Archives for January 2019

Dr. Fankell: Arlen is much more in the energy tissue interaction world. So hitting it with lasers, hitting it with ultrasound, things like that. I’m much more in the looking at the structural mechanics’ side of things.

Editor’s Note: Doug gives a fantastic presentation. At its conclusion, a lively conversation breaks out about predictive modeling versus simulation (from Arlen Ward’s presentation the day before) in bringing your medical device to market in the most efficient and cost-effective manner.

Doug Fankell: I’m going to talk about a “new” take on prototyping that could save you millions. It’s not necessarily new. And it’s going to be following up on some of Arlen’s presentation yesterday as well.

So just to give you a little bit of background about me, I’ve sort of been all over the map for school. I did my undergrad and master’s at the University of Wyoming, and UC Berkeley in structural engineering. So I have a weird roundabout path to how I ended up in biomedical device design.

I actually worked as a structural engineer for about a year and a half, decided I didn’t like it, and so I ended up at the University of Colorado in the Advanced Medical Technologies Lab working on both device design and then, more specifically, I was working on using analysis and finite element analysis to actually inform how we design these devices.

I graduated there a little over a year ago, I guess, and then started at Structural Integrity Associates where they’ve been doing finite element analysis of really hard nonlinear problems for a long time.

We have kind of 250 highly technical staff all over the country. We have some material testers, some material scientists, and they specialize in advanced nonlinear mechanics and FEA in highly regulated industries.

So they started in the nuclear power plant world which is very regulated and, since then, have expanded into different industries, such as aerospace; we’ve done a little bit with automotive, but I’m kind of their first foray into the biomed world.

They had all these tools, but they didn’t have anyone that really liked working on tissue and devices and things like that. So that’s where I came in.

So just to give you a background, I’m going to tell you a bunch of numbers that you guys probably have more understanding of and a better idea of than I do – about how much it costs to bring a device to market. And then hopefully, I’ll open your eyes on some benefits of using predictive computational modeling and then give you a couple of examples of projects that I work on that I can sort of talk about, and then just talk about where I think this is leading as well.

Background – Cost of bringing a surgical device to market

So here’s a general study that was done by Stanford in 2010 where they say it costs an average of $31 million to take a 510(k) product to market from initial concept, and an average of $94 million to take a PMA product to market.

So if we can reduce these numbers, I’m sure people would be very happy. And so where I’m really going to talk about how you can use computational modeling to help this is in the concept development and proof of concept areas, and then also how we can use it to inform our clinical tests and things like that.

Let’s make experiments better; let’s make our tests better so we actually know what we’re looking for before we spend all this money and then realize we have to reduce that.

Background – Time to bring a product to market

This talks about the time it takes to do this. So you see, on average, it takes a 510(k) product about 20 months just in that concept development and proof of concept phase.

I know that’s pretty widely varying depending on the type of product you have and what you’re doing, but just to give you a general idea. We’re going to hopefully help you reduce that time.

Background – Estimated costs of delays for a 30-person company

And then lastly, it comes down to money. Estimated costs of delays for a 30-person company, so eight-week delay. Sheldon et al. estimates that it costs a company about $1.8 million; 20 animal study, $5.5 million. I think that was for a year of work as well and a year of delay, and same with the additional hundred patients’ study.

So these are big numbers. So if you can avoid doing additional studies and reduce your amount of delays– I’m not exactly reinventing the world. That’s what everyone’s been talking about throughout this whole conference – let’s do things better to save money and make better products.

How Advanced Predictive Computational Modeling Can Help

So how can we use predictive computational modeling to help us? Well, you can reduce your time to market. You can speed up the design iteration process, so you can do more iterations. You can build a better device.

Again, I think there was a talk earlier today that was talking about, we need to get back to focusing on people and focusing on what’s our ultimate goal when we’re designing these medical devices.

We want to help the patient. We want to build better products, and a byproduct of that is hopefully we’ll also be making more money with these better products.

You can foresee potential errors that you might not have thought of as obvious before you actually are building these devices, and reduce experimental testing, and then also improve what you’re looking for in these tests, improve how you conduct these tests and the likelihood for FDA approval.

My boss asked me to summarize this in one concise statement. And it’s “I want to help produce safer, more effective products faster.” That’s the end goal. I know that’s very general but, again, I think if we don’t look at the end goal, we are going to get bogged down in the details and lose that.

Predictive Modeling

Arlen touched on this yesterday. Until recently, modeling of medical devices, I felt like it fit into one of two categories.

• We’ve either looked at the device itself – but then how do you model it in vitro? With all the soft tissue that’s acting on it, how do you actually model those boundary conditions? That’s really hard. And then what’s it doing to the body? You’re putting something foreign into the body;
• or we’ve looked at the tissue itself.

A lot of people have struggled at actually combining those two – looking at how medical devices interact with the tissue and how they actually work. So that’s what my PhD was focused on, and then also what I’ve been working to do now.

So predictive modeling, now it not only models the device but also the physics within the tissue when it’s acted on by the device.

So this could be simple like:

• Deformation. If you have a needle and you’re poking it through the skin, what’s going on in the skin? What stresses do you need to reach to push it through the skin?
• Fluid flow through the biological tissue. My thesis was developing a thermoporomechanics model of biological tissue I won’t bore you with. That’s a lot of math. If you ever want to see it, I can send it to you. It was like 2000 equations or something in my thesis.
• Within the biological tissue. We can look at temperature, chemical reaction, tissue remodeling within the biological tissue.

And honestly, most of the time, we have to do many of these things and explain how they affect one another.

The Design Iteration Process

So again, this is a very simplified slide but I think it really hammers home the point of – this could be a device; this could be even just a little portion of your device – “You have some need that you have to meet, right? You come up with ideas of how you’re going to solve that need, you design a prototype.” Only, most of the time, I swear I spend half of my time in SolidWorks.

But you come up with the design, you come up with ideas for this design, then you produce. You’d say, “Okay, this is the best one. We want to produce this prototype.” Or maybe you come up with a simple prototype of three different designs, then you test it, and you go through this loop over and over and over again until you get down to the design that works the best in what you want.

Well, I’m proposing that we want to speed up that design. So you still have your need; you come up with a bunch of ideas; you prototype this digitally in SolidWorks or in whatever you feel like you want to prototype it in; and then we simulate the design performance.

Now, this could be as simple as doing stuff that we already have the tools to do. Or maybe we have to do some laboratory benchtop testing to validate the computational models.

A lot of the times, even just doing simple computational models, you discover new needs or you’re like, “Oh, this might not actually do what we want it to do.” So then you go back, come up with new ideas, come up with new designs, but you’re not having to build any physical prototypes and take that time to build those prototypes.

From that, you can also say, “Well, this one looks like it is the best design.” But now, instead of coming up with 10 designs and then narrowing it down to one, maybe you can come up with 100 or you can do an optimization study and narrow down on what you think the best design, produce that, test that. And then lastly, you get additional experimental input or you can look at what you might really want to focus on with your testing. It just helps inform that testing as well.

Example I – Arterial Cutting and Fusion

So now, I’m going to take you through a couple of examples because this is all great theory, but unless you actually see how it’s applied, it doesn’t really matter. Right?

So during my Ph.D., ConMed Electrosurgery was really interested in developing a model to inform and speed up their design process. So this was taken a while and it was of their tissue fusion devices which they used to both cut and fuse arteries.

And the goal was to develop a predictive FEA model – we knew we were going to use experimental data, but they didn’t want to use a ton of experimental data. And most of the data that we used was stuff that they were collecting anyways as they went along – and then use that to inform device design.

So I came up with a model. This I ran on my desktop computer with six gigs of RAM. It was back four or five years ago.

And this is kind of what it looks like. The top, you’re looking at the Isochoric Strain Energy, which is a fancy way of comparing things to stress and strain. It was a more informative value, I guess, and then temperature at the bottom.

And from this and then from a little bit more experimental testing, we were able to actually take these terms and come up with a way of measuring the damage in the tissue. And then actually I wrote a little user subroutine that modeled how the tissue was mechanically being damaged.

And we didn’t care if the cells were staying alive, we just cared if there was actually a mechanical strength left in the tissue or if it was being cut all the way through.

And we were able to predict pretty well. This is my favorite picture. I always have to put this caveat of “Not all the pictures lined up exactly like this,” but this test actually ended up doing that. We were able to say, “We’re going to predict the outcome of this surgery.” We were able to do it almost 90% of the time. And we were also able to put bounds on “you have to press this hard with this device, and you have to get it this hot, or you’re not going to see cutting or you might only see it 50% of the time.”

And we ran a whole parametric study on different artery sizes, on different temperatures and different pressures, and all kinds of different things that really provided some insight.

You see on the right side there, there’s sort of this little ridge. They wanted to get rid of that ridge, and I ran several simulations and said, “If you get rid of that ridge, you’re not going to be able to cut the tissue.” And one of the more experienced people there was like, “Oh, yeah. 30 years ago, we tried to do this without that and they were like, ‘No, we couldn’t cut the tissue.’” So little insights like that into the job design are pretty interesting.

So, really, these FEA models were used to iterate the jaw shape and the control parameters. We could run tens or hundreds of optimization simulations in the time it took them to build one physical prototype of these jaws.

And then we were able to do parametric studies. And there were still some upfront V&V experimental tests, but they were doing these anyway. But now they have these tools and this understanding of the tissue to where they just have to go back and validate their models. That’s a big thing in the nuclear world.

We have lots of good ways of validating models because they don’t really let you test anything nuclear anymore [laughs]. Or it’s very small scale. So just the statistics behind that as well.

Example II – Tubes

And then another example. I can’t talk too much about this one. That first one was more of a big picture – how does this affect our whole device. This was just a very simple part of their design process but it was causing pretty significant delays.

They needed a composite tube casing, and they needed special holes within the tube in order to house their tooling. And this casing needed to be able to be pushed through an artery, but if it crimped at all, it just damaged their tools pretty significantly.

And it was taking them six to eight weeks to get these special custom tubes. So they’d have to get the tube, they’d go test it in the lab, be like “Oh, it’s not as stiff as we need,” or, “It’s too flexible. It’s not what we need,” as they’re trying to push it through some pulsing arteries.

Including the initial benchmarking of the material which they had already done – they’d gone through several iterations of this – we were able to come up with numerous design ideas and run simulations of them in the amount of time it took them to get one. Or a lot of the time, my colleague over there said that they would order 10 different types because it was taking six to eight weeks for them to get these tubes in the [case?? 00:13:04].

So this is just one very small part of a design that was causing pretty significant delays in them moving forward with producing their product, where it was something we could really help them out with and say, “Yes, let’s run through several iterations on a computer and come up with the best design. And then you can have that produced instead of having to wait six weeks just to get a product that wasn’t working how you wanted it to do.”

Summary

So just summarizing everything, what can we do with predictive computational modeling? Well, we can come up with faster design; you can iterate more quickly; you can iterate more so you can converge on a better solution, and you get additional insight into the performance. Then you get a better design product. You hopefully see better performance during testing, and you see less testing or what you need of less testing. And then lastly, hopefully, you get a better product; it gets to market faster; and it costs less.

That’s the ideal world. And this is just one way that we’re helping companies do that.

Future

And then lastly, looking at the future, Arlen touched on the ASME V&V 40 that’s supposedly coming out with guidelines this year.

And then, one of my favorites, he also touched on the high computing performance scene, like if you have a laptop, you can run these hundred-core simulations now. And it’s a million degrees or 10 million or 100 million degrees of freedom. And the simulation is not a problem anymore.

And then lastly, my favorite quote was from a paper that just came out from the FDA, where they say they believe computational modeling is poised to become a critical tool for accelerating regulatory decision making and adopting this will be essential for advancing their mission.

So that’s speaking to where this field is going and how important being able to use these tools effectively is. And with that, I’d love to answer any questions.

Joe Hage: I’ll start. Dr. Fankell?

Doug Fankell: Mhm?

Joe Hage: Respect, man.
[laughter]

Most of what you said went over my head, but that’s when I know it’s really good. And I never really thought about the parallels between the structural work that you used to do and the structural work that goes into making a medical device. It’s just, I don’t think everyone in TV Land, certainly I, would have made that intuition.

You referred to Dr. Ward’s work yesterday. And both of you are on different planes as it relates to simulating. He talked a lot about as it relates to clinical trials and animal testing, and you talked about it from another angle. Where do you see your work overlapping and where is it quite different?

Doug Fankell: I would say mostly, it’s different in our expertise. So we both got our Ph.D. looking at these vessel-sealing devices.

It was kind of funny, but I’ve been meeting with him. We’re both in Denver as well. But we hadn’t met until about six months ago.

Joe Hage: Oh, you knew one another.

Doug Fankell: Yes, we do.

Joe Hage: Okay. Go over one another’s houses at all or…?

Doug Fankell: [laughs] We have coffee every now and then.

But he’s much more in the energy tissue interaction world. So hitting it with lasers, hitting it with ultrasound, things like that. I’m much more in the looking at the structural mechanics’ side of things.

And honestly, they do overlap some. Very rarely are we looking at one physics anymore. My Ph.D. thesis was looking at the fluid flow, and the water becoming vapor, and how it was expanding the tissue, and what was going on as far as the deformations of those tissues. So there is some overlap, but I would say I’m much more of the structural mechanics’ side of thing, looking at structural damage, not necessarily thermal damage, Arlen Ward: I think the last time that we met for coffee, this was one of the things that we talked about quite a bit because it’s easy to say, “Oh, we both work in simulation so we’re natural competitors, right? People that would work with me would work with him. So one of us will be losing out between that.”

But really, in the conversation, what we realized is, there’s a lot of different areas of expertise that people outside of the simulation world may not really have an appreciation for.

And looking at it more as “we have a network of people that each are focused in different areas,” it helps when a device company comes to me and says, “We have a problem.” A good example of that is insertion forces for needles. My response to that is, “I know someone you should be talking to.” Could we do it? Yes, probably, but I know somebody that can do it a lot better than we could, and point them in that direction.

And being that connector like you and I talked about before, I think, is going to be necessary in the simulation world as it relates to medical devices because we can’t expect medical device companies to have that knowledge of the entire area.

Joe Hage: One more thing and I’ll come right to you, Tor and Hilary.

Because I don’t understand the discipline like you doctors can, I think about design and development firms as “I’m going to delegate that. They’re going to design and develop it for me.”

And this term about structural integrity and all of these things, they never even occurred to me. I don’t know, – and Torey, you are a great person to hand the mic to – should I think most design and development companies have this expertise or do they typically need to find someone like Doug or Arlen?

Tor Alden: Sure. We talked about this a little earlier. So as product development firms, we have simulators or some of the lower-end ones. We get in trouble when we start working with non-Newtonian fluids and other things.

Even though we have these capabilities and we rush to make prototypes and prototypes, – you mentioned the needles puncturing through tissue. That was one of the issues that we had – the challenge for us was really to convince the client of the importance of getting the simulation versus going into actual [?? 00:19:00] testing.

And so we ran into trouble when we actually tested on animals because after one or two needle punctures, the needle dulled slightly and the whole instrument blew up in our hand. So we had to go back to the drawing board, and the needle became the issue. But if we had earlier involvement with companies like yourselves, we probably could have avoided that.

But again, the challenge is, how do we sell your services to our clients?

Joe Hage: So I’m curious, because I know time is a premium, you want to get the market. And I guess in most cases, time is more valuable than money.

But I wonder, if I were in this business and I had to make these decisions, I have a certain cost associated with you, but I might be able to avoid it if the testing goes fine and I didn’t have the dulled needle, so I saved. But when it didn’t work out that way, how much money did it cost to do it again, not to mention the time? And would that be exactly how much he charges or a fraction of it? Do you have a perspective on that? Or you, Tor?

Doug Fankell: I would say that’s exactly the argument that I’m making. You can avoid all these delays.

I’m trying to remember who was talking yesterday where they were talking about the cost of finding out these errors later on in the process. I mean, it’s 500-fold or a thousand-fold or 100-fold. It’s much more expensive.

And then also, just coming up with these computer models, while it’s expensive, animal testing can get really expensive really fast if you’re not careful. And so I would say you can both look at it from a time aspect. I mean, I can set up 15 simulations and run them overnight. I can press go on Friday and have an answer Monday morning.

Joe Hage: Do you think it would be a fair parallel to say that hiring someone like your firm for simulations is like an insurance policy, like I’m paying a certain premium to mitigate risk?

Doug Fankell: I would say, “Yes, that’s one way of looking at it.” Maybe not necessarily the whole way of looking at it. We also can help drive innovation by being able to test your crazier design ideas and things like that.

Tor Alden: I think we actually ended up using more finite element analysis to our advantage when we started looking at the different needle tips, whether or not it was a single probe or a trilobe. As we worked through it, we let the simulation do the work. And then we were able to optimize prototype, then go back and take that prototype and optimize it again.

But it wasn’t a clear yes or no answer, where I think in our world as prototype or product developments, if you CNC something out of ABS, you’re 98% sure it’s going to work. Where in a finite element analysis, it’s a little bit more fuzzy, I think, to people that are untrained in it.

Doug Fankell: Yes. It can be–

Joe Hage: I want to follow up with Tor and ask, “You have a better sense of your competitive set. As I said immaturely a few moments ago, design and development firm, you know, they’re going to design and develop. How many design and development firms typically have functional expertise like Doug’s in-house? Would you say quarter, half or third?

Tor Alden: So I think everybody will say they have it, but I think whether or not you’re using SolidWorks simulator versus ANSYS or whatever you’re using, there’s a different level of functionality and capability.

And then it’s also who’s developing the parameters of the test to make sure the test is actually working. So yes, we can say we have it, but are we doing it efficiently and correctly?

Doug Fankell: I have a whole soapbox about SolidWorks simulation because it’s making things easy [laughter]. But as Arlen was saying yesterday, unless you’re doing a good job validating and verifying A. that it’s doing what it should be doing, and B. that it’s actually simulating the physics that’s going on in the world–

Joe Hage: The reason I’m going a little beyond time on this is I think this issue is probably something that the folks in TV Land face a lot. Because I know, as I said, I would be clueless about that.

If my design and development firm says they can do that, they can do that. So what kind of questions would I ask to even know that “well, could ya?”

Because I’m thinking – and correct me if I’m wrong – most of your clients are companies like Tor’s that do design and development instead of the manufacturer themselves because they don’t even know that they should really be asking you. Is that true?

Doug Fankell: Yes. That’s definitely been the people who have been most interested in working with us.

Joe Hage: Is that the same for you, Arlen?

Arlen Ward: Yes.

Doug Fankell: I would say asking a simple question like “how are you validating this model? How are you proving to me that the model is doing what you say it’s doing?”

That was what I spent a big portion of my thesis on. Great, I developed this crazy mathematical framework. Now, actually, is it doing what I hope it’s doing?

Joe Hage: At the end of the day, I’m going to talk a bit about medicaldevicesgroup.net – this site that I lead and I want it to be extremely useful.

Just talking with you, I’m thinking it would be great. I think everyone in this room can contribute to it. What is the compendium of questions that you didn’t even know to ask, you didn’t even know it was going to be on the test? Because I’ll meet physician entrepreneurs who have a great idea, and they’re like, you know, does hmm. And they won’t know to ask these questions in the first place.

If we could put together a resource of– It’ll be exhausting and it’ll be intimidating, but if you show it to the right people, did you ask these questions? If you have electricity running through it, did you ask these questions? Does it have IoT? Put it. How will it interact? All these things.

So, a group effort is really the spirit of the community that I attempted to build. And Hillary, you have a question?

Hillary Sweet: Yes.

So I’m just wondering if you have anyone that comes to you, not on the large manufacturing side, but with an idea of “we have this problem and we don’t know where to start?” Do you have anyone who comes to you and says, “What material or what thickness?”

I’m thinking of a particular application with someone who’s working on a urethral stent that continues to clog. I’m just thinking this could be really helpful for something like that.

Doug Fankell: Oh, definitely. We definitely have had some of that. You get people that are again, like you said, almost sort of clueless – I have this idea, but I have no idea what to do.

I used to deal with it. I’m an adjunct advisor of a grad design team, and they came up to me and they were like, “Oh, we have this great idea and this great design.” And I looked at it. I was like, “Well, you have to make that three times thicker, otherwise it’s just going to break every single time you even–“

Just little things like that.

Joe Hage: Were they like, “What do you mean?”

Doug Fankell: Yes, exactly. And I asked them, “You are engineers. Have you done any mechanical analysis on this?” And they were like, “Oh, no.” I was like, “Don’t be scared of equations. It’s okay.”

Joe Hage: Well, you have 2000 in your paper. So where can we read 2000 equations in case we—

Doug Fankell: Uhm… I can—

Joe Hage: I’m joking. I have no interest whatsoever in reading that document.
[laughter]

Ladies and gentlemen, Doug Fankell. Thank you very, very much.

Doug Fankell: Thank you.
[applause]

Dr. Ward : What can we do besides standard-issue testing an animal labs to get answers in a more cost effective manner?

Anyone that has ever built or designed a device from the ground up know, when it’s a sketch on a napkin, it’s cheap to change things. When it’s production tooling, that’s when people start to cry.

If it’s 1x in the concept phase, by the time you get to the production and test side of things, it’s 500 to 1000 times what the cost was in the beginning. So you do simulation work first.

Okay, so everybody wants to foster innovation, they want to create innovation, they want to bring innovative devices to market they want to do innovative things. And there’s lots and lots of theories about how to do that, right?

Every device company I know of talks about innovation in one way or another, from their mission statement all the way through to every corporate meeting they have in their R&D department, because they have the most innovative people and they come up with all these great devices. And they, every single one of them, changes the world, even if it is a laparoscopic device that we change the shaft length by an inch and a half. And it’s the most innovative thing that they’ve ever seen.

Not that I’ve ever been a part of those projects.

But here’s the debate, because everybody says, everybody knows, these are all self-evident things. So if you want to be innovative, half of the world says, you have to “Fail Fast.” In fact, you have to “Fail Often.” They write books about these things.

And I had to pick this one because it has the arrows and whatnot. But there’s probably three dozen books out there on innovation and failing fast, even Mark Zuckerberg is famous for the line, you know, move fast and break things at Facebook. And that’s what they credit with a success and things like that.

The other half of the camp is that none of that works. That’s just complete garbage. And you need a plan. And it’s not that we want failure, both of these people, both people are people in both of these camps, what they want is answers, right?

So the “Fail fast and fail often” crowd is “go try it and get an answer.” And the “All that failure stuff is garbage crowd” is just go get the answer.

Everybody’s after just getting that answer. And the way you get that is through testing things and trying things out and analyzing things. And using all those engineering skills that we’ve been talking about today

The problem is the budget.

If I say I want to try 300 different tests, nobody’s budget really will sustain that. You know, the product development budgets are smaller and smaller and smaller, you’re expected to do things faster and faster and faster.

And the small text over here in the corner says, “Pre-clinical data collection costs go up about 15% a year” – that’s independent of any changes to your product launch schedule.

So if you are a company that puts out things on a regular basis, you can expect those costs to go up 15% every year. And it’s not because only not only because those tests get more expensive.

It’s because the regulatory requirements, the questions that are asked at the FDA, or the EU, or the fringe cases that people are interested in, those are all things that have to be investigated. And so those costs go up.

So if you have a two year device development process, where you have a budget for your pre-clinical testing, by the time you get towards the end, where you’re burning most of that money, you’re off by about 30% or a little bit more than that.

What’s the alternative to standard issue testing in animal labs?

The question I wanted to talk to you about today was, you know, initially, no surprise, based on our conversation this morning, is, what else can we do besides the standard issue testing an animal labs that might get us those answers in a more cost effective manner? We’ve talked before about how much faster it might be. But really, at this point, we’re looking at the cost. And what do we save by looking at these in a different way.

So we want to address these changes as early as possible, right? So as everybody that has ever built a device, or has designed a device from the ground up, when it’s a sketch on a napkin, it’s really cheap to change things. When it’s production tooling, that’s when people start to cry when you tell them that they that they need to change something about their design.

And that’s where this line here in the middle comes from, the cost to extract defects. You know, if it’s 1x in the concept phase, by the time you get to the production and test side of things, it’s 500 to 1000 times what the cost was in the beginning.

And if you wait until you start testing things, which is in the production and test phase, where it’s literally called out right there. That’s an expensive time to start answering questions about about whether your device does what it’s supposed to do, and whether you really want to be making those design changes.

So we do simulation work again, that was the the hot seat question this morning, where Joe and I got a chance to chat.

And as a company, what we look at is using tissue testing as part of your development. We’re certainly we’re not against using tissue testing. In fact, it’s it’s definitely a requirement around understanding how your device works. But if there is a way that you can answer those questions that doesn’t involve the variability of tissue, you know, certainly worth investigating from the problem with invention vivo testing.

The problem with those is they’re time consuming, expensive and difficult. And the difficulty comes in the fact that the tissue is just not the same.

The more control we have over technology in that energy tissue interaction space, we’re looking at control of that energy. You can control lasers and electrical energy in ways today that was unheard of 25 years ago. Control systems are much faster, processors are much faster, sensors are more accurate.

So the question becomes if we start to have that fine level of control, and a lot of different knobs, and the way we are designing our device, if we’re looking at that effect, in something that’s expensive and difficult, difficult and noisy as a data source, you’re going to lose a lot of those subtle effects, not because it didn’t exist, but because of what you’re using to measure it. It covers that up unless you’re looking at a very large sample sizes.

Some studies we’ve been a part of looked at the variability of forcing renal arteries, which is used a lot in bustle ceiling, there was a 30% variance in that data collection, even when controlled to the same animal, the same side, the same day, and everything else they can think of, there was still that variance in terms of performance of the device that couldn’t be accounted by anything that they could come up with on the environmental side.

So collecting data through computer simulations, the FDA and other regulatory bodies refer to that as In Silico data, or In Silico trials. They view all of these different data collection sets in the same way. They consider simulation to be a model much in the way that they consider animal testing to be a model because of a model of what they expect to happen in humans. And in fact, even clinical data is considered a model because it should reflect what’s happening in the larger population, even though they’re working with a subset.

The FDA puts those all in the category of models. We have different ways of doing this. For the In Silico side of things we are looking at what is the device, the design of the device, how does the tissue behave, the tissue, whether you’re talking about liver versus a cardiac muscle or something, those things are going to behave differently, they’re going to react differently to the heat, to force, to energy absorption, like that. And also, the way that you apply that energy, if you turn it up to 11, as the show goes, you know, things are going to vaporize in different ways.

If you, if you apply the same amount of energy over a longer period of time, you’re going to get a different effect, if you pulse it, you’re going to get a different effect. If you put in a control system where you’re getting feedback from a sensor, you’ll get a different effect, those are all things that you have to take into account for specific cases when you’re doing a simulation.

So it used to be that in order to do these types of simulations, it was really in the purview of places that had a lot of computing power. And that was, you know, places like IBM, where they had people that spent their entire careers designing new mathematical models, pushing the envelope slightly, you know, because a lot of these can get very complicated.

If you’re looking at computational fluid dynamics, you know, you could have easily have multi-million degrees of freedom problems, even just when you’re looking at the fluid flow, much less anything else that’s happening in the system.

And so that required a lot of computing power are a lot of trade-offs in terms of simplifying your model in order to get to something that you could actually calculate in a reasonable amount of time versus what it is that you needed to answer from the standpoint of the application. And it was expensive, you know, you had to have in house experts that that was their full time job. You had to have it staff that can support those large computing centers, and things like that.

And that’s really no longer the case, because Amazon and Google and places like that have made made data centers available. And there’s even commercially available companies like Rescale now where you can use cloud-based computing resources to do the processing for you. So even though it used to be that IBM was a place that did all of this, all these calculations, now, even a startup has access to this, because when we do this, these types of simulations, and you run even hundreds of processors against a problem, and it runs for six hours, Amazon is super-excited about this, because it’s not user interface. So you don’t need it answer back in sub-second kind of things. And so when you say, I don’t care when it comes back, as long as it’s not days from now, they can load balance among all of their their different data centers around the world.

And then you get your answer back. And instead of something that would run on a very powerful workstation under your desk for a month, you get an answer back in a couple of hours. And then you can look at your result before you forget what it was that you changed in the model in the first place, which is key.
[Laughter]

Doug and I can commiserate about that – you know, where something runs for a few days. And then you realize that you forgot completely what it was that you changed from the last time you run it and, have to go back into it again.

But now, that’s a thing of the past. And it puts that computing power in the, in the hands of small startups and design houses, and you need to use it on a on a intermittent basis, you don’t have to build up your own computing systems, and then maintain them even when they’re idle.

At this point, you’re only paying for the time that you’re actually using, which is surprisingly inexpensive, when Amazon started to monetize their idle processing speed.

This was something that came up last April [at the 10x Conference], when we were talking about using simulation and speed of time-to-market simulation isn’t really a one-shot. And you haven’t answered everything.

I know from the very beginning of that my time and working in simulations and medical devices, the Holy Grail that everybody would love is to be able to take their SolidWorks model and upload it into a simulation and get an answer to everything that they ever possibly might want to know about that particular device in a very short amount of time.

But that’s not how this works. At least not yet. Not yet, is I’ve been working on this for 15 years. And it’s still not yet.

Instead, what we have to look at is that particular application and add enough complexity to answer those questions. And as we can see, these those orbits around and each orbit were kind of touching base with, with the physical validation, we get a little bit further out into the complexity space. But eventually we get out where we believe our models, we have confidence in our models.

You’re eventually out there, where you’re answering the questions that you need, with a model that you believe, and not only will you believe it, but also you have the data to show it to the FDA. And they’ll believe that as well.

So when you start looking at multiple design parameters, and you even if you just have two or three options for each one, those numbers of iterations get huge in a hurry. So once you have those models that you feel like you have all those things collected, where you’re, you have the physics represented in ways that that makes sense for your application, you can turn it loose on another parameter space and get response curves like this, where you’re looking at things like, you know, maybe this is, you know, tumor ablation size versus power and time, or electrode diameter, or whatever it is that you need to get an answer to. And you can look at maybe where your math minimums are, and drive it from there.

You can also look at things like designed tolerances. So if you know your middle-of-the-road cases, right? But then your manufacturing guy says, “Well, you know, how much room do I have to work here?” You can look at those best-case, worst-case scenarios or, the worst case scenario is if they’re on either end and and look at the performance and see if you if you’re on the edge of a cliff, or if you have some room to work because that can cut down on your production costs.

There are optimizations, but I’ll show you in a second. And then on the Monte Carlo side – Monte Carlo is where you basically, instead of putting just individual values in for things like tissue properties (because we all know the distribution of the thermal conductivity tissue isn’t one value, it’s a distribution for various patients and whatnot), you can put those distributions into the simulation and look at what kind of distribution you get out on the other end of the thing that you care about. Ablation size was the example we used earlier.

On the regulatory side, even though there are increasing requirements, we can start using some of the simulation information to address things around: patient BMI, differences between disease tissue and healthy tissue, you can look at things like we validated all of this in a porcine model and here’s the simulation that matches the porcine model, but when we change the properties to match them, and this is what we expect in our in our human cases.

And the FDA is completely on board with this. There’s two things that have come out recently, in the last couple years.

The first one is the guidance document that came out in 2016 around using reporting computational modeling studies as part of your device submissions.

What’s in there is there’s a whole bunch of checks that you need to hit in order to include simulation data as part of your submission, but the spolier alert is it’s exactly what you should be doing as a good simulation person anyway, where you’re validating your model, you’re you’re verifying that your code is calculating things correctly, all the things that you should be doing anyway, as a good simulation person are things that the FDA wants to see as part of that submission as well.

And the other one is ASME V&V 40: Verification, validation of computational modeling of medical devices. That’s a standard for the ASME that is supposed to come out this year, that’s a committee meeting. And in April, it was supposed to be out in July, and I haven’t seen it yet. So hopefully before the end of the year, that should be out.

But that is less about what actual verification validation you have to do and more things along the lines of context of use, what kind of risks are you looking at, and that’s going to drive how what kind of simulation is appropriate. And some places where it’s high risk, you’re going to need to do both, the simulation and the animal studies, and, you know, answering questions like applicability and things, things along those lines,

I put this slide in pretty much every presentation I do, because it’s important, and it’s surprisingly important in medical devices.

But you have to do validation, this isn’t an either-or sort of thing. We don’t get to just do simulation and never, ever actually go work in tissue.

And you have to do convergence tests, especially if you run lots and lots of these so that you know, that you’re getting to the right solution.

And the last line the bullet point down there is one that I never thought was going to be an issue in medical devices. But it turns out it is more often than, than I think we’d be comfortable with, but you can’t model what you don’t understand. If you don’t know the physics of why your device works. You can leverage simulation as part of your development because even though the FDA I don’t know if it’s still the case, but it certainly was a while ago where if you know that you do X, Y and Z and you always get the result that you want and you do that enough times and get it through the FDA if you don’t understand why x y&z drive that, that will prevent you from being able to use at least physics-based modeling for accelerating and saving your budget.

Rough examples real quick. One thing that we were involved in recently was, this is actually a device that came out of the Texas Medical Center that we were talking about with, with Lance Black. And I think he referred to someone at Methodist that was just handed their IP with no strings from the from the hospital.

This is that project. This is a urologist that came up with an idea for a device, they’re going to move from their initial concepts into a first-in-human trial. The top design is the end of the probe that they’re using. But actually they wanted to, rather than having to build their own prototypes that get those approved to us and humans, they wanted to use bipolar forceps as as their proxy electrodes and wanted to know what the difference was, whether there was any risk with damaging the tissue by applying these electrical pulses through bipolar foreceps, which is what’s in the bottom versus their device.

So we did a lot of simulation work around how far away is that from the ureter. We were looking for current concentrations of possible places where you get some thermal damage from application of these electrical pulses.

So we were able to create the visualizations and say it’s unlikely you’re going to get it have any thermal damage. They were able to take this to the IRB and get approval for their first-in-human, based off of this type of analysis.

So when they, rather than requiring more porcine models, another powerful technique using simulation is the optimization, where if you can describe something mathematically, you can turn the computer loose to solve those sorts of things.

I found videos are a good way to communicate what we do in simulation world with non-technical audiences, because it kind of gives them an idea and walks them through it at a reasonable pace. But what we’re looking at here is half of a jaw set, a hemostat-styled device where they’re trying to

minimize the mass of the device of the jaws themselves in order to increase visualization down at the tip of the device. But at the same time, it can’t be so flexible, that the jaws are going to deflect and touch and short-out if there’s no tissue between them.That was a subset of the different iterations that were done by the computer and try it and ran through all of this, for the shape optimization, I think it ran through about 350 different designs as it zeroed in on what would be the, the proper curve to that, given a certain load at the root, and then just simply supported at the tip. So it was an opportunity to drive through a bunch of those, have those then created, and move forward from as the first pass for prototypes.

Another example is a device where you’re looking at renal denervation. And this is a cooled catheter, where you run coolant through the balloon that occludes the vessel. There’s a nerve about four to six millimeters below the surface, that you want to apply RF energy, and ablate the nerve, but you want to run enough coolant through there, that you protect the vessel itself. And we were using optimization techniques on applying the energy into the end of the tissue to get a good idea what that behavior needed to be on the algorithm and energy delivery side rather than on the device itself.

Joe Hage: Are there some medical devices or situations that are not suitable for simulation first?

Arlen Ward: I would say that’s a balance, right? If you have a device that’s easy to prototype and not expensive to test, simulation is going to lose out to just building the prototype and and testing it.

If you if you’ve done a lot of these, whatever the devices that you’re looking at, and your experience tells you, you know, answers to those questions within a certain amount, that becomes an area where you’re just going to want to build it and test it rather than spend the time on the simulations. I certainly don’t say that the simulation is the be-all, end-all and applies in every case, I think it’s you use them both, it becomes just another tool in the toolbox.

Tor Alden: Great speech. Tor Alden, HS Design. We’re seeing a lot of AI, artificial intelligence and coming into the SolidWorks models and basically taking away our jobs eventually. But what at what point do you see the use of AI with your simulation tools? And when you mentioned you said you went through the ablation blades and yet you ran 137 – I forget how many models – did the machine optimize and pick the best one?

Did you just run it overnight and it gives you the right one, or do you have to do the post-modeling analysis to choose which one is the ideal?

So in that, in that particular case, for the job, we had a math equation that defined what would be optimal.

We basically said, we want to minimize the mass in the jaws by changing the shape by subtracting things away from the shape, but not exceeding a certain deflection in the job.

So those were the two things that had to be driven. In that case, it was just turned loose. And as it solved cases, it took an initial guess to the first one and looked at it, how it compared to the original and work its way through until it came to a minimum on in terms of mass, where anytime they would remove more mass than that or change shape in any direction, that it would increase the mass or exceed that limit of deflection.

So if you can describe your design goals, in terms of the optimization equations, you can turn it loose and have at the end, you have one one result that that simulation run says is the optimum based on where you’re at. For the most part, if you can describe what you’re trying to do in terms of that you can turn it loose.

Now there’s a whole other side of a whole other field of topology optimization were there it’s removing material from designs, and it works very well with additive manufacturing, because it’s no longer constrained by what you can machine. It’s now kind of printing out designs, that’s a whole other area that’s just getting started. And the impacts that I think are yet to be seen.

Srikoundinya Punnamaraju: I have a couple of questions.

At the interface of biology and engineering in a way. Are there other simulation tools available? The simulation is as good as the model of the inputs you to begin with. So, do you account for the biological environment of that to see the adverse impacts of, if any, of the simulation on the on the environment?

And, and the second question is, if you have to do like a number of models to get there? How does that compare to the testing?

Joe Hage: Well, let’s repeat the question because, despite protestation, he still spoke softly.

Arlen Ward: So the second half of that question was, if you have to go back and you’re running multiple simulations, how does that compare to to building these things, right.

A good time to use simulation is if you’re looking at six to eight weeks lead times to build something or you have a system where you have an energy delivery side and a disposable side and maybe you need to make progress on the disposable but your test fixture for the energy delivery side isn’t ready yet, that’s a great time to use simulation because even though you may not get 100% of the answer, if you’re 80 or 75% or something short of that, you’re still gonna be making progress towards for getting you know narrowing in on the answer that you want.

So time-wise I’ve yet to come across well I mean other than the things that we passed on doing the first place where you said you know you’re better off for your device just going out and testing it.

The one that I’m thinking of in particular was a needle-force insertion test where they had already built the prototype and it was a matter of sticking it through some porcine and skin and putting (??) on it. And it’s like, just go do that. You you don’t need us to develop models to address that. So time-wise, the simulation wins out.

When it’s the other side of that, where they’re complicated things to physically build and get answers to, but then you also have to instrument them up and find appropriate test.

On the boundary condition side, depending on whether you’re looking at mechanical (??) or Lachlan thermal boundary conditions, you can certainly match those to the soft tissue and the biological environment. Things like profusion are included in a lot of these models because it matters.

If you’re looking at polls, electrical pulses, and want to know whether it’s going to be a thermal damage risk, the blood that’s going through there is going to reduce that risk. So you want to include in the model, right? So those are those conditions are included as they need to be.

One of my pet peeves in the simulation world is companies that are using, especially implant companies that are using analysis to analyze their their implant, but they don’t analyze the soft tissue that’s around it, right? Because that’s the loading condition for the tissue, right.

Joe Hage: Dr. Ward Thank you very much. Thanks.