Speaker 1: Hushkin, what's one surprising thing your work has taught you
Speaker 1: about elite athletes?
Speaker 2: I never thought I would see muscles that were so
Speaker 2: developed they broke our scale. Wow.
Speaker 1: Yeah, like it was just too big the machine, the
Speaker 1: AI couldn't figure out what it is.
Speaker 2: Well, no, the AI found it, but we are like
Speaker 2: our kind of rating system.
Speaker 1: Wow. Was there a particular athlete or a particular sport
Speaker 1: or particular muscle?
Speaker 2: What?
Speaker 1: What? What muscle broke the scale?
Speaker 2: Uh? The gluteus maximus breaks it A.
Speaker 1: Fair kidding fantastic.
Speaker 2: Yes, it's a pain in my butt.
Speaker 1: Like because it's too big.
Speaker 2: Yeah, it's just so big.
Speaker 1: I'm Jacob Goldstein and this is what's your Problem? This
Speaker 1: month a bunch of Pushkin podcasts are coming out with
Speaker 1: Olympics inspired shows. Revisionist History has a series about America's
Speaker 1: decision to participate in Hitler's Berlin Olympics in nineteen thirty six.
Speaker 1: The Happiness Lab has an interview with a coach who
Speaker 1: coaches coaches and here on What's Your Problem, We're going
Speaker 1: to be talking with people who are working at the
Speaker 1: frontiers of technology to help elite athletes perform better. For example,
Speaker 1: today my guest is Sylvia Blemker. She's a professor of
Speaker 1: biomechanical engineering at the University of Virginia, and she's the
Speaker 1: co founder of a company called Springbok Analytics. Sylvia's problem
Speaker 1: is this, how do you combine MRI scans and artificial
Speaker 1: intelligence to generate new insights that can help both elite
Speaker 1: athletes and people suffering from diseases that affect the muscles.
Speaker 1: Springbox clients include medical researchers, Olympic athletes, major League baseball,
Speaker 1: and several professional basketball and soccer teams. You'll hear about
Speaker 1: all that on the show, but first we're going to
Speaker 1: pick up where we left off in the conversation. We
Speaker 1: were discussing the extraordinarily large muscles of elite athletes, and
Speaker 1: then Sylvia told me something even more surprising.
Speaker 2: The other thing is that they have some tiny muscles too, Huh.
Speaker 1: Like they have like smaller than a normal person's muscle.
Speaker 2: Much smaller.
Speaker 1: Huh.
Speaker 2: They put their muscle where they need it.
Speaker 1: What's an example, Like what muscle is tiny and what
Speaker 1: kind of athletes?
Speaker 2: Calf muscles are small in most fast athletes huh? And
Speaker 2: ether you look at a sprinter or like a running back.
Speaker 1: It's just all quad no calf, all.
Speaker 2: Like thigh no calf, yeah, THI And it kind of
Speaker 2: makes sense because you know, if you're trying to run fast,
Speaker 2: you wouldn't want to put a lot of mass like
Speaker 2: at the end of your leg. It's like as a
Speaker 2: lot of inertia to like move your leg.
Speaker 1: Huh.
Speaker 2: Because you know, the muscles are important for sprinting, that's
Speaker 2: the interesting thing, but they just don't they're small, very.
Speaker 1: H huh uh huh. So I'm particularly interested at this
Speaker 1: moment in the sports piece of what you do. I'm curious,
Speaker 1: by the way. Do you work with any Olympic teams
Speaker 1: or Olympic athletes? Yeah?
Speaker 2: Yeah, We've actually been working with several different Olympic athletes.
Speaker 2: The ones that probably that come to mind most are
Speaker 2: multiple players on the US women's national soccer team.
Speaker 1: Oh cool, tell me, like, tell me the story of
Speaker 1: that of that work. So they came to you, what
Speaker 1: did what did they what do they want when they
Speaker 1: came to you? Like, how did that? How did that begin?
Speaker 2: They came to us along with their team. So the
Speaker 2: technology we provide, you know, an athlete could understand it,
Speaker 2: but really with their team to help them figure out
Speaker 2: how to keep athletes healthy.
Speaker 1: So what did they what did they say? What did
Speaker 1: they say when they came to.
Speaker 2: So, for example, one athlete that's coming to mind had
Speaker 2: a known imbalance side to side that based on a
Speaker 2: history of injury, and they really wanted to know where
Speaker 2: that imbalance was coming from.
Speaker 1: So the woman had had hurt one of her legs,
Speaker 1: and that leg was even after she came back, that
Speaker 1: leg was weaker essentially than the other. I mean, is
Speaker 1: that the sort of gross you know, macro.
Speaker 2: Way, Yeah, exactly, that's a that's a nice way to
Speaker 2: put it.
Speaker 1: Yeah, And and they wanted a sort of finder like, okay,
Speaker 1: but we can see that, but what's going on on
Speaker 1: the inside, like muscle by muscle tell us that, yes.
Speaker 2: Exactly, that's precisely what we do. We go on the
Speaker 2: inside because on the outside you see perhaps that her
Speaker 2: knee extents or quads seem weaker on one side than
Speaker 2: the other. But there's four quads, quadre steps, four muscles,
Speaker 2: and so it's not clear which of those muscles are
Speaker 2: actually the culprit for that imbalance and in what way.
Speaker 1: Good So this is their question, and then what happens next?
Speaker 2: So This first step is an MRI scan, and so
Speaker 2: with these athletes or teams, we have ways to connect
Speaker 2: them with an MRI machine, whether it be through an
Speaker 2: imaging center that they partner with, or we've even actually
Speaker 2: brought MRI mobile trucks to sites to make it.
Speaker 1: Like players run off the field and get an MRI
Speaker 1: and go back and keep playing. Yeah, yeah, kind yeah, yeah.
Speaker 2: It helps just with the timing of things. But so
Speaker 2: first we connect them there, so it takes about ten minutes.
Speaker 2: Then they send those pictures up into the cloud into
Speaker 2: our server and then we crunched through it and then
Speaker 2: we send back a report on their muscles. We also
Speaker 2: have what we call it interactive Viewer, and it's presented
Speaker 2: in the form of a three D model. Three dimensional model,
Speaker 2: so you actually see your own legs, the muscles and bones,
Speaker 2: your own muscles and bones that we've identified from the
Speaker 2: images going through a process called segmentation where we find
Speaker 2: all the muscles and bones and then we reconstruct them,
Speaker 2: so it's kind of like a digital twin of that
Speaker 2: person that they can see on their computer. And so
Speaker 2: along with it or a number or all these metrics
Speaker 2: that helps them understand their balance. The development or strength
Speaker 2: of the muscles and the health of the muscles.
Speaker 1: So tell me about this report, they get like, what
Speaker 1: does it? What does it say?
Speaker 2: So the basis of that is actually a lot of
Speaker 2: research that we did over many years, because you need
Speaker 2: to understand where somebody falls relative to a normal essentially
Speaker 2: to give them. Essentially, we have a scoring system for
Speaker 2: the muscles and that's based on comparing with a large
Speaker 2: data set of healthy individuals. And so we know for
Speaker 2: a given person, based on their sex, age, height and weight,
Speaker 2: how big we expect all the muscles to be. And
Speaker 2: that's through a lot of previous research. So then we
Speaker 2: can say, okay, here's where you land each particular muscle
Speaker 2: compared to this nor what we call a normative database,
Speaker 2: so we call it a spring box score.
Speaker 1: Do you do it for every muscle in the leg.
Speaker 2: Or we do it? So our primary product that we
Speaker 2: started with was every muscle in the legs essentially from
Speaker 2: belly to feet, no muscle left behind. They're all important.
Speaker 1: How many muscles are there.
Speaker 2: Thirty five per leg?
Speaker 1: So seven okay, okay, yeah, more than I would have guessed,
Speaker 1: but a lot. Yeah, all important, okay. So, and so
Speaker 1: it's basically, how strong and healthy is every one of
Speaker 1: those seventy muscles relative to baseline?
Speaker 2: Right, And then the asymmetry comes where you can compare
Speaker 2: side to side. So for each of the thirty five
Speaker 2: muscles that they exist on each leg, we can say
Speaker 2: which side is bigger, which side is smaller, and by
Speaker 2: how much? And then we also have normative values for
Speaker 2: that because we're all just slightly asymmetric.
Speaker 1: Uh huh, And presumably some muscles are more asymmetric than others,
Speaker 1: And so you want to know kind of how how
Speaker 1: asymmetric relative to baseline is this particular pair of muscles exactly? Yeah,
Speaker 1: And and so in the in the case of this
Speaker 1: uh soccer player who came to you who you know,
Speaker 1: knew knew she had some kind of problem with her
Speaker 1: quadriceps on one side, but didn't know what was going on.
Speaker 1: What did you find?
Speaker 2: We found some imbalances, and actually not just in those muscles.
Speaker 2: It turns out that, you know, it's all connected. So
Speaker 2: if you have a weakness or an imbalance and one
Speaker 2: set of muscles, usually some other set of muscles are
Speaker 2: compensating in someone.
Speaker 1: Yeah, well, it's like when you like mess up. Even
Speaker 1: if you're just a recreational athlete, right Like, if you
Speaker 1: mess up something, you mess up your ankle, then you
Speaker 1: start walking funny, and then like your back hurts because
Speaker 1: you're walking funny. Right Like, that is a very anecdotally
Speaker 1: apparent thing.
Speaker 2: Yeah, yeah, we all know that, but that you know
Speaker 2: it shows through in the skin. But the thing is
Speaker 2: that it's not very intuitive from the outside which muscles
Speaker 2: have been affected, how they've compensated, and it looks different
Speaker 2: for every single person.
Speaker 1: Huh.
Speaker 2: So that's why the report is very valuable because for
Speaker 2: that person, they know exactly which muscles are the ones
Speaker 2: that they really need to target, both the ones that
Speaker 2: they already thought maybe were an issue, but then all
Speaker 2: the other ones that showed up and they didn't really realize.
Speaker 1: And so in the case of this soccer player, was
Speaker 1: it like one particular quadrcep on one side that was
Speaker 1: like the core thing and you could figure out which
Speaker 1: one it was.
Speaker 2: There was a few mess It wasn't just that. I
Speaker 2: think there were at least one calf muscle and then
Speaker 2: some in especially in the deep hip, those were impacted.
Speaker 2: So yeah, it kind of shows up everywhere.
Speaker 1: And so you have this essentially diagnosis, right, a very
Speaker 1: sort of fine grained kind of diagnosis. Do you also
Speaker 1: have a have a prescription? Do you have sort of
Speaker 1: particular kinds of training to address these very fine grained things,
Speaker 1: or do you leave that to the trainers or whoever?
Speaker 2: We leave that to the trainers, because I think that
Speaker 2: it's also important to have all the other information about
Speaker 2: the athlete. We're not arguing that it replaces everything else.
Speaker 2: And people pair it with lots of different other types
Speaker 2: of measurements depending on the application or in the setting,
Speaker 2: Like some people pair it with let's say, metrics of
Speaker 2: jump performance. I'm shifting over to basketball here, but that's
Speaker 2: just one that came to mind, where you can look
Speaker 2: at the asymmetry about how how an athlete jumps, but
Speaker 2: then you can also compare it to the asymmetry of
Speaker 2: their muscles and get some insight. So it definitely, you know,
Speaker 2: plugs in with a lot of other things.
Speaker 1: And to what extent can trainers or you know, strength
Speaker 1: coaches develop programs that are sufficiently kind of fine grained
Speaker 1: to match the kind of fine grained findings you're having, right, Like,
Speaker 1: for example, if you find, as I understand you did
Speaker 1: that a soccer player has one particular quadricep that is weak. Like,
Speaker 1: are there workouts that target a single quadricep and not
Speaker 1: the others?
Speaker 2: Yep, there are.
Speaker 1: That's cool for whichever quadricepp you're just like, just for fun,
Speaker 1: give me an example.
Speaker 2: You know. One one way that it's very simple is
Speaker 2: using something called biofeedback. Huh. So you can measure whether
Speaker 2: you use something called EMG, which is a way to
Speaker 2: measure how much electrical activity is a muscle, and then
Speaker 2: you can see which muscles you're using for a given task.
Speaker 2: So if you give people the feedback of which of
Speaker 2: those muscles they're using and say, oh, no, you're not
Speaker 2: using this one, use this one more, that actually works
Speaker 2: very effectively.
Speaker 1: Oh really, So you can basically use your brain if
Speaker 1: you're getting the feedback to focus on which quadricep you're training.
Speaker 2: Yeah, and there's other ways you can give the feedback
Speaker 2: in other different ways, but yeah, our brains are very
Speaker 2: good at that. Once they get feedback, they're very good
Speaker 2: at learning.
Speaker 1: That's cool, especially somehow to think of what elead athletes right,
Speaker 1: because they are already presumably like super dialed in in
Speaker 1: terms of like the relationship between their brain and their
Speaker 1: body at this very elite level exactly.
Speaker 2: Yeah. The other I was going to mention a lot
Speaker 2: of players and teams use this not just one time,
Speaker 2: but overtime. So they'll get a scan, figure out a plan,
Speaker 2: work on that for maybe three months or six months,
Speaker 2: and then do another scan and see how things are
Speaker 2: progressing and adjust accordingly. So that's definitely another way to
Speaker 2: in the long term see if what they're doing is
Speaker 2: resulting in the change that they're hoping to see.
Speaker 1: So, what happened with that soccer player who had the
Speaker 1: weak quadricep and other related Yeah, No, I.
Speaker 2: Think she's doing great, like staying healthy and and you know,
Speaker 2: getting ready.
Speaker 1: Yeah. So I know you can't tell us her name,
Speaker 1: but will we see her in the Olympics this time? Great? So,
Speaker 1: as you were talking about that, I mean there was
Speaker 1: a moment where it was like, Okay, the athlete goes
Speaker 1: and gets the MRI and then you get the scan.
Speaker 1: You get the scan, and then you said, like, you
Speaker 1: crunch through the numbers and then you make the report.
Speaker 1: Ssumably you crunching through the numbers is like the result
Speaker 1: of many many years of work and kind of the
Speaker 1: core of what your company does. So I want to
Speaker 1: talk a little bit more about that and kind of
Speaker 1: how you how you got here, Like how did you
Speaker 1: come to start the company?
Speaker 2: Mm hmm? How long do I have a while?
Speaker 1: I mean it's not you know, it's not the radio,
Speaker 1: it's a podcast.
Speaker 2: You. I am a professor too, so I can go on.
Speaker 1: Let me ask you this, What was the moment when
Speaker 1: you decided to start the company?
Speaker 2: I can give you one moment and then we can
Speaker 2: do a.
Speaker 1: Couple of moments. Yeah, that's what's what a good story is,
Speaker 1: like three moments.
Speaker 2: In time, right right? Three not four?
Speaker 1: Four is a tricky number. We could do five, or
Speaker 1: we could do one. I think I think is better.
Speaker 2: Yeah. Yeah. So I'm a professor. I run a lab
Speaker 2: and form my entire career. I've been fascinated with must
Speaker 2: and how it works, and fascinated by something that we
Speaker 2: all in like the muscle field. We call form function relationships.
Speaker 2: So the idea that the way a muscle is shaped
Speaker 2: and the way it's structured and how big it is
Speaker 2: influences how well it works or how well it functions,
Speaker 2: how strong it is, how well it behaves. And there's
Speaker 2: a lot too there, and there's a lot of nuance.
Speaker 2: And that's like I've spent a career studying that in
Speaker 2: lots of different ways. So I've always been interested in
Speaker 2: quantifying muscle and figuring out how that influence is, how
Speaker 2: it works, and both in healthy people or in athletes,
Speaker 2: and also in different patient populations different In particular, I
Speaker 2: have an interest in movement disorders, so neuromuscular diseases that
Speaker 2: lead to impairments and mobility and movement ability. So one
Speaker 2: of the light bulb moments for this was the fact
Speaker 2: that I'd been using MRI to study muscle in my
Speaker 2: research for a long time. It's kind of a ubiquitous
Speaker 2: tool or like often use tool and research. But I
Speaker 2: was struck by the fact that I was hearing from,
Speaker 2: in particular a surgeon collaborator, and the surgeon was telling
Speaker 2: me about his work and helping kids with cerebral palsy
Speaker 2: improve their movement where they had hindered movement and largely
Speaker 2: because their muscles are impacted. Not only do they have
Speaker 2: an impaired ability to control their muscles, but their muscles
Speaker 2: end up with impairments in their structure or their form,
Speaker 2: which then influences how well they work. So surgeons have
Speaker 2: to go in and do surgeries to try to change that.
Speaker 2: They do things like modify tendons to try to make
Speaker 2: muscles less stiff, or they transfer muscles to make them
Speaker 2: do a new thing. But one of the big tricky
Speaker 2: parts is that oftentimes some of those muscles are very week.
Speaker 2: So if they choose the wrong muscle, then they'll make
Speaker 2: a weak muscle even weaker. Huh, And that's catastrophic. So
Speaker 2: it's a very fine line that a surgeon has to
Speaker 2: figure out and they have to go in. You know,
Speaker 2: we just talked about there's thirty five muscle muscles in
Speaker 2: each leg, So which of the muscles are the ones
Speaker 2: that should be operated on and which ones should be avoided?
Speaker 2: And so what my collaborator, a guy named doctor Mark Abel,
Speaker 2: fantastic surgeon. He was telling me, Yeah, like, it's very hard,
Speaker 2: and I don't he didn't have a way to see that.
Speaker 2: All that he could do is look from the outside.
Speaker 2: No technology could give him the information he needed to
Speaker 2: figure out which muscles he should focus on and which
Speaker 2: ones to.
Speaker 1: Avoid, because it's not obvious by looking what's a strong
Speaker 1: muscle and what's a weak one. Yeah, I guess that's
Speaker 1: surprising to me on some level, Like I don't I've
Speaker 1: never thought about it. But naively, I would think you
Speaker 1: could look at a muscle and say it looks strong
Speaker 1: or it looks weak.
Speaker 2: Not right, because you just see it from the surface.
Speaker 2: You don't see it on the inside. And the other challenges.
Speaker 2: For every joint, there's many muscles. So like we just
Speaker 2: said that quadrceps has four muscles on the back of
Speaker 2: the leg, ham strings, there's three hamstrings muscles. There's other
Speaker 2: muscles that are in the thigh. So you're just seeing
Speaker 2: what's an impairment and the overall movement, let's say of
Speaker 2: a joint. But then there could be many muscles or
Speaker 2: combinations of muscles that are leading to that, and you
Speaker 2: don't know when you look from the outside. Our body
Speaker 2: is designed that way actually to be somewhat we call
Speaker 2: it redundant. We have more muscles than we need probably,
Speaker 2: but if you think about imbalances, then any one of
Speaker 2: those muscles could create it create some trouble.
Speaker 1: So, okay, so the surgeon describes this problem he's having,
Speaker 1: and then and then what do you do?
Speaker 2: So then I was thinking, well, you know, that's the
Speaker 2: information that we generate all the time. When we're doing
Speaker 2: our research. We take MRIs, quantify, we identify muscles, we
Speaker 2: create three dimensional models of the muscles, we figure out
Speaker 2: how they're working from that. But I was struck by
Speaker 2: the fact that none of that was something that a
Speaker 2: clinician could use, despite the fact that MRI is obviously
Speaker 2: ubiquitous in healthcare. Right, yeah, you can't go to a
Speaker 2: hospital without finding multiple MRIs, but there's no way to
Speaker 2: use those MRIs and the way that I was using
Speaker 2: them for my research, And I thought, well, that's too bad,
Speaker 2: because this would be very useful to the surgeon in
Speaker 2: figuring out how to treat these patients. So that was
Speaker 2: one light bulb at the beginning. So a lot of
Speaker 2: it was figuring out how to take something that we
Speaker 2: use in research for very specific, targeted basic science questions
Speaker 2: and turn it into something that is useful clinically.
Speaker 1: And again this is like my ignorance, Like I might
Speaker 1: have thought, well, you could just do an MRI and
Speaker 1: see how big or not big the muscles are, and
Speaker 1: infer from how big or not big the muscles are,
Speaker 1: how strong or not strong they are. And that sounds straightforward,
Speaker 1: but clearly it's not, like, why is it harder than that?
Speaker 2: So a couple of reasons. One is going taking the
Speaker 2: MRI pictures and figuring out how big the muscles is
Speaker 2: a very challenging problem. So in order to accurately get
Speaker 2: how big the muscles are, you have to essentially generate
Speaker 2: its shape in three dimensions, so you have to get
Speaker 2: the whole length of the muscle, and so you do
Speaker 2: that off of multiple MRI pictures. So the MRI essentially
Speaker 2: kind of takes pictures through the body at multiple different
Speaker 2: slices we call them, going from you know, the abdomen
Speaker 2: all the way down to the feet, sort of going
Speaker 2: cross sectionally we call it. And so we usually have
Speaker 2: over two hundred images. So in each image you have
Speaker 2: to find each muscle, and so for any given image
Speaker 2: there's probably at least fifteen muscles or more.
Speaker 1: So it wasn't like you could just push the like
Speaker 1: show me the muscles button on the MRI and it
Speaker 1: would show you the muscles.
Speaker 2: Like.
Speaker 1: Nobody had done that, and there was no obvious way
Speaker 1: to do it, certainly not for a surgeon. Ordering a
Speaker 1: standard MRON just didn't exist.
Speaker 2: It did not exist.
Speaker 1: So okay, so you realize this, what happens? How do
Speaker 1: you make it happen?
Speaker 2: So, you know, one of our first tasks was to
Speaker 2: figure out how to get many muscles. So one of
Speaker 2: the things that we had done on the research side
Speaker 2: is really focus on a couple of muscles, but I
Speaker 2: knew for this application that wasn't going to work. We
Speaker 2: have to be able to identify any muscle. That was
Speaker 2: really the problem is that like you don't know which
Speaker 2: one's the problem, so you don't know which one to
Speaker 2: look at. So you got to look at all of them.
Speaker 2: And then the next task was to figure out all
Speaker 2: those muscles and figure out a process to go from
Speaker 2: the you know, identifying each and every muscle and each
Speaker 2: and every image. So it's called developing an atlas.
Speaker 1: And is that an AI problem?
Speaker 2: So now we have an AI, and it's the type
Speaker 2: of AI as supervised learning, where they can you essentially
Speaker 2: train the computer to do what the person would do.
Speaker 2: But in order to do that, you need to do
Speaker 2: what the person would do first. And so we did
Speaker 2: that all manually at first, in order to generate one
Speaker 2: of these reports. At first, it took us about fifty
Speaker 2: hours per person.
Speaker 1: Just going through image after image after image and saying
Speaker 1: this is this muscle, that is that muscle exactly.
Speaker 2: So we needed to develop that. But the other piece
Speaker 2: we needed is this back to this normative database I
Speaker 2: talked about, because if I just told you how big
Speaker 2: your muscle is in milli leaders in volume, what are
Speaker 2: you going to do with that information?
Speaker 1: Like, oh, great, and nobody knew And it's interesting. It's
Speaker 1: one of those things you always think, oh, surely there's
Speaker 1: some data in the world that everybody knows X. But
Speaker 1: so you're saying, nobody knew what was the kind of
Speaker 1: medium size of a particular quadricept for whatever, a healthy
Speaker 1: twelve year old boy or whatever. Nobody knew that at
Speaker 1: that time. Well, all of.
Speaker 2: The information up until then, for the most part, was
Speaker 2: based on dissecting cadavers.
Speaker 1: Uh huh.
Speaker 2: Was based on taking cadavers and dissecting muscles, weighing the muscles.
Speaker 2: And you know, one of the big challenges with that
Speaker 2: is usually cadavers are older adults, and so they're not
Speaker 2: really representative of a younger, healthy population. And I will
Speaker 2: tell you at that time that that was a lot
Speaker 2: of work and we had I had people saying, like,
Speaker 2: why are you doing that. Uh, like that seems like
Speaker 2: a waste of time. That's crazy. You know, I had
Speaker 2: this vision and I trusted that it was going to
Speaker 2: turn into something at least useful to the research community,
Speaker 2: and you know, I'm thankful that we stuck with it.
Speaker 1: There's lots more to come on the show, including but
Speaker 1: not limited to the work Sylvia and her colleagues are
Speaker 1: doing with major league pitchers, college football players, and patients
Speaker 1: with degenerative muscle disease. Sylvia and her colleagues trained an
Speaker 1: AI model to do what had previously taken a human
Speaker 1: fifty hours for every person who got scanned, and they
Speaker 1: expanded from working with patients with cerebral palsy to working
Speaker 1: with elite athletes. Today, their clients include not just Olympic athletes,
Speaker 1: but teams in the NBA and the Premier League. Also,
Speaker 1: she told me they're working on a project with Major
Speaker 1: League Baseball.
Speaker 2: Yeah, so we're working with the MLB studying pictures and
Speaker 2: we're getting essentially a normative database, whole body scan of pictures.
Speaker 1: And is that partly because like pitchers mess up their
Speaker 1: arms so badly? Is that kind of the motivation there, Yes.
Speaker 2: There's definitely a lot of issue with with injury and surgery.
Speaker 2: And so the idea here is that by taking these scans,
Speaker 2: we can really figure out where there might be weaknesses
Speaker 2: and sort of potential areas for mitigating the injuries.
Speaker 1: So when you do work for a whole team, like
Speaker 1: say the Bulls, you know, a basketball team, an NBA team, Like,
Speaker 1: what's the nature of that of that work? What do
Speaker 1: you do for a team like that?
Speaker 2: Yeah, we're they will do a baseline of the whole
Speaker 2: team and.
Speaker 1: They basically tailor the athletes training presumably strength training in
Speaker 1: particular on a muscle by muscle basis, based on the
Speaker 1: reports that you're sending them.
Speaker 2: Correct.
Speaker 1: Yeah, yeah, And I mean you can imagine like better
Speaker 1: performance being one outcome. Reduced risk of injury seems plausible, right,
Speaker 1: Like it seems obvious that like a big asymmetry could
Speaker 1: make you more likely to be injured. I mean, are
Speaker 1: you at a point now where you can predict the
Speaker 1: risk of injury?
Speaker 2: That's like a whole can of worms.
Speaker 1: I won't say, I mean, is that interesting to you?
Speaker 1: Or is that like too much? Or yeah?
Speaker 2: No, no, no, this is something we think about a lot,
Speaker 2: and let me I want to so first, I'll tell
Speaker 2: you why it's a can of worms. Yeah yeah, And
Speaker 2: I'll tell you what project.
Speaker 1: Tell me about the can. We'll look at it from
Speaker 1: the outside of.
Speaker 2: It, so from the cannon. Like, there's a lot of
Speaker 2: technologies out there that will say that they're predicting injury risk.
Speaker 2: They'll give you numbers, and they're just not based on anything,
Speaker 2: and so I don't know, it's there's.
Speaker 1: A lot of the yeah.
Speaker 2: Yeah, yeah, yeah. So that's not what we're about, Like,
Speaker 2: we're about like providing actual things that matter. And so
Speaker 2: the question is like can you do do these muscle scans?
Speaker 2: Do they correlate with injury likelihood in some way? And
Speaker 2: so we actually have a project to address that very question.
Speaker 2: It's actually funded by the NFL. Uh. We're actually in
Speaker 2: that project. We're working with college teams called college football
Speaker 2: teams baselining entire rosters at the beginning of the season
Speaker 2: and then tracking hamstring injuries and then if a if
Speaker 2: an athlete gets injured, they come back for a scan
Speaker 2: at the time of injury and then return to sport.
Speaker 2: And so one of our questions is based on the
Speaker 2: baseline scan, can we predict who's more likely to get
Speaker 2: an injury, an initial injury index injury, and then then
Speaker 2: the secondary question is can we predict who will be
Speaker 2: re injured? I was saying that we often pairt people
Speaker 2: pair it with other things. In this project, we're always
Speaker 2: also doing that. Each athlete is getting an assessment of
Speaker 2: their sprint mechanics, so kind of the biomechanics of how
Speaker 2: they run, and then also assessment of their strength of
Speaker 2: their hamstring muscles, so like kind of measured strength. Obviously
Speaker 2: you can't do that when they have an injury, of course,
Speaker 2: but you can you know when they're healthy.
Speaker 1: So that biomechanics piece seems like something that has been
Speaker 1: developing in parallel with your work, also driven by computer vision, right, that,
Speaker 1: like markerless motion captures, seems like a big world that
Speaker 1: that overlaps with your world some Yeah, So tell me
Speaker 1: about your work with female athletes versus male athletes and
Speaker 1: how that plays a role.
Speaker 2: Yeah, I will say probably the biggest thing that we've
Speaker 2: been focused on is making sure that our data addresses that.
Speaker 2: So our normative database is uh separated by sex, so
Speaker 2: and it is different because like women aren't small men, right,
Speaker 2: So it's important that we have that basis to compare
Speaker 2: that's like for women and not comparing to some average
Speaker 2: or primarily male data sets. So that's that's one huge
Speaker 2: important thing is that it's it's compared to the normative
Speaker 2: values for the female population. And then in terms of
Speaker 2: like working with the female athletes, I think, you know,
Speaker 2: one of the big ones is is really just ability
Speaker 2: to personalize and provide this like really accurate detailed assessment
Speaker 2: of their of their bodies. And you know, a lot
Speaker 2: of the you know, knowledge about like appropriate body composition
Speaker 2: historically has been based on studies and men, and but
Speaker 2: then we're applying them to women and making us feel
Speaker 2: really bad about ourselves. So really motivated to move away
Speaker 2: from that and sort of acknowledge the muscular physiology and
Speaker 2: anatomy of the female and also the female athlete to
Speaker 2: really understand understand that. You know, I think one thing
Speaker 2: obviously that we've seen is, you know, acl injuries are
Speaker 2: more common in women than men, and examining how these
Speaker 2: like recovery profiles look on how they differ between men
Speaker 2: and women. That's something that we're observing and seeing how
Speaker 2: those things shake out. But we're motivated by really providing
Speaker 2: that information that's specific to women.
Speaker 1: So what are some of the non sports things you're
Speaker 1: working on, things you're trying to figure out.
Speaker 2: Yeah, I mean, one that's I'm really interested in is
Speaker 2: this area that we're applying to in clinical trials for
Speaker 2: muscle disease. So we've been working in a specific muscle
Speaker 2: disease called fascioscapulo humoral muscular district f SHD, which is
Speaker 2: a slowly progressing muscle disease genetic and basis, and so
Speaker 2: eventually people with f SHD need a wheelchair. Just life
Speaker 2: is very difficult, and so it's pretty devastating. But the
Speaker 2: other exciting thing is there are some new treatments out there,
Speaker 2: some in particular gene therapies coming online. And now the
Speaker 2: challenges do they work because the problem is in these
Speaker 2: diseases because they're pretty slowly progressing. If you want to
Speaker 2: see if a drug is helping somebody, it's very hard
Speaker 2: to see that in a slowly progressing.
Speaker 1: Right and the clinical manifestations are hard to pick up.
Speaker 1: If it makes your muscles shrink more slowly, it's going
Speaker 1: to be hard to see.
Speaker 2: It's very hard to see, especially from like rudimentary measures.
Speaker 2: But with the MRIs. We've been able to provide this
Speaker 2: really detailed insight about the disease date of each muscle
Speaker 2: and how it's progressing over time. And so you know,
Speaker 2: one of our goals is to really lean in on
Speaker 2: IS and help figure out exactly how people should look
Speaker 2: at all this data and figure out if a drug
Speaker 2: is working or not. It's really profoundly important because without that,
Speaker 2: these these clinical trials just won't move forward.
Speaker 1: What else are you sort of still trying to figure out?
Speaker 2: So we talked about predicting injury but having all the
Speaker 2: data needed to show like if you do if your
Speaker 2: scan looks like this, and if you do this, you
Speaker 2: will be able to improve your jump high by height.
Speaker 1: By that, yeah, you'll be able to throw a fastball
Speaker 1: two miles an hour faster. Like that would be wildly valuable.
Speaker 2: That would be very and we do have data in
Speaker 2: our search. We were able to show that these muscle
Speaker 2: scores correlate with performance metrics such as jump, hide, and speed.
Speaker 2: So we for sure see that The question is then
Speaker 2: the spin on like observing how how that plays out,
Speaker 2: Like if you then strengthen the appropriate muscles, how how
Speaker 2: much faster do you get? And you just need more
Speaker 2: and more data to really like to go after that.
Speaker 2: But that's one thing that I'm fascinated by. One of
Speaker 2: the other interesting ones, Can I go off on a tangent.
Speaker 1: Anything you want?
Speaker 2: One of our research partners that's interested in how muscles
Speaker 2: adapt to strength training and different interventions and what influences
Speaker 2: that had a really interesting finding that I think is
Speaker 2: quite profound but also obvious. So everybody if there, if
Speaker 2: they're targeted training their quadrceps and hamstrings, those muscles got bigger,
Speaker 2: that makes sense, but in a fair number of the people,
Speaker 2: some muscles got smaller. And then you know, he had
Speaker 2: done some controlling and in documentation of nutrition intake, and
Speaker 2: he found that people that had higher caloric and protein
Speaker 2: intake had less of that effect.
Speaker 1: So all that, all the Jim bros. Telling you to
Speaker 1: eat a lot of protein are validated by this guy's study.
Speaker 2: Yeah, yeah, but they're not. It's not necessarily to make
Speaker 2: that muscle that you're working bigger so you don't lose
Speaker 2: the other muscles.
Speaker 1: Uh huh. That's a good one. And was he using
Speaker 1: your scans to figure out that?
Speaker 2: Yeah? Yeah, he was using our scans and and the
Speaker 2: thing that was cool. Is that Normally in research you
Speaker 2: wouldn't bother looking at those other muscles. You would just
Speaker 2: look at the ones that were targeted, right, because those
Speaker 2: are the ones that you just think about. But by
Speaker 2: getting the entire extent of the of all the muscles,
Speaker 2: you see these impacts that you wouldn't necessarily.
Speaker 1: Have known, Like he wasn't even looking for it.
Speaker 2: Yeah, it's it's quite profound because somebody's strength training recovering
Speaker 2: from an injury. That really means like that, the nutritional
Speaker 2: elements important because you could be strengthening some muscles but
Speaker 2: weakening others if you're not. If you're not, you know,
Speaker 2: playing your cards right there.
Speaker 1: We'll be back in a minute with the lightning round.
Speaker 1: M h, I want to finish with the lightning round.
Speaker 1: I won't take too long. It'll be fun.
Speaker 2: Okay, what I don't know what that is?
Speaker 1: Well, you'll find out right now.
Speaker 2: Does this have to be fast?
Speaker 1: I could call it the random round? Okay, I like
Speaker 1: that random. Have you scanned yourself?
Speaker 2: Yes? Well times? Oh of course that's what kind of thing.
Speaker 2: Probably I've been in an MRI machine, I don't know,
Speaker 2: maybe a hundred times.
Speaker 1: Like it's not radiation. Right, it's not like an X ray.
Speaker 1: You could do it every day.
Speaker 2: As much as you want.
Speaker 1: Yeah, what'd you learn?
Speaker 2: So I actually used it? You know, I've learned lots
Speaker 2: over the years, but I will tell you one anecdote.
Speaker 2: I have a hip replacement. I have a genetic condition
Speaker 2: that leads to early arthritis. And so I was before
Speaker 2: I got my hip replacement, I got a scan. I
Speaker 2: knew I was getting weak, but holy cow, was I
Speaker 2: really weak on that side. What was profound was how
Speaker 2: weak my hip flexers were very weak. And I you know,
Speaker 2: I think a lot of times people talk about hip
Speaker 2: flexures being tight, and that's kind of what I thought
Speaker 2: was happening. I felt pain and I felt like I
Speaker 2: was having a lot of tightness, but it was actually
Speaker 2: weakness and they were like super small on both sides,
Speaker 2: but really especially on the on the side that was
Speaker 2: effect it. So that was one thing that I worked
Speaker 2: on a lot.
Speaker 1: Do that genetic condition you have, did that influence your
Speaker 1: your work and all your decision to go into the field.
Speaker 2: I mean it's like loosely maybe because my dad had
Speaker 2: the same thing, which actually caused him to go blind. Oh,
Speaker 2: it has like a multiple different issues, and so I
Speaker 2: think that at an early age got me interested in
Speaker 2: medicine and disabilities and like helping helping people, so that
Speaker 2: that might be broadly speaking, I didn't and I knew
Speaker 2: I had some eye problems. I didn't know the genetic thing.
Speaker 2: We didn't discover that till later.
Speaker 1: But huh. Interesting. What's the most underrated muscle in the
Speaker 1: human body?
Speaker 2: Hmm, that's a hard one. So I have a few
Speaker 2: favorite muscles.
Speaker 1: Okay, what's your favorite muscle?
Speaker 2: Yeah, so the so muscle so as major it's it's
Speaker 2: a hip flexer, okay, but it also it's really cool
Speaker 2: it actually it's also a back lower back muscle, so
Speaker 2: it attaches to the lumbar vertebrae. But then it also
Speaker 2: crosses the front of your hip. It's really hard to
Speaker 2: find because it's like really back deep in your hip.
Speaker 2: It goes right over your.
Speaker 1: Your in the middle of your body kind of yeah,
Speaker 1: right in the middle.
Speaker 2: It kind of connects everything, sort of connects your lower
Speaker 2: extremity to the rest of your body in some ways.
Speaker 1: Okay, last one, why do you hate astrophysicist Barbie?
Speaker 2: I don't hate anything. I mean, well, it's too perfect.
Speaker 2: It's kind of like this, you know idea that like, oh,
Speaker 2: you know, you can, you can inspire girls to go
Speaker 2: into science by showing them that Barbie does too. But
Speaker 2: Barbie's like fictitious, so it kind of tells you that,
Speaker 2: like it like promotes the idea of perfectionism in society
Speaker 2: but definitely in girls. And you know, what we really
Speaker 2: want to promote is almost the opposite of that is
Speaker 2: taking risks and and not worrying about being perfect and
Speaker 2: just doing something that matters to you. So yeah, I
Speaker 2: don't know. I mean, I don't hate it. I had
Speaker 2: Barbies when I was a kid, but I just it
Speaker 2: kind of.
Speaker 1: Like wrote you wrote a whole column about it.
Speaker 2: That was I was very proud of that. Yeah, no,
Speaker 2: and really that, you know, the astrophysicist part, honestly was
Speaker 2: more of a hook. The article I had written already
Speaker 2: before that Astrophysicist Barbie came to be. It was about
Speaker 2: the issue of perfectionism and how that dissuades girls to
Speaker 2: go into stem and research.
Speaker 1: Well, a good hook is important, and you found.
Speaker 2: Yeah.
Speaker 1: Sylvia Bleimker is a professor at the University of Virginia
Speaker 1: and the co founder of Springbok analytics. Next week on
Speaker 1: What's Your Problem, I'll be talking to Jimmy Buffy. He
Speaker 1: is using AI to bring the insights of biomechanics to
Speaker 1: professional athletes. Jimmy told me that before the advent of AI,
Speaker 1: when biomechanics experts tried to work with athletes, it could
Speaker 1: be somewhat awkward. So You've got like a picture in
Speaker 1: his underwear with a bunch of little metal balls, Take
Speaker 1: them and they're like, just pitch like you always pitched right.
Speaker 3: And the state of the art for tracking it was
Speaker 3: an awful experience for the people you.
Speaker 2: Were trying to track.
Speaker 1: So what changes?
Speaker 3: The big inflection point was computer vision, basically using artificial
Speaker 3: intelligence to identify where those joints are in a camera image,
Speaker 3: rather than needing to paste those reflective markers.
Speaker 1: Today's show was produced by Gabriel Hunter Chang, edited by
Speaker 1: Lydia Jean Kott, and engineered by Sarah Bugero. You can
Speaker 1: email us at Problem at Pushkin dot FM. I'm Jacob Goldstein.
Speaker 1: We'll be back next week with another episode of What's
Speaker 1: Your Problem.