Deep Learning at Hyperfine Research
Transcript of AI in Action Podcast

Making MRI's smarter

Deep Learning at Hyperfine Research

As soon as Hyperfine was getting the first scans, I started building out the machine learning competency. We now have a strong team and are building algorithms that improve image quality and run on the scanner and tools for image interpretation that run in the cloud. I am passionate to work on hard and complex problems and there is no lack of those at Hyperfine.

Transcript of the AI in Action podcast with host JP Valentine (JP) and guest Michal Sofka (MS).

JP 0:31

You’re listening to AI in Action on your host JP Valentine. Our guest today is Michal Sofka. Michal leads the deep learning team at Hyperfine Research. Michal, welcome to the show.

MS 0:44

Thank you, JP, happy to be here.

JP 0:46

That’s our pleasure. Michal, let’s start with the background of yourself, how you first got involved in technology, what your interests were, then talk us through some of the roles you’ve held along the way leading us up to your current position with Hyperfine.

MS 1:01 - Michal’s tech journey

I grew up in Czech Republic and came to the US for grad school. My PhD at the Rensselaer Polytechnic Institute focused on machine learning for various retinal image analysis tasks, and a few problems in Computer Aided detection. For example, comparing tumors in lung CT scans. I then went to work at Siemens Corporate Research based in Princeton, New Jersey. It houses about 220 scientists and engineers who are focused on researching and developing emerging technologies, with applications ranging from health care and communications to automation and security. And I personally joined a team that was pioneering machine learning tools for diagnosis and treatment planning. And over there I worked on many many projects including automated measurements in fetal ultrasound, detecting and finding outlines of anatomical structures in CT scans and building software tools for total knee replacement surgery. And after that, when I was looking for my next adventure, many were advising me to do something orthogonal. So I joined a newly acquired network security startup team and worked at Cisco for two years. And my main projects were about machine learning tools for threat defense. And I then found out about the collection of startups in 4Catalyzer, and I was immediately hooked. I joined about four years ago and initially worked on Butterfly Network projects for handheld ultrasound, to improve image acquisition and interpretation. And as soon as Hyperfine was getting the first scans, I started building out machine learning competency in the company. And we now have a strong team. And we’re building algorithms that improve images, and run on the scanner and also tools for image interpretation that run in the cloud. I’m really passionate to work on hard and complex problems, and there’s definitely no lack of those in Hyperfine.

JP 3:16

Excellent. Well, thank you for that overview. It’s really helpful to understand your journey. So leading us now to your current role of Hyperfine, if you could give us an overview of who Hyperfine are and then give us some insight into the sets of technologies that you’re currently working with.

MS 3:34 - Hyperfine’s mission to make MRIs more accessible

Hyperfine is a privately held company founded by Jonathan Rothberg in 2014. And the company is on a mission to make MRI accessible to every patient, regardless of income or resources like simply anywhere and anytime. MRI is really truly a technological marvel, but remains broadly accessible. Nearly 90 percent of the world has no access to them at all. Let me give you some examples. Japan has 52 scanners per million population. The USA has 37. But Canada only nine and Israel five, and we go to India it’s 0.1 scanners per million population. Considering the developing world, it gets even worse. Uganda has four MRI scanners and a population of 43 million so that’s one MRI for 10 million people. Hyperfine’s point-of-care MRI, that represents multiple innovations in the MRI design, architecture and the workflow, has been filed in more than hundred patents issued or currently pending. And the system itself is highly portable and wheels directly to the patient’s bedside. It plugs into an electrical wall outlet and is controlled via a wireless tablet such as an Apple iPad. It is a big deal since current systems require complicated installations and are lifted with a crane into a specially designed hospital section. Our AI algorithms generate high quality images they make up for the losses caused by the simplified design, the smaller magnet and then the absence of the shielded room. And AI cloud software processes the images for faster diagnosis, decision making and treatment planning.

JP 5:36

That’s amazing. So how do you guys do what you’re doing? I mean, this seems like such a massive advancement in technology. It clearly could have huge implications for the medical field, but how are you guys able to make such strides in innovation and more importantly for your role? How does AI and data play a part in this?

MS 5:59 - The role of Deep Learning in building smarter MRI systems

Our ongoing effort is to use deep learning and in two different workflows. We’re looking at a deep learning-based image reconstruction. So, this is the process of producing images of internal organs from physical measurements using sensors. By quick introduction, an MRI works by measuring the response of atomic nuclei of body tissues to high frequency radio waves, when placed in a strong magnetic field. Put simply it measures how atoms orient themselves when placed in a magnet. And the speed by which the data can be collected depends on physiological properties of tissues and hardware constraints. Typically, it takes time to do a single scan and the entire scan exam of multiple scans might take 30 minutes or more. That’s a long time. For this to be practical, but with deep learning, we can shorten this time. Or, alternatively, we can produce higher quality images using the same fixed scanning time. It’s all about this trade off. One powerful idea that we rely on is to capture less measurements, and then reduce the scanning time and reconstruct the image with the same image quality as if it were reconstructed with the full set of measurements. So this is one area and in the second area, we’re focused on, the scanner uploads data to the cloud, where it is processed by the deep learning algorithms, powering clinical applications for diagnosis and treatment planning. Our first anatomical target is the brain and we build tools to automatically measure various structures in the brain and to measure and outline abnormalities. These tools are very critical for accurate diagnosis. Many of the steps would have to be done in a manual and tedious way, which is amplified by the fact that the data is 3d. So, in a nutshell, this streamlined clinical workflow has utmost importance, especially in emergency departments, one of many environments in need of our machines. One example of where time matters is stroke. Perhaps you’ve heard the slogan time is brain in a brain was stroke, 1.9 million neurons and 7.5 miles of myelinated fibers are destroyed every minute. To put it in perspective, a brain with stroke ages three weeks every minute, so now AI tools can help outline damaged tissues and provide quantitative information to the stroke teams in a very timely manner.

JP 8:44

Great to hear about some of the potential use cases and, you know, really allows us to imagine the scope of impact This could have when it becomes more broadly adapted. What are the main challenges that you and your team face in getting this new product to market, whether it’s from the deep learning algorithms or the hardware to every hospital.

MS 9:19 - Challenges the team faces when getting the product to market

It’s really about how to carefully coordinate both hardware and software teams so that we can work in a synchronized way to build these products. Hyperfine is really created around three technological areas. It’s cloud, deep learning, and MRI device itself. And we were fortunate to attract experts from top universities in the world, and from the best engineering teams. Our mechanical, electrical and device software teams are based in Connecticut and our cloud and deep learning teams are in New York. And just to give you an idea how this works, mechanical and electrical engineers take care of the hardware components, including off the shelf and custom manufactured parts. Device software engineers take care of the platforms that run the scanner itself, and medical physicists, designing instructions to highlight different tissues and abnormalities. And then deep learning scientists and engineers reconstruct the highest quality image and build applications for clinical decision making. Cloud software engineers build our viewer and back end systems for storing, archiving, analyzing the scanner data. So there’s a great advantage of having all teams work together on the final product and our machine learning algorithms that improve image quality have access to the entire imaging pipeline. We can modify the way the measurements are obtained using the hardware, we can use various software and hardware tricks to help reconstruct better images when the patient moves. And we know what kind of interference we can expect in the hospital so that we can address it. The scanner data is stored in the cloud, it is available immediately for training new systems to further improve the algorithms for image quality improvement and for providing clinical insights. And since the scanners use differently than traditional MRI, this type of data really paves the way for new clinical applications that have not been really possible to envision so far.

JP 11:40

So it’s great to learn about the structure of the team because clearly it’s such a complex project, combining software hardware, medical expertise, so it’s good insight to learn how you guys approach in such a collaborative manner. Speaking specifically about your AI team, what have you learned in your role as the leader of this team, what’s most important to you when building a successful AI team that innovates and delivers products?

MS 12:07 - Building and positioning the AI team for success

That’s a good question. There are a number of roles needed in a highly innovative AI startup. Just to make sure that the startup has cutting edge technology and competitive advantage, but also can deliver the products to its customers. Specifically, you will need smart scientists who can think out of the box, design new algorithms to previously unsolved problems and quickly prototype them and test them. They need to know how to address complex challenges in the computational pipeline. And you cannot really find these solutions in available publications. Then you need skilled software engineers who know the latest computing services, developer tools, and cloud platforms. They know how to efficiently implement complicated pipelines that can handle large amounts of data that can scale adaptively and are flexible to accommodate new features. And then you need subject matter experts who would work with the product manager to ensure that you’re building the right tools. In healthcare, this would be a visionary clinician, who can imagine your workflows, solutions and approaches. And again, they can see how they can be applied to the current needs. This can be hard, since in some situations, your customers cannot really articulate what they need.

JP 13:45

So as you guys build the next generation of AI products and you know, particularly software products in a highly regulated healthcare environment, it’s especially challenging. Can you speak to how you guys are handling these constraints are Hyperfine?

MS 14:00 - Developing software in a regulated environment

Yes, this is our day-to-day discussion. There’s a lot of scrutiny around filing AI, machine learning software going for the FDA clearance, which seems to have intensified through although similar tools existed years ago. So let me clarify. The truth is that previous algorithms were locked prior to marketing and any changes likely require FDA review. However, not all algorithms are locked. Some of those systems being developed today can adapt over time. Even if there is extensive testing and documentation before every release, for example, after retraining the system, it would not be practical to go through another round of 510k clearance process. So the agency, the FDA, is adapting and developing a guidance such as this kind of retrain and release cycle is possible without incurring additional risks. And, and the risk is really the key word. With regulatory bodies, it’s all about keeping risks under control. The developers need to ensure that any changes to the released software will not introduce additional risks, or modify existing risks that could result in significant harm to the patient. And this is the reason why it is so challenging to introduce new self learning tools that would be adapting to the environment and the user. But this will come in future eventually.

JP 15:41

So there’s certainly a lot that you guys have already accomplished. And I encourage anyone listening to go and look at the Hyperfine product to give you a sense of the advancement comparing the hardware, costs and mobility to traditional MRI machines which would have taken up, you know, your average New York City, one bedroom apartment. So it’s amazing to see the journey. What are your common projects that you’re most excited about?

MS 16:09 - Projects I am the most excited about

I am most excited about the opportunities that that is this new imaging device will bring. So for the first time, we were able to do quick imaging easily in an emergency department, whenever there is a suspicion for a problem and the patient’s head. And we will be able to learn about diseases such as stroke in order to identify what exactly happened and when, detect what is happening at a particular time, and predict the best possible treatment. So many many interesting and impactful problems for AI. And another example, we can do imaging more frequently than before. This makes it possible to monitor patients in the ICU, for example, which is important when we want to know the progress of the head injury. Is the patient getting better or worse? And how quickly can we find out? Again, smart AI tools will make it easier to quantify and report these changes. These are the things I’m excited about and many more.

T1 scan reconstructed using linear and deep learning algorithm (work in progress).

JP 17:17

Excellent, excellent. Well, we are too. I mean, looking at the impact that Hyperfine could have the medical industry as a whole is incredible. We’re excited to see what else is coming. I want to get your take on the startup environment, particularly your thoughts on graduates and on people who are starting their career in technology. There’s a lot going on at Hyperfine. What specifically are you enjoying most about your role? And then, you’ve got a lot of experience in the AI tech community in general. How can we tell your story and at least give some insight into what’s possible within the startup environment, not just Hyperfine? What are you most excited about?

MS 18:02 - What am I most enjoying about my current role

There are a few things I’m really excited about every day. I’m surrounded by smart, very smart people, which I share the journey with and learn from. There’s really something special about his deep intellectual debate when you’re trying to get to the bottom of a difficult issue. For example, our scanner got disassembled to the bare bones a few times. And we occasionally scrutinize our algorithms and examine them line by line. So we go into really the very detail of the design. And the second thing is that we are on a very important mission to make a significant contribution to health care of humankind. And this is a risky project that corporations typically would not undertake. More than 90% of the world does not have access to MRI. Imaging is very important for diagnosing various conditions. And for example, where you have a stroke, which I mentioned a few times, a clogged vein inside the brain, and you get treated within a few hours of that happening, you may get blood thinner and may be on a path to full recovery. And yet many strokes are missed in the emergency department and having access to imaging and diagnosis tools might improve that. So people are actually dying because the strokes are missed. And the third thing I love about my job is working on super challenging problems. I have always been fascinated by scientific achievements, and positive impact and progress in technology and human lives. And really pushing the boundary of what is possible with AI today and working really, really hard problems very fulfilling for me.

JP 19:50

Excellent, well, final question for you, Michal. Clearly as you go as they continue to be successful, the organization is going to grow and we’re all very much looking forward to seeing Hyperfine’s equipment in every hospital around the world. As the organization grows, how will your data team grow? And what opportunities are there going to be for, you know, individuals listening to this, whether it’s on the machine learning side data science or, or overall within the data team.

MS 20:20 - What future looks like at Hyperfine

We have a lot to do at Hyperfine. And although we have a list of tremendous accomplishments, the path ahead of us is incredibly exciting. As we scale the company, deliver a lot of scanners to our customers and grow the team, we’re going to expand the offering both in terms of hardware as well as software. What I’m personally excited about are new machine learning and cloud services that will be driven by the device and the data we’re managing. I’m looking forward to building out this competency and seeing the impact of many different areas of healthcare. Access to frequent MRI imaging will make it possible to build databases for various patient conditions, and hopefully yield to better understanding of the diseases and new discoveries in treatment. This is the impact I’m really passionate about.

JP 21:21

Absolutely. Well, this has been an absolute pleasure. I really enjoyed learning about what you guys are doing at Hyperfine. I’m sure everyone listening will encourage the company and yourself all the success given how much of an impact they can have to the medical field. So thank you very much, Michal, this has been a great learning.

MS 21:40

Thank you, JP.

Deep Learning MRI Image Reconstruction Clinical Applications

Dialogue & Discussion