Michal Brzus PhD

Justin Brady: [00:00:00] From the Jethro’s BBQ studio, I’m Justin Brady, and AI is being used to predict and therefore tailor recovery now. This is a thing. Um, AI in, in healthcare is gonna absolutely bro- blow all of our minds. We’ve, we’re barely even gone down this path yet,

and It’s already

blowing my minds, uh, blowing my mind.

Uh, to talk about

That is the CEO of NeuroPred, Dr. Michal Budzisz, uh, joins us today. Thank you so much for coming on the show.

Michal Brzus: Yeah, thank you for having me. Hi, everyone

Justin Brady: Um, everyone, before we get started, we obviously appreciate Denton’s Davis Brown bringing you all this crazy content. We shove your brain so full of smart stuff that sometimes it hurts.

Brianna Young was in

here, one of their attorneys, talking about easy business succession planning and why owners neglect it, and probably shouldn’t. It’s, like, super easy. And then of course, uh, Joellen Whitney is a AI privacy expert and lawyer. She talked about setting up proper [00:01:00] guardrails, why that’s important, and why collecting too much data at your workplace is actually probably not a good thing.

So go check that out at Iowapodcast.com/business. Uh, Dr. Budzisz, uh, thank you so much for coming in here. I’m

Doing my best on the bas- b- the, the last name

by the way. Polish last name, and I always try to nail last names and names as much as I can.

Um, but don’t hold me to

Michal Brzus: doing great

Justin Brady: Um, first of all, August 19th

or you got first place in the John Papa John On- Iowa Entrepreneurial Venture Competition, I believe.

tell us a little bit about that. Congratulations.

Michal Brzus: Yeah, thank you so much. So, um, it’s annual startup competition that is organized by the John Papa John, uh, organization, uh, which is– They have the, also the student, uh, competition- Yeah … because they’re in every Iowa, uh, school. I think there is five chapters in Iowa, Iowa State, and, uh, UNI and a couple others.

But this is the statewide competition that [00:02:00] is open to all the startups and companies in Iowa.

Justin Brady: Yeah, so congratulations on that. The NeuroPred, right, is, is absolutely, it’s like one of the first things we’re seeing now using AI to predict future outcomes and therefore adjust for those outcomes, that, that there’s probably a better, uh, nuanced way of saying that. But can you give people an overall 30,000 foot view of what you’re working on?

And then I have so many questions, but like, what is this thing?

Michal Brzus: Uh, yeah, it’s a great question and, uh, we’re very excited to, to move this ’cause it used to be only possible in research setting, and it looks like we’ll be able to now bring it to the clinic and to everyone. Uh, so currently stroke care is, uh, doing actually very good with saving people lives. The mortality rates are dropping.

Actually, Iowa has a better mortality rates than average US. However, we still see 60% of patients post-stroke have, [00:03:00] uh, long-term cognitive impairment, uh, which really cause a lot of issues for people, but it cause only 50% of people in the working age after stroke are actually able to get back to work.

Justin Brady: Wow.

Michal Brzus: And the problems…

There’s many problems. Rehab starts way too, uh, too late- Mm … missing the neuroplasticity period where actually brain can improve, and, uh, it kinda goes in blindly. Doctors don’t have any tools to actually know what’s gonna happen to the patient and tailor the rehab for that patient. So what we can do, we can take the standard clinical MRI imaging of the brain, find all the brain damages, and predict cognitive outcomes for specific domains so to give that information to the doctors so they can make better decisions in this very, uh, crucial initial period and give patients the best chance for recovery.

Justin Brady: So in a way, in a way it’s almost the medical imaging crystal ball. I mean, it’s, you know, no one can tell the future, but you’re, you’re kinda working on this patient, this brain, this stroke [00:04:00] patient here, this br- stroke victim. Here is the trajectory of recovery. This is what it’ll look like, and therefore you’re able to make almost clinical adjustments to change that outcome in the future.

This is essentially what you’re working on, right?

Michal Brzus: Uh, yes, exactly. So we are trying to basically make doctors’ life easier, provide them with the tools and, uh, information that is just not clinically feasible currently, um, so they can drive patients, uh, through the curve faster and better, um, in a way that is actually personalized to each specific patient

Justin Brady: Yeah, so this is key because what this means is that current stroke recovery is essentially a one-size-fits-all thing. And

Michal Brzus: Uh, yes. There, there are great, uh, great, uh, institutions that, uh, do some personalization. There is, you know, a lot of really great comprehensive stroke centers at clinics that [00:05:00] have world best clinicians that are trying to really fit it to the patient. But especially in places like Iowa and other rural areas and majority of the hospitals, that unfortunately is what ends up happening, that basically everyone goes through the same protocol

Justin Brady: So more data in this case. Um, m- the, the research that you’re working on, the brain scans that you’re working on through, you know, use of AI is giving these clinicians a roadmap to follow essentially

Michal Brzus: Yes. Yes. We predicting the risk for specific domains. So, you know, not every patient will have the same needs in rehab. Not every patient has the same outcomes. Uh, but every brain’s different, so why should everyone be getting the same care? We can definitely improve on the, on the current way it’s being done, uh, by, you know, not necessarily outsourcing anything to AI, but allowing AI to make doctors’ life better and make them more efficient and better to drive the [00:06:00] patient through the care in a better way

Justin Brady: Yeah, I mean, like, AI– We’ve had this conversation on this show a lot, which is AI is extremely good at pattern recognition.

It’s very

very way better than

It’s already way better at it than people are, to a degree. Um, you know, they’re– People are good at emotional pattern recognition. I believe that AI may never figure that out entirely, but AI is really, really good at complicated pattering- patterns and seeing, um, um, seeing patterns develop essentially, which is what you’re doing. You’re looking at… Now explain this, because you’re able to get so much–

AI needs a lot of data to train, and you have the data because you– of, I think it’s, um, the Iowa Neurological Patient Registry,

right? Yes, it is. So-

Michal Brzus: exactly. So, um, we are a University of Iowa spin out, uh, where I worked on this project, uh, during my PhD at University of Iowa. And this is an ongoing project that’s, I think, been 48 years now since 1978 where-

Justin Brady: Oh, ‘ [00:07:00] 78.

Wow.

Michal Brzus: Yeah, Antonio- I know, saying like-

Justin Brady: mid-’80s or something. Goes

back further

Michal Brzus: it depends what you will point out as the actual start, but yeah, Antonio and Hanna Damasio who are like godfathers of neuroscience and the whole, whole subfield of neuroscience was basically started in Iowa.

And up to this date, and those are already outdated numbers, been 4,500 people already enrolled in the registry. And, uh, we have over 1,000 patients with this more modern imaging as well, where each patient has, uh, quantified, like measured brain lesions, so the brain damage that is localized. And the patients also undergo a detailed kind of behavioral testing to measure their resulting function, which really, Iowa’s the only place in the world with that extensive data set like that that allows you to connect this local brain damage to the patient outcomes and model based on that

Justin Brady: Yeah. So the reason you’re able to do that is [00:08:00] because of this long historical set of data.

And so what’s really interesting about this is, um, you’re not relying on, what’s the word? You’re not relying on scanning technology that was just invented, right? You’re, ’cause you’re, you’re, the AI is reading existing MRIs, I believe.

And so these existing MRIs,

Technically charts go

I mean, how, how, how far back do charts go? Probably early ’80s is when M- MRIs

were ear- like popularized in a

clinical setting, right?

Michal Brzus: actually it’s also– it could be a big problem for a lot of other kind of, uh, fields in kind of medical imaging. But, uh, we are a little bit lucky because basically since very early 2000s, basically since the year 2000, the protocols for the MRI, the way the imaging is done and was used clinically, has been remarkably stable.

So even those 20-year-old scans, in a lot of ways, uh, are very usable for us. Of course, the quality of the image is [00:09:00] gonna be worse.

But this is also a benefit for us because we have a very good mix of different scanner models and, uh, and the quality of data which makes the model more robust to changes.

Because the big problem with a lot of research tools is they are, you know, fine-tuned to a specific scanner model that all the data came from, and, uh, they’re really good at that, but when you apply it to the real world, they start failing just because they didn’t see, like, all the data. They only- Right

kinda look through this narrow window of specific type of imaging. So, uh, this just makes our models and the whole system better because we expose it to so many different, uh, you know, manufacturers of the MRI machines and, uh, quality of data and the, you know, the sheer amount of patients.

Justin Brady: I think some of the concerns with newer technology in the AI space and the hardware that comes along with it is, wow, that’s really, really cool, but we’re not really gonna get any [00:10:00] benefit from that until like two, three years out when we’ve scanned a bunch of patients and when we’ve already used this technology to ingest new information.

But what’s cool about this is you’re saying, actually that doesn’t matter because our technology can go back for the, you know, for the non-technical, our technology can go back all those MRIs, all that data we’ve been collecting, like what would this be by now? Millions of patient files since the ’80s. Technically, we could go back and use all of that data to predict future outcome for stroke patients in a clinical setting right now

Michal Brzus: Yeah, so that’s actually, there’s one, one small caveat there. So that is very true, and that’s why you see this huge now, uh, very fast adoption of the different radiology AI tools

in clinics. The problems that you can do it with imaging, but to make those predictive algorithms like what we’re trying to do here, you need the imaging plus this behavioral testing [00:11:00] done after.

you don’t

have that. Because we have that at Iowa. Yeah. And a couple other places have some data sets that can allow you potentially do it. Um, but, uh, that’s our basically competitive advantage at, at Iowa, that Iowa is basically the only place in the world that you can do it at this point because we have combination of both, imaging and the testing,

uh, of the

Justin Brady: interesting cause you need… Okay, so the imaging is key, but the imaging doesn’t show you the full picture without the, uh, the additional data that your clinicians have been collecting

Michal Brzus: Y- uh, yeah. So basically, you know, all the radiology AI, all those different tools, they are focused on helping just the radiologist. They look at what already happened, what’s on imaging, and making doctors better with that. We wanna take it from kind of looking at what happened to drive what’s next for the patient, this kind of the beginning of predictive neurology.

And, uh, to do that, you need to have the dataset of testing, because there’s a lot of research projects and a [00:12:00] lot of, you know, uh, neuroscience where we learn what part of the br- part of the brain is responsible for what function, right? But without the ability to have some hard measures you can measure against, you don’t know if you’re actually just guessing or actually working well.

S- and the FDA now has, uh, also a hard job because there’s so many, uh, products that people try to develop, but their job is to make sure it’s safe for the patients. So, um, without having this dataset you can test against patient outcomes, you could only kind of give suggestions that this is what we think is gonna happen rather than having something to test against and actually be able to say that we have this and this accuracy, we can really predict it, and therefore prove to the regulatory bodies that it is safe and beneficial for the patient.

Justin Brady: So what kind of delay in like the, in a, in a current setting without use of AI? ‘Cause it’s not FDA approved yet. You’re, you’re [00:13:00] still testing. This is still in the lab, I

guess is

Michal Brzus: We hope to, to file to FDA, um, by the, by the end of the year,

Justin Brady: Oh, goodness.

Michal Brzus: to get it into the market next year. Yeah.

We’re working, We’re working, hard.

Justin Brady: This is side note, side note. Um, you’re currently in the FDA process, right?

Or getting ready for

Michal Brzus: FDA has multiple steps to really help the companies- Yes, they do. … uh, but there is also a lot of steps. So we completed earlier this year what’s called the pre-submission process called Q-sub that you can actually show your initial evidence and the idea to FDA and get some real feedback that can help you to both de-risk and prepare better the actual submission.

So we’ve done it earlier this year, and then now working hard on, uh, basically based on the, uh, things we learned from FDA talking with the experts, uh, to prepare our, uh, submission, uh, to get to the market next year

Justin Brady: Do you have, uh, I don’t know what the right word is. Do you have an [00:14:00] inroad or is there less red tape for you? ‘Cause I know the FDA process is a bear and people, uh, a lot of startups complain about how awful it is and how it’s impossible to get through. Do you have kind of a fast track or a red tape free track because of the university setting or have you been running into a lot of snags trying to get things approved through the FDA?

Michal Brzus: Um, there is no fast kind of road to, to skip any of that.

Uh, but the, for… As we are a basically diagnosis support system, we are not trying to replace doctors, right? We are classified as a Class 2 medical device, where, you know, Class 3 would be implantables that are, like, very high risk for the patient. Uh, so we are kind of step below it in Class 2.

So there is two, uh, two routes. One is called De Novo, where you have to basically show that this is a completely new product, and the testing and the amount of evidence and the timeline is much longer and more expensive. And the second track is called [00:15:00] 510K, where you find existing solutions that are approved by FDA, and you basically show that this is your predicate device and you are substantially equivalent, is the word they use.

Uh, and you can leverage other people’s products to go into a quicker route. It’s still gonna take, you know, couple months and quite a bit of funds to, to go through it properly, but, uh, it’s, uh, easier and de-risks the, the submission process

Justin Brady: Yeah, so I wanna go back to the core problem you’re solving, ’cause this is fascinating. Basically, right now, the current protocol when someone is– has a stroke and they are in the recovery phase, is that initial approach with them from their caretaker, the clinician, whoever’s,

Whoever’s overseeing

them in a medical setting, is it initially a wait and see kind of ex- like kind of a… Is it initially a wait and see kind of thing ’cause they don’t know what they’re doing, they’re, they don’t know what they’re dealing with just yet, so they’re looking for special markers, and [00:16:00] then they make the decisions, and the decisions are too late, um, and if they would’ve made those decisions earlier, there would’ve been better outcomes?

Is this kind of one of the things that you’re correcting for with this?

Um- That was a terrible last question …

Michal Brzus: no,

Justin Brady: it,

But

Michal Brzus: Was it right? It’s great because, you know, it shows a lot of, kind of, questions, and also, um, it’s a pretty complex ecosystem, stroke, right? So I’ll… How about I’ll give you a case study that kind of help- like, basically gave us the idea to start this, and then from that- Okay, great

I’ll be able to show you. So there was a case of a 62-year-old woman who was Iowan. She was basically running their family farm. She was doing accounting for the, for the family farm, and they were sitting by the TV with the family, watching Wheel of Fortune. Um, and suddenly she felt kind of dizziness. She had issues w- operating remote and coming up with words, so they rush her to the hospital, right?

In a hospital, the first thing they do when y- they have a patient with code stroke is the CT scan. [00:17:00] Uh, CT scan showed nothing. Then the, she had an MRI that showed a small lesion, but she also scored one on a, what’s called the NIH stroke scale, where one is basically the best you can get. So she was discharged, that she’s gonna probably recover well,

Justin Brady: Oh

Michal Brzus: uh, because it was a mild stroke with a very small, kind of, brain damage.

Uh, however, she still struggled, so at her three months post-stroke, uh, follow-up with a neurologist, um, she was referred for a larger neuropsychological evaluation that happened six months after stroke, and that evaluation showed severe impairment in many, many domains. And she was unable to really get back to her normal life and work, which, you know, it was also a big burden for the family farm as well, right?

Uh, and, you know, she wasn’t, like, that old, uh, either way. Um, so the problem is that it is now predicted that one in four adults will have a stroke. And, um, the [00:18:00] initial focus in a, in a clinic is to save patient life,

Justin Brady: Ühüm

Michal Brzus: So there is two ma- major types of stroke: ischemic stroke, which is like the blockage in the, in a vessel that, you know, parts of the brain stops getting supply of blood, or the brain bleeding.

So the CT scan is very quick and can basically answer that question if it’s a brain bleeding or a blockage. Mm. Because, uh, the first thing you do… And 80, 87% of strokes is the blockage in the brain, not bleeding. Uh, so the initial step is to give patient like a blood thinners, basically. Mm. It, it’s not blood thinners, but like a compound that, that supposed to help, you know, restore the circulation in the brain.

But if you would give that to the patient with the brain bleeding, you’re basically killing them. So the initial CT is to make this quick decision to get this initial intervention to the patient as soon as possible, because everyone says that time is brain. And then after that, [00:19:00] uh, typically within 24 hours, patients will have a MRI, because MRI is more detailed and a higher kind of tier of imaging that shows really well the actual extent of the brain damage, and that’s where we come in to really, um, analyze this and create a report.

So there’s a lot of competition in the kind of the CT space. A lot of companies have their algorithms, but there’s not much to really help doctors in the MRI and, uh, to allow them to help drive the care better, which is why we decided to go this way because we really saw it as the big hole in the, in the way that current care is being done.

Justin Brady: So this is interesting. Um, right now, how many– Like, it

Sounds like right now

the initial protocol for a stroke is very limited. People kind of have to wait and see. In this, in this scenario, they missed something.

But Not

not necessarily they didn’t do their job, they just didn’t have the data,

and had They had the data up front, it would’ve con- it would entirely change the recovery protocol.

Would this, [00:20:00] uh, patient have been able to return to work and have a normal, uh, quality of life had she had that data up front?

Michal Brzus: Have that data We can’t answer that for sure,

but, but, uh- yeah. It really … what i- what is known is that the first three months post-stroke is the period of the, what we call the highest brain plasticity,

which allows for the best recovery chance. And after three months, it’s a steep decline of the ability of brain to kind of restructure itself, and after six months, basically, that’s where you’re gonna end up for the rest of your life.

So that patient actually had a pretty quick turnaround, three months post-stroke checkup and then six months after testing.

Justin Brady: See, I think most people have an assumption, me included, because I don’t know medic- medicine that well, right? Uh, but I think most people have an assumption once the event happens, the stroke happens, it’s too late, recovery’s just gonna happen the way it always does and your body takes over. But what you

guys have found out is

once the stroke happens,

I mean, yeah, we can’t…

reverse the initial stroke, but

Recovery

if we have data up front, is dramatically different, at least in [00:21:00] theory.

Michal Brzus: Yes. And, uh, there is a lot of studies being, uh, done right now in both the rehab space and, and the neuroscience space that one, like prove that the first three months especially that, uh, 30 to 90-day period as well is very crucial. And, uh, the targeted rehab is also beneficial, uh, to the patient rather than kind of generic

Justin Brady: us about– Yeah, so tell us about targeted rehab and what that looks like.

I think it would help people, and if you need to go back to something that I, um, didn’t touch on, that’s fine. But, um, I think it would help people to understand, like, what’s targeted repa- rehab or what’s an A/B case for a stroke, uh, victim?

Like A case being like we just

went about it

normal, and B case being, okay, we saw these signs, therefore we did X, Y, and Z

Michal Brzus: Mm-hmm. So the one thing I wanna come back to ju- Yeah … to just make it clear is that there is a lot [00:22:00] of procedures that are life-saving and great, uh, in that initial intervention. So for example, there is thrombectomy, where it’s like a mechanical procedure of going into the brain and removing the clot mechanically, and it, it has a great impact on the patients.

And, you know, in Iowa, there is only two places that can do it. There’s University of Iowa Hospital, the Tier I comprehensive stroke center, only comprehensive stroke center in Iowa, and I think MercyOne in Des Moines that has the advanced thrombectomy, uh, ability. So all the stroke patients in Iowa go there basically.

Mm. Just two locations.

Um,

but after that, um, the, what would be the A and B case? Um,

Justin Brady: And that’s a hard one,

Michal Brzus: yeah, w- w- so one patient, you know, can kind of go like what happened. Uh, they go through the system, then, you know, they get out of [00:23:00] the hospital, go to their family physician to get s- kind of evaluation, get a referral potentially for rehab or the other evaluation, right?

And, uh, they kind of go like it is being done right now through the system, and it often leads to rehab starting in this period where basically we know that the rehab has almost no actual effect on a patient. Of course, it’s, it- it’s always helpful, and, uh, one thing I wanna clarify is that there is the brain plasticity, where this is the actual ability of the brain to restructure itself, r- kind of like rewire itself and improve, and then the patient learning how to get better to live around their limitations, not necessarily improving the limitations, right?

So, um, in, in the rehab case, there is always the, the mix of both, right? One is the, your body and your brain actually improving, which we know that this basically ends at six months. Okay.

Justin Brady: Oh my.

Michal Brzus: And [00:24:00] then the patient can still improve, but the improvement comes from patients being better at living around their limitations.

Justin Brady: Goodness. So the, as soon as a stroke happens, obviously the st- the, the time, the time starts ticking. And the more data you have up front, the more you can actually effectuate and change patient outcomes. The problem being current technology is very limited in how we look at an MRI. We can look at the damage, we can say, “Okay, we, we know you have damage.

W- we I guess we’ll, we’ll help.” But we don’t have predictive, um, which was what AI is prom- uh, is, which is what AI promises to deliver, and this is what you’re working on.

Um,

it’s absolutely fascinating

and I know that, like, uh, University of Iowa as well, I think the company’s called Digital Diagnostics. I think Dr. Abramoff over there, he’s, he’s doing something, uh, not

Similar, but he’s using AI to scan retinas for diabetic retinopathy.

And So

naturally I’m like, “Okay, you’re doing MRI scans, [00:25:00] predicting outcomes for stroke victims. Cool.” I gotta

ask what, where else does this

apply? This has got to apply in other fields, right?

I think you

guys are working on

TBI, tumors,

epilepsy.

Michal Brzus: not yet. Uh,

Justin Brady: Or you could

Michal Brzus: it. I, I wanna, I wanna give also a huge shout-out to Dr. Abramoff and, and Digital Diagnostics, they previously been called IDX. They were the first ever FDA-approved autonomous AI system in medicine, coming from Iowa.

Yeah. So we have a, you know, great, uh, history of really leading this, this field, which is just wonderful, and they, you know, they had to go through a much, much harder route, uh, to establish kind of this precedence for everyone.

Justin Brady: That’s a good point. ’cause he testified to Congress on how these-

uh, systems and regulatory stuff

should be set

Michal Brzus: Yeah, so they did a lot of work that thankfully we don’t have to do now , which is, you know, we’re very grateful for that, and, and they’re wonderful.

Um, but yes, and what was the second part of the question? Sorry.

Justin Brady: Yeah. What’s the-

what’s up next?

Like, if we’re treating [00:26:00] stroke victims, um, do we push into TBI? Do we, a traumatic brain injury, do we push

into tumors, epilepsy? How far does this take things? Uh, how, how, how big is that crystal ball you’re

Michal Brzus: That, that’s a great question. That’s kind of what we are the most excited about because, uh, and again, I have to go back to the University of Iowa, this Iowa Neurological Patient Registry. It has data from patients that are not just stroke only, that we have multiple it- etiologies of, you know, different conditions that happened with including brain tumors, kind of resections of the brain, uh, TBI.

Uh, and we already found and have in published research, um, that the models can apply to, uh, to other conditions very well, because after all, it’s a localized brain damage, right?

So we are now– It’s accepted, it’s not published yet, but in pediatric data, I think it’s post-stroke or lesion-derived epilepsy [00:27:00] prediction.

And, uh, we are working hard on, uh, on all of those aspects, right? So the University of Iowa research group, uh, led by Dr. Bose and, and, uh, other, uh, people like Dan Tranel and other, uh, PIs in there, making great research in both the, um, prognostications, uh, from localized brain damage, but also on the rehab side, like TMS, it’s called transcranial magnetic stimulation, I believe,

which, you know, based on the condition, you can use TMS in a specific way to really help patients with recovery, suicide prevention, and other things.

So, uh, to answer your question now back, ’cause I’m started rambling a little bit here. Um, yes, we are very excited ’cause we already see proof of concepts that this technology, slightly adjusted, will be able to, uh, to transfer to all types of strokes, traumatic brain injury, surgery [00:28:00] planning, so you can potentially plan the surgery in a better way to reduce patient risk of where in a brain you’re actually gonna be cutting in, how much brain to take, and, uh, things like that.

So the, uh, possibilities are extremely exciting because we can really change the way neurology is being done from this predictive, uh, place where we are trying to, from day one, to limit patient risks and, and improve their outcomes for, for basically any localized brain damage injury.

Justin Brady: I, I would say final question is, uh, and I know there’s probably not a definitive answer on this, so I’m not looking for an exact number. But very, very broadly, very generally, um, how many different protocols do you think there are for a stroke victim? In other words, you d- you- your AI looks at the scan and says– and starts to narrow down 2,000 different protocols into one, five thou- well, a couple hundred, 10?

How many different protocols do you think there would be for stroke victim [00:29:00] recovery once the data’s collected?

Michal Brzus: It’s a great question. I am not a rehabili- uh, rehabilitation specialist. I’m not a medical doctor as well. Sure. Yeah, yeah. Uh, but, uh, w- based on, you know, how much research is being done right now in the rehab, in the targeted rehab, and how we can basically, uh, run specific rehab protocols, uh, there’s gonna be a lot.

And, two, I wanna also be clear about the limitations of our system right now, right? We are– We don’t have any product that we’re selling right now.

Right, We are pre-clearance. But our initial focus is on really trying to make the whole system work better and provide the information that is currently not there.

A- and, you know, potential integration with telehealth and, and other pieces that will really help the patients and the doctors drive the car the best way. And the rehab is something we’re gonna be looking more in the future as a kind of follow-up to that. So we have to still, unfortunately, stay [00:30:00] with a narrow focus because, you know, FDA is already a hard obstacle to go through.

Uh, so we have to kinda stay focused on what we have, uh, before kind of expanding to rehab. But long term, we really see a potential to either work together with some rehab companies and, and things like that to have basically, from day one, the proposal of the best, uh… How, how the best potential year of care for that patient would look like right away.

Justin Brady: Yeah.

it’s Incredible.

and we’re really happy that you’re building this right here in Iowa, of course, utilizing the Iowa Neurological Patient Registry. So every patient that has gone through strokes, um, in Iowa here, we’re gonna be able to benefit off that and save lives in the future. Amazing work you’re doing over there.

Uh, Dr.

Michael Berdoust, CEO of NeuroPred. Thank you

Also

for putting up with me

butchering of your last name.

[00:31:00] Uh, Thank you so

much for coming on the Iowa Business Podcast.

What is the

website, or how can people go learn more about what you’re doing over there?

Michal Brzus: Uh, yep. We are at neuropred.com. So if anyone’s interested in just talking to us or connecting, please feel free to reach out.

Justin Brady: Thanks for coming on the show

Michal Brzus: Yeah. Thanks so much