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E101 - Gaized and Convicted with Ken Fichtler

There are very few, if any, reliable methods of measuring cannabis impairment.  Road-side sobriety tests done by trained Drug Recognition Experts are very subjective and workplaces are limited with how they assess employees with safety sensitive jobs.  Back by video evidence, Gaize offers real-time impairment screening within 6 minutes.  This portable AV device is considered accurate, providing objective eye behaviours when deciding when an individual is impaired on Cannabis.  Considered the first impairment detection platform released to the marketplace, Gaize will search for answers by finding the six ways your eyes give you away.  

Episode Transcript

Trevor: We're back. 

Kirk: Hey, Trevor. How's it going? 

Trevor: Good. I haven't seen you in person for a while because you were a long way away. 

Kirk: Yeah, I had a fantastic non-vacation vacation. I spent. I spent four weeks down in the East Cape of Baja and visited some friends down there was basically just lived in Baja. 

Trevor: Let's pretend I'm from Dauphin, Manitoba. Where on the planet is Baja? 

Kirk: Well, the Baja Peninsula, Right. I mean, we know that from the Westerns in the old days. Well, right down, right down on the very tip, the southern tip is Cabo San Jose. 

Trevor: So, you're in the country of Mexico. 

Kirk: Country of Mexico. It's called California, Big Sur. So, the Baja has two states in Mexico. And I was in California, Big Sur, on the eastern tip. So ,I was outside the window at a house I was living in, and I was looking at the Sea of Cortez and the Pacific Ocean. We were about 50 kilometers away from the Cabo airport, which is, you know, the busy tourist area. And you go on a sandroad and they don't build up roads there. They scrape roads down, so they scrape into the sand. So, we arrived at around 5:30 - 6:00 in the evening. And of course, by the time we hit the road at 6:30, it's dark and we wake up in the morning and we're looking over the Sea of Cortez and it's like, Oh my God. So, we're in the middle of a middle of a community of maybe 20 houses, gravity fed water and solar panels. And our job was to house sit and care for a dog. So, Michelle and I basically walked the desert, walked the beaches, saw turtles laying eggs, threw sticks into the surf, heard coyotes, you know, went to farmer's markets, and just hung out in Mexico for four weeks. It was wonderful. 

Trevor: Excellent. While you're away, because, you know, apparently, I don't like warm weather. So instead, I went winter camping with our friend Pat, his son and my son. So that was sort of the long weekend in February. And we slept in a tent in the cold and went fishing. And of course, you know, instead of catching fish, we got Skid doos stuck in slush and other people had to rescue us. And that was only day one and then day two, Brent's son is sort of a we'll call him an outdoor teacher. He sort of has a, we'll call it almost like an outdoor daycare slash school. Anyway, he's outside all day long, so he decided to take us on a snowshoe. But, you know, not down some trail like bushwhacking the whole time. So he could collect pine resin. Then he convinced us that it'd be a great idea for us old farts to follow him down a semi cliff down to a lake. So, you know, sliding on my butt down a hill in snowshoes, wondering if I was going to get up at the bottom anyway. Good, good times. 

Kirk: Well, we had we had a northern wind coming down, coming down the Sea of Cortez. So, we had to wear sweaters at night. 

Trevor: But, you know, it's almost the same. But so now we're sort of updated, though. You found an interesting guy with an interesting company doing some techie virtual reality stuff, one to sort of lead us into who we're talking to and about today. 

Kirk: Yeah, I'll let him introduce himself because I like when people do that. They pronounce their name correct and all that. But it's a product. It's a product that really excites me. I think it's kind of cool. 

Trevor: So Ken and a magic VR helmet that's going to tell me whether I'm stoned or not, maybe kind of something. 

Kirk: Yeah. Ken is an entrepreneur. What I like about this story is that I like to talk to entrepreneurs. This is a this is a fella from Montana, and he's involved with the government in Montana. He explains his story and he was thinking about how will, how will cannabis affect his community. And he figured, I love this, he says. He says that there wasn't a lot of drawbacks with cannabis coming on market except the fact that people could be driving. So, he thought, what could he do to help with that? Yeah, you picked up on that eh. His job, his job is to analyze the effects cannabis will have in the state of Montana. And his answer.

Trevor: Not a lot. 

Kirk: Not a lot. It's going to actually benefit them, you know, benefit commerce, benefit business, which I thought was really quite interesting. But however, he did see a drawback with people driving and handling heavy equipment and safety. So, he did some research and came up with this gadget called Gaize. G-A-I-Z-E. And it's a real time measurement of impairment, specifically to cannabis. Now, he gets on to this about how he hopes to, you know, get more data into the system where he'll be able to expand it to other substances. But right now, right now, he's focused. He's focused on cannabis. 

Trevor: Yeah. And we'll get into this more afterwards. But there are some terms he uses like nystagmus and eye movement. And you actually do that a fair bit in sort of your day-to-day clinical work. But I think he does it not a bad job of explaining all that and then will of course throw our $0.02 in at the end. So, so we just let Ken take it from here? 

Kirk: Yeah, I think so. But a couple of things. So just so people understand. We talk about nystagmus a lot in this because it's sort of the movement of the eyes is what nystagmus is and it gives you a window into the neurological assessment of your client that your patient.  In Gaize, his machine tests for six and out of the six eye movements, two of them are very specific to cannabis. So, as you're listening to this interview, ensure that you hear that because we do use the word nystagmus a lot and some of the movements aren't necessarily nystagmus, they're how are your pupil responds to light, which is not nystagmus. So, so just be aware that that we're using the word nystagmus. But it doesn't describe all of the six movements. Does that make sense? 

Trevor: I think so, yeah. 

Kirk: Yeah. So, yeah, let's get into the discussion and then we'll come out and get a little deeper into it. 

Ken Fichtler: My name's Ken Fichtler. I'm the founder and CEO of Gaize. We are creating what I believe is the first cannabis impairment detection device that works in real time. And basically, what we've done is we've taken the drug recognition expert eye tests, which are the track of my finger tests that are, you know, pretty commonly portrayed on movies or TV. And we put those in a VR headset. So, programed them to run exactly according to the training manual. And we can very precisely control the amount of light that is entering the eyes and things like that. And so what we do is we run through the the exact same tests that the cops use and we capture eye movement data and video throughout that process. We provide the video as evidence to law enforcement and the eye movement data then gets analyzed by machine learning and statistical algorithms to determine whether or not there is signs or symptoms of impairment that we can detect in the eyes. 

Kirk: How old is your company? 

Ken Fichtler: We were incorporated in January of 21, so we're about two years old almost. Exactly. 

Kirk: Cool. Cool concept. I mean, I've been doing some studying. I'm on your Web page. Your web page is very informative. It has lots of studies out there. And I'm looking at the 1977 study about how, you know, the difficulties that individuals have with determining impairment. What gave you the spark to say, hey, I can, we can create this device? 

Ken Fichtler: Yeah. So, I was the Director of Economic Development for the state of Montana for four years under Governor Steve Bullock. And in that role, I was sort of charged with understanding what was going to be the impact on, you know, various initiatives. And one of the things that I was studying was Montana was a medical cannabis only state at the time, and I thought it was very likely would become a recreational state if we ever saw a ballot initiative. Medicine has come to pass. And so what I was doing at the time was trying to understand what the impacts would be. And so I was looking at, you know, what will be the impacts to the state economy, what would happen to tax revenue, what would happen to the businesses and law enforcement. And it was all really positive with this one big exception, and that was that there was no device to check for impairment. And so, law enforcement and business owners were very concerned that they were going to see this spike of impaired drivers and workers. So, I initially approached this problem from the perspective of just, you know, understanding this was a huge opportunity one. And I thought that it was important to solve this problem in order to make sure that cannabis legalization could proceed and proceed safely. So, I looked around at the companies that existed and nobody was doing it right. Everyone was trying to measure impairment by looking at the amount of THC in the body. If you look at any of the science, what becomes very, very clear immediately is that you cannot look at the amount of THC in the body and derive impairment from that number. There is there's simply no predictive amount of THC in the body and no consistent amount of impairment is experienced on any number, on any amount of THC. So, from that point, it almost became more interesting to me and I thought, well, nobody seems like they're doing this right. And if it was to be done right, what would it look like? And so what I thought it could look like is what the tests that we know work are these drug recognition expert tests. They've been shown to be, you know, reasonably successful at determining whether or not someone is impaired on cannabis and many other drugs. They are not widely used. There's not enough DREs in the country. These Drug Recognition Experts are put through a very rigorous training. It's very difficult. It's not an attractive thing for most cops to do. So, there aren't enough of them. And obviously businesses don't have access either. So, my thought really was what would happen if we automated these tests and could that be done. And that really led me down this path of looking into what tests were most predictive, of what tests were subjective, what are really the shortcomings of doing this through human. And there are some you know, really obvious correlations when you start looking at it from that perspective. And that is the eye movement tests are by far the most objective. The limitations of a human are obviously you know, you've got a lot of opportunity for human error, you've got an opportunity for subjectivity, bias. And then these officers are performing the test based on memorized test procedures, which is, you know, probably never a good thing for trying to get to real consistency. So, I thought you know, we could probably automate these tests using a VR headset. And so, and if we did that, we could probably track eye movement using these very high precision sensors. And if we did that, we could probably use that data and then train machine learning models to recognize impairment. And so that's really what we've done. 

Kirk: Very cool. Now, economic development really isn't I mean, it's social science. It's not a lot of science science. So how did a social science guy get into the science of all this? I am making an assumption, making an assumption that you're more into the social sciences, but. 

Ken Fichtler: Not exactly, I mean, I've had a couple tech companies in the past and the economic development job was really a departure from what had been the norm of my career. It looked like a really interesting opportunity. It certainly was. And I feel like we did some great work in that role. But that was that was more of the aberration for my career. 

Kirk: Okay. So so a little background on your career would help. I want to get into the science of your product, but tell me more about yourself. 

Ken Fichtler: Yeah. So, I'm a multi time entrepreneur. Native Montanan. I spent some time in a company doing a turnaround up company that was doing infrared optics manufacturing. I had a search engine optimization company, I had a web app development company, I had a marketing company. And so I've sort of had a couple different sprints in my career. And I what I have sort of said in my specialty is, is thinking about hard problems and designing solutions around them and then building a team to tackle that problem. So that's what I've done here. I'm not the scientist that has done this research, but I have assembled a team that has done that and done it successfully. So, I'm more of a business guy. 

Kirk: Well, I congratulate you on this. So let's talk a little bit about Gaize, I noticed on your web page you guys did a 350 participant study on your product. So, did that study confirm your science or was it a study to help your product learn? 

Ken Fichtler: Sure. Yeah. So really, the answer is both. That data set is used as the training data for our machine learning model. We're also able to validate that our machine learning model is effective based on that same data set. So, it is sort of one in the same. I'm really though, that was the most important thing from that study was to capture a large amount of training data to create these machine learning and statistical models. 

Kirk: Okay, So when someone consumes cannabis because on your website you're truly focusing on cannabis impairment. So, is there a specific type of nystagmus that happens with cannabis consumption? 

Ken Fichtler: Yeah. So our product actually runs through, so, each of the drug recognition expert eye tests. So I'll list them. Those include equal tracking, lack of smooth pursuit, horizontal gaze or staggers nystagmus at maximum deviation. So that's the far left and right peripheries of the vision. Horizontal gaze nystagmus at 45 degrees from center and vertical gaze nystagmus. So that's vertical periphery. Lack of convergence and pupillary rebound dilation. So, each of those tests is looking at a specific thing. The horizontal gaze nystagmus test and the nystagmus tests overall are much more predictive of impairment on other drugs. Primarily alcohol. So we are certainly using that data in our machine learning model for cannabis. But it is not one of the tests that we're finding to be highly predictive of cannabis impairment.

Kirk: And I'm sorry, so what is your gaze isn't and nystagmus isn't?

Ken Fichtler: Correct. So, nystagmus is not one of the indicators of cannabis impairment that we're finding. It is occasionally present, particularly in extremely impaired people, but it is not universally present. So, we're really focused on the lack of convergence test and the pupillary rebound dilation test. Lack of convergence basically brings the stimulus towards your nose, the bridge of your nose, and tries to get you to cross your eyes. And the pupillary rebound dilation test exposes your eye to three different light conditions. So that's room light and then 90 seconds of darkness. And it'll allows your pupils to fully dilate and then we expose it to bright white light and that causes your pupils to constrict. We measure how rapidly they constrict and whether or not they stay in a constricted state. 

Kirk: Okay, so to review that. So your device does. Did you say five tests was like six tests. And out of those six, how many of them are predictive of cannabis? 

Ken Fichtler: Two are very predictive of cannabis. We're finding some signal and the other tests to be clear. But it is those are not the tests that we're really focused on. 

Kirk: Okay. But for the purposes of defense and law, you want all those tests in there and then they will focus up on the two. 

Ken Fichtler: Yeah, because really the vision for Gaize over time is to add additional substances. I want to create what I'm calling the first impairment detection platform. So a single device that can detect impairment from any substance. And I you know, I always start this conversation by saying I'm not opposed to cannabis legalization at all. I think this is a good thing. And I think it makes a lot of sense that alcohol is going to be legal. Cannabis should certainly be legal, but I think we need a way to make sure it's done safely. We need a way to ensure that people are not driving or working while high because, you know, there are extremely bad outcomes that come from that. So, the reason that we do the each of the tests that the drug recognition expert does is because we want our device to ultimately be able to detect impairment from any drug or any combination of drug. And so, by conducting the plurality of those tests, we can determine if someone's high, drunk, drunk or high, high on meth? Anything. We just need the training data and we can design algorithms to do that. 

Kirk: It's a very cool it's a very cool concept. So, walk me through your selling points to a law enforcement agency. How do you how do you say to a law enforcement agency, here's how our device will work in the field? 

Ken Fichtler: Yeah. So, the law enforcement use case really is a bit different than the commercial use case in that, you know, obviously there are criminal justice outcomes that are coming from these tests. And so, one of the things that we're really focused on for law enforcement is the generation of evidence. That's something that is of marginal value also to commercial customers. But for law enforcement, I think that's of huge value. Right now, when a law enforcement officer conducts a standardized field sobriety test on the side of a road, or when a drug recognition expert conducts their test back at the station, what evidence is generated really is only coming from perhaps a body-cam if it was activated or a dash-cam on a patrol car. There's not any eye movement data or video that's generated in this process, even though these are the most objective tests that are used. And so our device records eye movement from about an inch away from the eyeball. And so, it is very up close to very clear video of what's happening to the eyes and in the eyes from a perfectly conducted DRE eye test. So, we think that evidence piece is really, really important for law enforcement. The other piece of it, you know, right now, there's no tool that law enforcement can use to detect a cannabis impaired driver. It is really at the whims of the patrol officer, whether or not they think someone has impairment cannabis and whether they're going to bring them back to the station and conduct additional tests. So what we're providing is really a stand in for the portable breathalyzer test that can be used on the side of the road to determine whether or not someone is likely to be impaired on cannabis with reasonably, very high precision. Where our devices, it's much more accurate than even the best trained human drug recognition expert officers. And so while we encourage confirmatory tests to be done every time a Gaize device predicted someone to be impaired, it is certainly the best technology available for that purpose. And so, we think that that's the other really pressing use case. I think. 

Kirk: Okay. So I guess if I'm if I am sitting in my car being pulled over and the police officer, the law enforcement officer thinks that I'm impaired, he'd ask me to step out of the car, probably put me through the, you know, the walk and turn pass probably the one leg test. But instead of doing his subjective test or objective test of nystagmus, he put the Gaize on at the roadside. Or would that be something you would do in the in at the shop, at the jail? 

Ken Fichtler: Yeah, it can be done. In either case, the vision here is if someone is exhibiting signs of impairment in the roadside. They are blowing a zero on a breathalyzer. The cop has no other tools other than simply placing them under arrest. You know, that person could simply be fatigued, in which case they would probably be, you know, told to stop driving and just go home. Or they could be impaired in cannabis or another drug. And so Gaize could be used in a roadside setting. Gaize, you know, require that people are seated while they're going through the test. So, you'd probably lean up against the car or sit in your own car while that was being conducted. But the result of that test would be very clear data about whether or not someone is actually impaired. 

Kirk: So there's the officer would key in the person's name driver's license, date, all the all the tombstone information that they would use in a court. And then the computer technology then says, you know, there's a green light, the guy is a go, there's a yellow light cautionary, there's a red light. Oop, the signals are out, The guy is impaired. 

Ken Fichtler: More or less. So, we run through, as I said, six different tests. We provide information about whether or not the person is passing or failing each of those tests. And we provide a predicted substance of impairment and are confident that that person is impaired on that substance. And so the outcome for the patrol officer really is verification that, yeah, we're pretty sure this person is impaired, we're pretty sure they're impairment cannabis. Let's go ahead and bring it back to the station and do some other tests. 

Kirk: Okay. But if he pulls up myself, I mean, I'm in my sixties. Is there is there a difference in your study between demographics? Do like some people have nystagmus as a chronic ailment? So how does your how does your device quantify age, demographic, sex? Does it do anything like that? 

Ken Fichtler: So in our study, we captured all this demographic data. What we're doing right now actually is going through our machine learning model and ensuring that there's no demographic bias built in. So, for example, for detecting more females and males as impaired, then we need to understand what's happening there and remove any an existing bias. Any time you train a machine learning model, there's the potential ear training and bias based on what your training data is. And so we are making sure that that is not the case. And removing bias as we find it. There is you know, there are many genetic conditions that can lead to many different types of abnormal eye movements. And so another good reason for us to do the plurality of the tests that we use is that there are there's a reason that someone can fail a single test. But when we look at the tests in a collective, we can very precisely understand whether or not the eye movement behavior we're seeing is correlated to a particular substance. And so that's, I think, really, really important because there are so many reasons that you could be exhibiting nystagmus, for example. There are many reasons that you could have abnormally large pupils. But when you look at, for example, a failure on lack of conversions and a failure on pupil rebound dilation, and then some perhaps some other indicators from other points in the test, you can be very confident that this person is impaired on cannabis and not simply tired or drunk or something else. 

Kirk: Okay. Your product is still a couple of years old, so it's truly in its infancy and I can see how time goes on it's going to become more robust. So how is information gathered now? And I guess the other question is confidentiality. I'm going to make an assumption that if I'm working for Upper Snowshoe Montana Police Department and I'm using your product, I will have a database within my department that the information goes to that would have the tombstone information because I need for evidence. Does that that it now get pumped up to the cloud, to your to your grand device and you like and then you work with the data. How does that all work? 

Ken Fichtler: Yeah. So the data flow basically is we've got a we've got three different apps we've built. The first is a mobile device app that basically controls the headset. And so, the headset is a slave to the mobile device that allows the officer in this case or a, you know, HR person or whoever to press the start button and conduct to test. The data from there flows up to our cloud server where it gets processed using our machine learning algorithm, and that result then flows back down to the mobile device to alert the test administrator whether or not someone is high and if they're sober you can let them go. So that data is, there is no personally identifiable information. So privacy. We don't capture any personal identifiable information in that data set. And so even in our worst case, we were hacked or something that would never be correlated back to an individual. The final piece of software developed is a web app that allows, for example, a station to look at the tests that all of their officers are doing. And so if you're the chief of police, you want to understand who your which patrol officers are using Gaize most often you can go to this web app. You can see where they're conducting tests. How often. How many of those were positive results. Download test results, download evidence all that. 

Kirk: Okay. So so obviously you need cell service if you're doing it in on the scene or you bring the person back to the to the building and you have your Wi-Fi system. 

Ken Fichtler: Yeah, we're looking at ways to do on device. So, on the mobile device, do the machine learning processing there, unfortunately there's a huge variability in processing power amongst mobile devices. And so, whereas like the latest iPhone could probably run the model, I'm not sure that, you know, an older phone could do the same. So we'll probably get there. But that's a that's a future state that we're looking at. 

Kirk: Ironically, I'm thinking of law enforcement, but you're also marketing to manufacturers. So people that, you know, crane operators, people that are, I imagine, working in heavy duty devices. 

Ken Fichtler: Yeah. So, anybody that's in a safety sensitive role. So that would be anyone whose life is depending on someone performing their job appropriately. We're interested in selling devices into those positions. On the commercial side, the only screening tool that's currently available really is the urinalysis screening, and that is a distinctly retroactive way to look at impairment. You can tell that someone has previously used cannabis, but you cannot tell based on a urinalysis screening whether or not they're currently high. So, businesses really have no tools at all to detect whether or not someone is currently impaired on THC.  In the United States, I'm not sure what the Stats in Canada is, but in the United States, THC use in states where it's recreationally legal or where adult use cannabis is legal are starting to protect THC use as a class. And so, what that means is that you cannot, as a business now perform THC tests and take adverse action against an employee based on a positive result. You have to actually prove that they were impaired during their working hours. And so that's impossible for businesses currently. We're really providing, I think, one of the only ways that that could reasonably be done. And so our hope is that this can really drive safety radically higher in businesses that are that have safety sensitive employees. 

Kirk: Yeah, that's I guess that's where the skeptics will say just because my eyes are behaving in such a way, how do you prove I'm impaired? I may be a medical cannabis user. So, if I'm a medical cannabis user and I'm microdosing, my cannabis is Gaize going to say I'm impaired? 

Ken Fichtler: No, that's a really great point. So, the eye movement characteristics that we look at are only present when you have sort of demonstrable reductions in your ability to do equal tracking or to do divided attention tasks or reaction time tasks. And so, these are things that are only indicative of impairment that is, you know, above and beyond what you'd want to be operating a vehicle or equipment under. 

Kirk: Okay. So someone I don't know if this is a fair question, can I develop a tolerance, like if I've been using cannabis for five years and all of a sudden my employer says, here, put this device on. Well, buddy, I've been working for you for five years using medical cannabis. Is there a tolerance? Does the body develop tolerance? 

Ken Fichtler: Definitely. Cannabis has a has a actually a huge tolerance effect. So heavy users of cannabis will have a radically lower amount of impairment that comes from using the same amount of THC that an infrequent user can become extremely impaired. So, tolerance is one of the things that our device is particularly useful for because it is again, it's only detecting impairment that can be objectively sort of correlated to reduced ability to perform a job or to drive a vehicle. So, there's been studies that have validated this was a great one that came out last year from UCSD Center for Medical Cannabis Research, looked at what is the sort of impairment that happens driving impairment, in particular or in this case that happens from consuming cannabis. And they found results that were consistent with what we found. But basically, tolerance has a huge impact. And so. A person with a large tolerance for THC would have to consume much more cannabis to achieve these same failure states that a person who consumes very little cannabis would do. 

Kirk: I'm wondering if your data collection captures that. Like if I'm if I'm a chronic if I shouldn't say chronic, but if I'm a medicinal cannabis user and I get pulled over, I guess the officer has to make other determinations of why he pulled you over and then add that add that to the database that your device gives them. So would you be capturing that? Do they say in there that, you know, would they have prescribed would they have the list of prescribed medicines somebody is on that you would capture that? 

Ken Fichtler: We don't capture that data, but the officer would capture that. And so that's a determination the officer would have to make. You bring up a really good point, though, about tolerance and about heavy usage with THC. You know, the current state of the situation is that if an officer pulls you over and they suspect you're driving while impaired on THC, they'll do a saliva screening or a urinalysis screening or in the law enforcement case, most probably a blood draw and they'll look for THC in your blood. And if you're above a certain amount in Canada, I believe it's Five milligrams per milliliter if I'm not mistaken. 

Kirk: Yeah. Yeah. 

Ken Fichtler: Most the other states in the U.S. that have legislated legal cannabis have also established these per se limits. What's important to know about those is that there is no science at all that backs up the idea that you can have, you know, five milligrams per milliliter THC in your blood indicates that you're impaired. That science does not exist. And in fact, quite the opposite. There are many studies that have looked at this issue and verified that there is no amount of THC in your body that can be correlated to some predictable amount of impairment. So fundamentally, these five nanogram per milliliter per se laws are nonsensical and were created entirely to facilitate prosecution of cannabis users. And so our product provides a much more rational path forward and that we are looking at actual impairment as it is manifesting in the body and not some arbitrary amount of THC. So a heavy user of cannabis will always have THC in their body that is above the legal limit. And there was actually a case that came before Washington State Supreme Court last year that was it was exactly this where a guy had not used cannabis at 24 hours, was given a blood draw and was found to be over the legal limit. And he challenged the state on whether or not that was a rational law. And what I think is the most insane court ruling I've ever seen, the court came back and said, Yeah, we agree there's no science behind this whatsoever, but we're going to stick with it. Because if anything else. that was the state of Washington versus Frazier. And it's just, I think, a really terrible criminal justice outcome. And it's exactly the kind of thing that cannabis legalization is supposed to be moving us away from. And in fact, it has delivered, you know, exactly into this really bad situation. So, I believe that a device that measures impairment in the body is a much, much better path forward and should be supported by the cannabis industry because of this reasons I'm describing. 

Kirk: Yeah, no, I agree with you. If you're impaired, don't drive. And what we've what we've learned through our research, through our podcast is that some of the research coming out of Australia suggests that those that use cannabis will sometimes self-regulate themselves. Yeah, I'm too stoned to drive, man. Whereas, whereas alcohol, I don't know about alcohol, but the car can find its own way home. Right. One. One for the ditch. Off we go. So, I think there's a behavior difference between cannabis users sometimes and alcohol users. This leads me to another question, obviously, and I shouldn't say obviously. There are, what, 108 cannabinoids in the cannabis plant. So, your research is saying that right now, if someone's impaired, it's measuring THC impairment. I mean, CBDs we know, don't cause psychotropic effects. But is there any differentiation in your studies between the cannabinoids or is it just impairment? 

Ken Fichtler: Yeah, it's just impairment. So Delta9THC, the people in our study consumed flower. So, they consumed, you know, kind of dried flower cannabis. We've done some small amount of confirmatory testing with vaped and edible cannabis. But when you do more work in those areas. It appears, however, that the same impairment manifests across methods of ingestion. And so, we're very confident with our device from that respect. You bring up an interesting point, though. If you start looking at synthetic cannabinoids, it becomes a different story. And there's a there's almost zero science that has been done. And looking at how these synthetic cannabinoids impact eye movement or how the impairment can otherwise be characterized. And that is going to be, I think, a very difficult thing to get our arms around because there's just so many of them. 

Kirk: Well, we've often talked about this, especially early in this project. We've been doing this about five years. And very early on we often talked about how many times Trevor: being the pharmacist, fills a prescription that is a psychotropic prescription that's going to have some effect on muscle skeletal movements, going to have some effect on cerebral thought processes. And you watch as the person, you know, get into the parking lot takes one and gets in the car and drives away. Right. So we know that people. But it's okay because it's a prescribed medicine, right? It's okay. They can drive on it. So. Right. So there's your there's your device. I mean, nystagmus, other substances create nystagmus. So, as you said earlier, I guess you're hoping that the learning technology and your device will teach you about what other substances are being abused or used. 

Ken Fichtler: Yeah. So what’s really interesting about these tests is that each class of substance has a unique way in which it impacts eye movement. There's been not as much research done on prescriptions, frankly, but for each class of drug, so, each class of illegal drugs are regulated substance like alcohol, opiates, stimulants, central nervous system depressants, cannabis, inhalants, psychedelics. Each of these has a unique way in which it impacts eye movement. And we believe that we can detect impairment from at least most of those, if not all of them. The work is simply capture a data set that is of good enough quality and large enough number that we can put our models at it and train it to recognize that impairment. 

Kirk: Fantastic. I only got about 5 minutes left with you here. A couple of real quick questions. You're in the marketplace now. People are buying your product. 

Ken Fichtler: We are almost in the marketplace now. We have we have a good we have a really strong preorder list. We are, as I said earlier, we're eliminating the bias that we could have trained in during the course of our clinical trials. So that work is ongoing. And then we're doing a couple of things around eliminating false negatives and false positives in our model. So, there are people that for which it appears, and these are occasionally high, high tolerance people and some others we're still trying to understand the causes of. But there are some people for with consuming, the same amount of cannabis does not have the same effect. And so we're trying to understand what are the causes of that and how do we determine whether or not this person is actively impaired or not. And in some cases, it appears that some people are just not impacted the same way by cannabis. And so that's a bit of an interesting problem. But we're our intention is to be in the market by the end of this month and with a with a model that is radically more accurate than the best trained human. 

Trevor: Kirk: That was really interesting. I really enjoyed your chat with Ken from Gaize. Because we talked about a little bit right at the beginning, I'm going to pick your brain here again in case the people missed it. What's nice, nystagmus or nystagmus? What? What? Like what does it look like? What? What is it? 

Kirk: Nystagmus is when you're usually what happens is that you ask somebody to focus on your finger and you go the finger horizontally and you watch the pupil go and follow the fingers. And what happens is that normally a person's eyes will track without any variation. But with nystagmus, what'll happen is that your eye will track, but there will be variations or quivers little movements of your pupil, of your eyeball as it tracks across. So that's the nystagmus. Is that is that unusual quiver motion that happens when you're tracking. 

Trevor: Okay. And so now when you said the horizontal nystagmus at the extremities where relate to alcohol, not cannabis, that would be, you know, out when I'm looking way off to the right or way off to the left, that I'd get that quiver. 

Kirk: Right. Is that peripheral vision. And as you're as you're as your eye tracks to the periphery, there'll be a quivering there'll be a movement. That's not it should be smooth, right? It should not be any movement. 

Trevor: Okay.

Kirk: So you'll see a small quiver and it's abnormal, right? I mean, it's a lot of assessments on people as you're looking for something abnormal. The average person's eyes track without any alteration. 

Trevor: And the two that he said were they think closely related to cannabis impairment was loss of convergence. So, I think everybody can picture that. That's take the finger to the middle of your nose and you go cross-eyed and that they aren't doing that. And pupil dilation sort of not as expected. They said they'd have different brightness of light and there's sort of an expected how fast that pupil opens and closes and I guess it's not doing as normal. So, it does seem to be that the two out of his six that were more related to they think cannabis impairment. So that was very interesting. 

Kirk: Yeah. Yeah. But what I like about is that they're doing the six and this goes back way back in time. The web page. The web page has all their research and some of the research we're going to put on our web page. But going to their Web page, which is You'll find a lot of the research and they go back all the way to 1977 when the US government studied roadside testing and what it would take for people to be qualified to do roadside tests. So, what they've done is they've taken those tests that people do that the, you know, the police would do on the roadside, put it into an algorithm. And I think what's really cool about this business is that they're capturing all these behaviors you know, your lying eyes, right? You can't hide your lying eyes. And they're going to pick up, over time how your eye responds to substances. I found it interesting, though, that they're not tracking people's prescription drugs. I would think that they would want that in their database and some point be able to correlate common eye behavior's from certain substances. 

Trevor: He did say that he would like it to expand out to other drugs but that's a nice segue into I'm going to pull the usual Kirk: and say we have some other episodes in our library about driving We have E60 for Driving Under the Influence with Dr. Tom Arkel, an Australian researcher who put subjects on the road in the Netherlands with and without placebo cannabis to see what would happen. That what is fascinating. Still fascinates me. And then more related, this one Cause Agnostic with Driveable and kind of like you were saying Driveable guys, now it wasn't a VR headset, it was something more akin to an iPad but, the reason they call it Cause Agnostic is they weren't really caring what impaired you. They were looking to see basically, do you still have the reaction times necessary to perform safety sensitive things like, you know, our crane operator or whoever or driving? So just interesting that there's we've talked to at least two groups now that are using, I will call it, quote unquote, electronics to see to see what cannabis and or other things are doing to impair you. It's all very interesting. 

Kirk: I think it's fascinating. What I what I also find interesting is the nuances of this. You know, our culture is very comfortable with alcohol. In the sense that we're very comfortable to not drive when we're drunk. However, we have a huge issue in our culture about people driving drunk. But usually what happens is that when someone is under the influence of alcohol, we know the impairment because we've had how many decades of learning. You know, buddy, you're too drunk to drive. Give me your keys. Cannabis is something different. What I found interesting in what he's talking about here is that his machine is measuring impairment, not the amount of THC, you know, whereas other tests, blood tests, saliva test, they're testing that you have THC in your system. Therefore, you might be impaired. 

Trevor: Well, his isn't quite measuring impairment. It's much closer. And I think he made a great point that, you know, unlike alcohol, which like you said, we have decades and decades of, you know, a blood alcohol level of X equals impairment. Why? We've established that. And we've also established that a THC blood level of X doesn't mean anything. You know, if you are new to cannabis, you might be freaking impaired. If you use it every day because of medical or whatever, you might not be impaired at all. And what you'd really need to measure impairment at the roadside would be, you know, a tractor trailer with a, you know, pull someone off, I'll throw them in in a driving simulator. And that's the only way you're going to actually know if they're impaired. So, this is a good surrogate marker, but it's still not, you know, obviously the drive around driving simulator to see if someone's actually impaired is completely impractical. But this is this is a nice surrogate marker. That and big bonus is it doesn't require any blood tests. And back to what I think you were getting at earlier is, right now we don't have, we can take blood, we can measure a THC level, but that number is pretty meaningless. 

Kirk: Well, I interpreted it a little differently in the sense that in the model. The police officer is driving down the road and he sees something that makes him believe he's going to pull over this driver. And now there could be a check stop. It could be erratic, irregular behavior on the road. But for whatever reason, the officer has pulled you over, comes up to the car, you know, touches the driver's side panel on the back. So, you know, there's witness that he was there, that old trick and says, hello, sir. Driver's Registration. Now, if it's like, you know, This Hour has 22-minutes where they roll down the window and a cloud of smoke comes out at you, then he's got reasonable, right to say, you know, I'd like to do a test on you if he like if he can smell cannabis or he smells or smells a product in the vehicle or the person's behavior, or maybe he's asking some questions. You know. Sir, have you had any intoxicants? You know, I'm a medicinal cannabis user or whatever. He can then apply this device. And my interpretation is that through the interpretation of the two primary two primary tests that Gaize can predict that, yes, from the results of the test, we can predict this person is impaired. That's how I interpreted what he was saying. So I'm thinking, but before that happens in a law enforcement situation, there has to be a reason for the officer to have initiated the test. Now, from a commercial working on an industrial site, it may be Tuesday we bring Gaize on to the site and do arbitrary tests on people for this for safety, meeting occupational health and safety. And again, if you identify as a cannabis user or there's a reason to believe you use cannabis and they put these goggles on you this AV goggles and you fail these two particular tests, they're saying that you are impaired. 

Trevor: I really like that. And you mentioned a couple of times it's worth repeating. So again, like you said, getting away from the law enforcement thing, an employer with a safety sensitive. Workforce. And, you know, we're going to I'm going to use crane operator as my fictitious person. You know, if you right now, you could make him pee in a bottle. But that, again, doesn't really say much about their impairment level. And it also doesn't say what's going on right now. That might be I smoked cannabis three days ago. So the nice thing about gays is there really isn't any good tools that an employer has now to say whether or not it's a good idea for, you know, this person to go up in the crane. So this sounds like a good at least gives the employer an option before they you know, if we're going to do some kind of random testing or not so random testing of employees before they go do safety sensitive work. 

Kirk: Mm hmm. When we interviewed when we interviewed Ken, we interviewed him gosh, I think I interviewed him in January. This one's been on this show for a while, and I apologize for that. But he sent me an email. I was in Mexico, actually sent me an email February 13th and said that he had launched. So this this product is now out there. 

Trevor: Good, good. So February 13th, it went live. So and we'll have a link to the website on in the show notes. But yeah, if this catches your fancy because you're part of law enforcement or a safety sensitive employer, this is a tool that you can get your hands on now. Speaking of driving, I know you were looking up to see things at some stats and study. Try and get some current stats and study about impairment and driving in Canada and cannabis. What? What did you stumble on to? 

Kirk: Yeah, well, I receive updates from Health from the Government of Canada on new research, and this is brand new research out March 2023 in Health Promotions, Chronic Disease Prevention Canada. This document came out from the Government of Canada sponsored by and the study is called Impact of Substance Related Harms on Injury Hospitalizations in Canada from 2010 to 2020. So over a ten year period of time, they're studying how many hospitalizations are a result of substance related harms. Now 2018 was legalization of Canada of cannabis. 

Trevor: Specifically driving, or you could harm yourself in any way. 

Kirk: Any way at all. It's all substance related hospitalizations, right? So, there's 2,108,489 samples in this study. So, of course, poly-substance abuse rates the top.  The most people that go into hospital due to self-intended or what the terminology you use here, they use the intended substance related injuries hospitalization and unintended. In all cases, people that mix substances, opium, alcohol, whatever you're mixing with, that's the top dog. But what I found interesting is and is that the female the Intentional Substance related injuries so people that that intended to take the substances and ended up injuring themselves and in a hospital the highest risk group was females between 15 and 19 years old and females are. 

Trevor: Are we talking about like self-harm as in, you know, a suicide attempt that's probably in there somewhere. 

Kirk: Some of it's in there. Yeah, some of it's in there. But you know, but it's just it's, it's from 14 all the way all the way to 74, females rank highest in the age groups of intended substance related injuries. So, for whatever reason they were taking a substance and hurt themselves. Unintended substance related injuries. And this is fascinating to me the unintended ones are these 75 to 85 year old.  The highest level, 85 plus. Now, that is people who are on prescription medications and. 

Trevor: Took the wrong one or took too many. 

Kirk: Took too many, fell down. Falls, right. So, there's some cool statistics in here and they're not going to be surprising to everyone.  You know, everyone because, of course, alcohol. Right. It's a specific rates of substance related motor vehicle collisions that ended up hospitalized. The highest among these were 20 to 29 year olds, with a hospitalization rate of 7.2 in 100,000 for men, 3.2, 100,000 for women. So young boys, young men are ending up in the hospital, most for alcohol, for motor vehicle accidents. So, alcohol use concurred most frequently to falls and motor vehicle accidents. I don't think we're surprised about that. But where we might be surprised about when it came to cannabinoids. And here's the quote. "We found that cannabinoids account for less than 1% of all substance related injury hospitalizations, which is low relative to the proportions of Canadians and North American populations using cannabis." So, you know, this is again, I'm not promoting the use of cannabis, but I guess what I'm saying is that from the studies, from our own interviews, when people were so afraid of legalization of cannabis that we're going to have more vehicle access, we're going to have accidents, It's not happening. Well, it's not happening. It's not happening from statistics. It's not happening from our research. 

Trevor: And just because I can't resist your, you know, alcohol related falls. So a couple of my favorite books are Freakonomics. Well, the whole Freakonomics set. And now. 

Kirk: I love Freakonomics

Trevor: And now it's a podcast. And one of the things they quoted in one of their books was, you know, how unsafe walking and drinking was because, you know, far more people that they weren't promoting drinking and driving either. But they said if you look at the stats, you know, far more people, you know, get drunk, walk home, fall and crack their head on the sidewalk than anything else. So, you know, drinking and walking wasn't recommended either. 

Kirk: Yeah, well, I mean, now. Now. No, no alcohol. No alcohol is good for you in the new studies. But I look forward to seeing Gaize grow. I think this is a fantastic product from the perspective of another, just another tool to justify, justifies not the word, I guess another tool that we can use to just protect people, Right. I always say that one thing about driving, you know, I have driven a car in many different vehicles, in many different lands and different countries. And what I have found is that we all, no matter where you are driving, everybody wants to get home, right? We all we all drive to get home and we drive to protect ourselves from hurting other people because if we hurt, other people are hurting ourselves. So I think driving, I think driving is one of the biggest communist plots out there. It's one of the biggest social... Think about it's one of the biggest social responsibilities you have. And we pay to get the license right. We pay to get the physical. We pay for the privilege to do it. And we do it for all the same reason to get someplace safe. So I think these guys have a fantastic product. I like the idea that it's your lying eyes, man. You can't hide them. And I think this is the name of the episode. You can't hide your lying eyes. So good on them. 

Trevor: Yep. No, noninvasive. You know, we always like an AI story. I think this is very cool. I'm hoping to follow them in the future as they get more and more data and maybe more and more drugs in their list. But speaking of driving, Kirk, you and I took a road trip to Yorkton Saskatchewan, and you talk to a to a random hockey player in a in a bar we were at. 

Kirk: Yeah. 

Trevor: One of our what was set up one to set up this story. 

Kirk: Yeah. One of our buddies on our team we went to a Proye Cup which is well you are. 

Trevor: Fat old man hockey. 

Kirk: Oh, it's some of the best, worst hockey I've ever seen in my life. I loved being the trainer for the local Kinsman team. It was a lot of fun. Anyways, I digress. We were having lunch and I was again talking about our podcast with an acquaintance friend of ours, and he said, I said we were trying to get My Cannabis Stories. He said, I got on my cannabis story for you and let's listen. This is a buddy at a hockey tournament. 

Dan Sorochynski: Hi, my name's Dan Sorochynski, and I was asked to do a quick excerpt on what happens to me when I consume or smoke marijuana. Essentially, I get the munchies and everyone is aware of what the munchies are, except mine are incredibly violent. Meaning I eat everything and a lot of it. It doesn't matter how much I've eaten for the balance of the day. Once I smoke weed, I could out eat the largest man you've ever met. I will eat a full large pizza on my own without breaking a sweat. And then I'll eat wings. And then I'll eat something sweet like a chocolate cake. It's violent. So, weed is awesome. But be aware that the side effect is weight gain because you eat a lot. 

Trevor: So that was Dan. Dan from Winnipeg. It's always fun when a someone you know would be it's a funny story, you know. 

Kirk: Well. 

Trevor: The heart tugging ones are good too but you know. 

Kirk: Well, it's what I like about it. It's an honest story. The guy basically saying is that when he gets high, he gets the munchies. It's like, oh, gosh, how many years ago?  I got I got one from an acquaintance, a colleague up north that she got high going to a concert and couldn't drive, you know. So, I like My Cannabis Stories because everybody has a different experience with cannabis. And that's what makes this such an interesting substance to learn because everyone behaves differently. Do you want to talk about the Canada Cannabis Act? What we are, what we're listening to today on the radio? 

Trevor: Sure. So not sure when this is dropping. So, it was current when Kirk: and I were talking about it, but who knows what it'll be by the time this drops. But within the last 24 hours, Quebec said you can't grow pot in your own house. And that was the Supreme Court of Canada ruling. 

Kirk: Yeah, it's going to be interesting to see how that falls up because Manitoba has theirs sitting at the Supreme Court right now. So that's going to be sent we're going to be following. 

Trevor: Absolutely. So, I think I better say I am Trevor: Shewfelt. I'm the pharmacist. 

Kirk: I am Kirk Nyquist I'm the registered nurse and we are Reefer Medness - The Podcast. Go to our web page. All the research from Gaize is, all the things that Ken was saying about Gaize. It's all researched. It's on his web page. There'll be a link on our Web page. I will also have the research that talked about the stuff from the Health Promotions Canada that'll be on there. Go to our web page. It's searchable. Episode 101 Reefer Medness - The Podcast. Music. As you know, we usually ask our guys for music and Ken asked for to Tu Rumba. And man, I don't know about you, but I was rocking to it. I thought it was really. 

Trevor: Driving down the road. This is a great driving tune. 

Kirk: Its a great driving tune. Correct. 

Trevor: All right. Talk to everyone later. Come on back.