we can start to get a consortium of some of the pediatric hospitals working together. We need that also because normal for normal, you need to know the gender, the age, the thie ethnicity. You know, so many demographics that are nice to characterize what normal is. So if we all work together, we can also get a better idea of what is normal. What is normal variants. And there's a lot of other projects that are funded by N. H. Building up some of those databases as well, too. But we could put him into all into one place where we can actually now query on that. Then we could start to really do precision medicine. >> And the other thing, which we definitely are working on and I want to do, is build a community of developers around this platform because, you know, there's no way our team can write all of these tools. No, no, no, we want to. But we want everyone else who wants to make these tools very easily hop onto this platform. And that's very important to us because it's so much easier to develop to christen it just about the Amazon. There's almost no comparison. How much easier >> we'Ll Definitely theme, we hear echoing throughout Red Hat summit here is that Does that tie into, like, the open shift community? Or, you know, what is the intersection with red hat? >> It definitely does, because this is kind of the age of continue ization, which makes so many things so much easier on DH. This platform that we've developed is all about container ization. So we want to have medical by medical or any kind of scientific developers get onto that container ization idea because when they do that and it's not that hard to do. But when you do that, then suddenly you can have your your analysis run almost anywhere. >> And that's an important part in medicine, because I run the same analysis on different computers, get different results. So the container ization concept, I think, is something that we've been after, which is a reproduce ability that anybody can run it along there, use the same container we know we're going. Same result. And that is >> critical. Yes, especially with what you're doing right, you have to have that one hundred percent certainty. Yep. Standardisation goes along, Ray. Sort of fascinating stuff. Thank you both for joining us. And good luck. You're an exciting phase, that's for sure. And we wish you all the best going forward here. Thank you so much. Thank you both. Back with more from Boston. You're watching Red Hat Summit coverage live here on the Q t.