For questions at the end of the management's presentation. When we do break, we encourage questions. As a reminder, we are only taking questions through the web portal. If you are listening over the telephone, please access the web links mentioned earlier to ask a question. You can submit a question at any time. I will ask the questions on the air for everyone to hear, and Adrian and Andrew or Sarah will then answer. I am not going to reference any names, but simply read the questions asked. As we have a fairly large audience today, if I cannot get to your question today and it is not getting addressed during the call and can be, I will come back to you by email. I am not going to read the forward-looking statements, but I do state that they apply and I reference them on page two of this PowerPoint. With that said, once again, thank you for joining us. Remember, this is fairly informal, and we do encourage questions to help you better understand the business and its growth path. Now I'll turn the call over to Adrian to start his part of the discussion and presentation. Thanks, Glen, and thanks everyone for joining. We're going to kind of go through the Perimeter story, talk a little bit about the problem statement, go into the solution that we've got, and then get into market and a few other things. Let's jump right into it. This slide summarizes kind of from us as a company why we're here, right? We're focused in the cancer space. The problem we're trying to address is this idea that it's not an idea, it's actually the reality of many times patients go through cancer surgery and the surgeon doesn't remove all the cancer in that first operation, which then causes delayed full treatment paths as well as re-operations. This is the problem that we're addressing. Obviously, it has huge emotional impact to patients, but also has cost impacts, has healthcare follow-on post-operative therapy impacts. It's a very, very big problem and much more common than most people realize. This idea of clean margins is kind of what drives it. What the surgeon is trying to do when they're performing cancer removal surgery is get a clean margin, a margin of healthy tissue surrounding the tumor, right? That diagram on the left kind of shows what that is. Cancer in the middle surrounded by healthy tissue, that's a successful surgery. Unfortunately, what happens a lot of the time is the picture on the right where there's a place where there is no margin. The cancer cells are right up against the edge of the resected tissue. Unfortunately, that correlates to a high likelihood that there are some cancer cells left back in the patient. That problem, that situation, that's what drives a re-operation. Okay? This is what the surgeons try and do, and this is what we're trying to help them to do. The reason why this is why getting clean margins the first time is so important is because these positive margins lead to very frequently a repeat operation. Even with skilled surgeons, they really have a high what we call re-operation rate. It's hard to detect the cancer. In many cases, you can't feel it, you can't see it, so you can't really sense it from that way. Traditional imaging modalities like X-ray or MRI or ultrasound don't have the resolution to be able to see the edges of the tumor. That makes it very difficult to be able to tell whether as a surgeon in the moment whether you got everything out. We'll show you some pictures a little bit later on that help sort of bring that home. It's a big problem across many, many different cancer types. Breast cancer is the area we're focused on right now. About 23% of all breast cancer lumpectomies results in a positive margin after that procedure, which means those patients now have to, and those doctors have to now decide whether they're going to come back in for a second surgery, which is what frequently happens, or some other path of treatment. None of that is really a great option versus just getting all the cancer the first time. Not just lumpectomies, but also thyroids, prostate, and those are the ones we have listed here, but really this affects all the solid-state tumors. It's a relatively large problem. Here's an example from a patient's perspective, right? If the procedure is not successful in getting all the cancer out on the first time, what does that mean? From the patient's standpoint, it is a very high increase in post-op complications after multiple surgeries, right? 66% increase in post-op complications. There is a delay in chemo or radiation after the fact. There is compromised cosmesis, which is typically why someone would get a lumpectomy, to maintain good cosmesis, but that is compromised now for multiple surgeries. Of course, there is the emotional impact of that. So many people we know have had breast cancer, and many of them have to go back again. It is such a common problem. From a patient standpoint, I think that is very, very, very, very impactful. Also, from the other two constituents, this hospital system is very strained, as everyone knows right now. It's not like there's an abundance of capacity in the hospital system. Every time you have to bring a patient back, it puts an additional strain not only on your operating room capacity, but also on the surgeons. Surgeons are very overworked, I guess, to some extent. That really impacts them. Huge strain on the system. If you could take out that second and third operation, that helps relieve some of that. The economic cost. Every additional surgery costs a lot more money to bring that patient back in. Being able to be successful on the first shot really, really helps reduce the cost of overall healthcare. This is a big problem that affects across the entire dimension of healthcare. With our tech, we're able to address all of those problems. I'm going to hand it over to Andrew now to go into a little bit more of the technology. Andrew. Thanks, Adrian. Okay, as Adrian outlined, we have a problem with positive margins being left behind. How is Perimeter going about solving that problem? At the core of our technology is optical coherence tomography. It is an imaging technique that is 10 times higher resolution than ultrasound and X-ray and 100 times higher resolution than MRI. What you are seeing here is a cross-sectional image of a human fingernail. The top is OCT, the middle is ultrasound, and the bottom is MRI. You can see the level of detail that OCT is able to provide as opposed to ultrasound and MRI. This then allows us, when you apply it practically in the operating room, to be able to visualize microscopic features that all other imaging technologies cannot see and therefore gives the operator the ability to identify these positive margins. OCT is not a brand new technology. It's been around for about 20, 25 years. It's currently broadly used in ophthalmology with about 30 million eye exams done every year, in cardiology, and also in dermatology. What is novel about Perimeter's OCT, we have developed wide-field OCT. Traditional OCT imaging covers a relatively small area, which is about 1 by 1 centimeter, which in ophthalmology would be imaging the back of the eye. Similarly, in cardiology, Perimeter's wide-field allows the use of OCT over a much larger surface area, up to 10 by 10 centimeters, which is a 100x increase over traditional imaging. This allows us to image complex and large tissue specimens in a really fast amount of time, which makes it relevant in the operating room. What does the output from the device look like? On the bottom left here, we see a histology image, which is some early-stage breast cancer. The pathologist will look at this under the microscope. It usually takes anywhere from one to two weeks for it to get a pathology report. If they discover this early-stage cancer near the surface of the tissue, it would be referred to as a positive margin, and therefore our patient would be called back. When we image the same piece of tissue in the operating room, we can see very similar features, and a trained reader, or in our case as well, algorithms are able to pick out this feature and highlight them to the surgeon in real time. They can decide whether to take action while the patient is on the table or not. Again, having that real-time, high-resolution visualization is allowing the user to visualize the margins in real time. Some of our current users are Dr. Tower here on the bottom, who was our first commercial adopter in Texas. Since then, we've completed thousands of patients with the S-Series. You can read the early benefit and impacts that it's had on their patients. You can see Dr. Anglin, who's a strong user. I think she has a device at two centers that she operates in. The technology can alleviate a surgeon's uncertainty on margins and help address the possibility that they have to call the patient back for a second operation. Where do we fit into the workflow? Let's look at the standard of care first on the bottom. The tissue is resected. It goes off to pathology. We wait a week, maybe even longer. A pathology report is produced. If there is a negative or positive margin, then a re-operation is more than likely scheduled. This impacts cosmesis, delayed treatment, and any complications as well. With Perimeter's S-Series technology, after the resection, the specimen is imaged. It can take approximately 10-15 minutes. If further action is needed at that moment in time, the surgeon will take additional tissue. All of the tissue will continue on the pathology. A report will be produced, hopefully, then with all negative margins. The patient can continue on to the next stage of their treatment and getting back to life. Perimeter has a growing body of evidence, which can be found on our website, on our clinical section. Not only do we have a lot of publications in the breast space, but we also have a publication that came out recently for the use of our technology in head and neck. We're in the writing mode for a second head and neck paper at the moment. In 2025, there should be an increase in publications in the breast space due to the information that will come from our clinical trial. We also have a clinical registry now set up where adopters of our technology can track information from the cases using our device that can streamline the process of producing further publications. If we look at one particular example of using our technology, this is a surgeon from Weatherford, Texas. There was a recent, in 2024, a publication that was completed by MD Anderson where they analyzed the national rate of re-operations in the United States, complications and cost. The re-operation rate came back at 20%, so 19.9%. That was our reference. When we looked at our surgeon's first 72 patients using the technology, that surgeon had a 5.6% re-operation rate. Traumatic impact even on the surgeon's first cohort of patients. If you break that down even further, DCIS is a major problem. It is an earlier stage cancer when it comes to breast cancer, but it is more elusive, and it is much harder for people to see with traditional techniques such as X-ray. Therefore, it actually causes more re-operations. If you look at patients who are under the age of 65 in this column here, there's about a 31% re-operation rate for patients who have DCIS, 24% for patients over the age of 65 in the Medicare group. Using our technology, that dropped down to 13%. When we look at invasive cancer, which is more visible but still can cause problems in the under-65 group, it was 18%, 12.7% in the Medicare group over 65. We actually had zero invasive cancer re-operations using our technology. This section here does account for about 75% of all cancers, quite remarkable results for both DCIS and for IDC for that particular surgeon. Perimeter's OCT technology creates really high-resolution images that need to be reviewed. Surgeons are busy in the operating room, and they are not imaging specialists. From quite a while back, Perimeter realized that we had to engage in using AI to introduce more efficiency and potentially more accuracy to our technology. If you look at the current landscape in medical imaging, there are over 300 FDA- authorized imaging tools. 50% of all healthcare organizations are now using AI in imaging. That is up from 17% in a short period of time from 2018. 75% of these tools are focused in radiology or oncology. There is definitely a significant uptick, as everybody is aware of the use of AI in our industry. Our Perimeter's artificial intelligence tool is called ImageAssist. The model is designed to both assess suspicious and non-suspicious breast tissue through supervised learning. We have about 2 million wide-field OCT images in our repository, all of which have corresponding pathology data which were collected during IRB-approved studies that was led by an initiative with MD Anderson. The data is then kind of parsed into three use cases. We have a training set, we have a validation set, and we have a test set. We run the data through the training and validation set in a loop until we get to a level of confidence, and then we put it through our test set. Once we got it to a specific performance goal, then that allowed us to start our RCT, our clinical trial. If you actually look at the data or the numbers around the amount of data that we used to train the algorithm in the clinical trial, it's very encouraging for the simple reason is we didn't actually have a huge amount of positive or negative labels in the training section, but we were still able to produce the technology that was able to meet its primary endpoint. We're pretty confident on our secondary endpoints as well. If you take this piece of information and then you add zeros to the end of the training model, we have doubled the amount of data that we have since we first developed this technology. We also have an ImageAssist 3.0 version, which will be part of the commercial launch in 2026. The major components that we have of our platform today are the imaging hardware. We have a single-use consumable that is used on every case. We have our proprietary imaging atlas, which is used to train users and also to train our AI algorithms. We actually have the ImageAssist tool itself. This is pending approval post-submission in the next couple of weeks to months. Okay, as I mentioned, our AI and our OCT imaging technology has just completed a large multi-site clinical trial. You can see the caliber of the participants. We have MD Anderson, Baylor, Mayo Clinic, another MD Anderson, Redeemer, Moffitt, some of the largest cancer centers in the country. The design of the trial is quite progressive in comparison to traditional clinical trial designs. If you look at a traditional trial, we have patients who are randomized into either standard of care or with the new technology. There is an analysis done on the performance of the new technology versus standard of care. This generally needs large populations because you are comparing one group against the other. We have used what is called between-subject. We have used a within-subject design where we have our group of patients. They both go into standard of care. After standard of care, they are randomized. Some of the patients go forward to use the device, and the other patients do not. We have a two-to-one ratio, which allows us to put more patients into the device arm, which makes the design more efficient. We use our technology on this group of patients, and we perform the analysis before and after. A good analogy to understand this a little bit more clearly is if you were, let's say, developing a blood pressure drug, it's much more effective to take a measurement of blood pressure on a patient, give them the drug, and then measure their blood pressure afterwards, than to give the drug to a patient and then have another patient and take the analysis that way. It allows you to be much more streamlined, and you get a much better reading when you have a within-subject study design as opposed to the traditional standard of care. We have a control arm here, but the analysis on our study was only done on the device arm of the study. The outcome from the trial, we were very excited to announce recently that we have met our primary endpoint with statistical significance in reducing the number of patients that had residual positive margins during cancer surgery. We were able to achieve this with superiority based on predefined clinical and statistical significant parameters. We expect to submit to the FDA in early 2025, so in the next weeks to short months. With that, I will hand you back over to Adrian. All right, thanks, Andrew. Looking at what this means, what this all means to us from a market size and the business side of things, if you look at this slide here, on the left-hand side, if we look at the economics of having to do re-excisions, a re-excision, you can kind of run the math here on the left-hand side. There's about, if we look at just breast cancer, there's surgeries, there's about 230,000 lumpectomies a year in the United States with about a 20% re-excision rate. Every time one of those re-excisions, one of those patients have to come back for a re-excision, it adds about $17,000 on average to their healthcare treatment. If you kind of extend that out, you're looking at close to a $700 million per year additional cost borne by the healthcare system for just lumpectomy re-excisions. That's at a 20% re-excision rate. If you take that and you run the math on that data that Andrew referred to a little bit earlier, where the surgeon Weatherford was able to get their rate down on the first few patients down to 5.6% across their first 72 patients, that resultant difference between 19.9% and 5.6% ends up being a healthcare system savings cost of almost $500,000,000 per year. Almost 30,000 patients that do not have to go through that second re-excision just by virtue of bringing this 20% down to 5.6%. The summary, the reason we did this analysis was really just to drive home the point that there is, even where the technology is today, and this 5.6% analysis was done before the AI and with the product that is in market right now. Even with this, we're able to have significant healthcare savings and actually a significant number of patients that we're able to successfully keep from a second re-excision. Even beyond surgery, if you think about the tech we've got, we have the wide-field OCT imaging technology, and then soon to have the AI image recognition technology in market. Those two pieces of tech, call them the two IP pools within the company. Right now, we're applying in the surgical suites in what you've seen to date in this presentation. Those same two technologies can actually also be applied in two other main areas of the treatment path. Biopsy is one situation. There's about 2 million breast biopsies a year in the United States. Taking that same imaging technology that we've created and taking the AI library that we have that's continuing to grow and applying it at that stage of the process allows the radiologist to provide some information, again, in real time to the patients versus waiting the 7-10 days that biopsy results normally take to come back. This is very impactful because of all the biopsies that are taken out of the 2 million, 80% of them are nothing to worry about. 20% of them have something that needs to go. The patient needs to move further. For those 80% of patients, you can give them some indication potentially at the time that things don't look okay. For the other 20%, those samples can be popped to the top of the queue. There is significant value there, we think, in terms of taking this technology and applying it in the biopsy space. Similarly, for pathology, taking the same technology and moving it to the pathology labs is valuable. Right now, when the excised tissue comes down to pathology from post-surgery, the pathologist looks at about 2% of the entire volume to look for those margins. That also means that about 98% is not looked at. The surgeon and the pathologist are doing their best to try to look at the places they believe to be the most likely to have not enough margin. With the images that we create up in the surgical suites, the ability for the pathologist to look at that while they're deciding what part of that tissue to put under the microscope also helps them to get much better at inspecting for negative margins. There is a market expansion opportunity there. The nice thing about the way this company has developed over time is these two IP pools, although we're beachheading inside the surgical suite, with the same basic IP, we can also expand the market far beyond those 230,000 breast cancer surgeries per year. Even beyond breast cancer, I alluded to this a little bit earlier, that the same basic imaging technology can also be used across really any sort of solid-state cancer. Here you see a bit of data on the top six or seven different types of cancers. The graph on the left-hand side shows how much money is, I'll call it wasted, every year because of re-excisions. Breast, of course, has the largest occurrence, which makes the dollar amount a lot. You can see it's not insignificant as you continue down through head and neck and prostate, colorectal, etc. What's even more impactful as you move to those other tissue types is if you look then over to the right-hand graph, the five-year survival rates of a patient who has to come back for a second surgery or a patient that did not have positive clean margins on that first surgery, it can actually be quite impactful to their five-year survival rate. Whereas breast cancer drops by 10% from a 90% to 80% five-year survival rate if the surgeon's unable to get all the cancer out in the first surgery, other cancer types like head and neck, for instance, drops from a 55% all the way down to a 10% five-year survival rate if that first surgery is unsuccessful. High impact across the board here, both economically as well as from a survival rate standpoint. A lot of room to move for us. This is kind of where you see what we're doing with the company. If we track our progress through to the end of last year, really that was about getting our first product on market, developing the tech, getting first product on market, starting to get some of the data that you've seen presented today, getting word out into the surgical community that the product is usable and does help with patient outcomes, completing the trial for the AI, which really enables a broad expansion of the product into the broader surgical user space. In 2025, while that study is going through review with the FDA for approval in early 2026, 2025 is really about expanding the current product out, increasing the footprint that we've got our product in market, increasing awareness, geographically expanding beyond the few geographies we are in the United States, and then engaging with strategic partnerships, both with hospital groups as well as with strategic companies that play in this space. That is our mission for this year. As we look into 2026, we will hopefully have FDA approval early in that year, which then allows us to really scale out that AI to a much, much broader and a much, much more accelerated pace than we are able to with the S-Series. This is our journey. Also in 2026, we get to expand now into different tissue types and to further deepen our strategic partnerships. With respect to the IP pools, so to speak, what I talk about, we're fairly well protected. We've got a number of patents around a lot of the imaging and the hardware. Software, as most of you know, is not really patentable. What you do with software is you trade secret it. We've got a lot of our AI, it's not patented, but it's under trade secret. We've got a fairly large image library. Those of you who follow AI have some understanding that the algorithms are open source. There's not a ton of value in the algorithms, but really the data sets is where the real value is. The nice thing about this business is that we've got a large library, and it's very difficult to replicate. To replicate the library, you have to replicate the hardware. We're the only ones who have the hardware, and the hardware is very well patent protected. That library is fairly well moated, so to speak, against other competitors replicating it. This is our leadership team, a ton of experience across both AI as well as medical devices. You can see some of the logos of the companies we've worked at. A ton of experience with this team. Three of us are here on this call right now. Here's a summary of our capital structure. You can see we're listed on the TSX Venture Exchange. We have an OTC symbol also here in the U.S. We've got coverage from Leede Jones Gable, Paradigm, and Raymond James. We've got some analyst coverage. We do have a couple of major shareholders, Social Capital Master Holdings. That's Social Capital, Chamath Palihapitiya's firm. He's about a third of the company, 31% ownership. And then Rocco Fiorentino, who's been with us since the very early days of the company and has recently been increasing his stake through successive fundraisers. One thing I do want to note here, although it's not really like Chamath's name's not on here explicitly, he is Social Capital is his firm. He's very supportive, actually. He invested significantly in the last round that we just did two or three months ago. He is very active on social media. He has started to increase the frequency at which he's speaking about us. He's been tweeting about us recently by name. He's mentioned our technology a few times in a few podcasts he's done with Joe Rogan and with Tucker Carlson over the past few months. The word is getting out there. Having him as a major shareholder is very useful for just building awareness with a general base. He's a very supportive investor. In summary, very large markets, a product, a solution, a technology that's quite protected, that has a very, very high impact on a big problem in a large market. Our revenue model, our business model actually is a very high gross margin model that we're able to, that 75% is actually very attainable. We've got clear line of sight to that right now from a gross margin standpoint. We've got our current product in market already seeding it, seeding the market, helping us with workflow improvements, helping us with developing KOLs, key opinion leader surgeons. Our clinical trial for the AI overlay, software overlay that goes on top of that current product was successfully completed last year and is going to be running through the FDA approval process this year. We had a superiority result of that on the primary endpoint, which is very, very positive for us in terms of our ability to be successful through the approval process. That should hopefully allow us to start marketing that in 2026. What that really does is it greatly reduces the barrier to adoption. What our expectation is, is as that gets approved based off of the foundation we're building with the current S-Series, we're able to accelerate the adoption curve in the marketplace with many, many more surgeons. That is the summary. I think with that, I can hand it over to Glen and open it up for questions. Super. Thank you, Adrian. We do have quite a few questions in the queue already. To our audience, if you have a question, please use the text box within the portal to ask. I think some of your new slides in here, Adrian, actually anticipated a lot of these questions. Maybe we will just read some of them for sort of a little bit more depth. I guess you have a new slide that talks about the milestones in the FDA process, but maybe you could talk about any potential challenges that you could see or potentially see through that process. Yeah, sure. I'll open it up, and then I'll hand it maybe to Andrew, who's got a lot of experience in this area. We are in the process right now of submitting, of putting the paperwork together, and we'll submit that in the next few months to the FDA. Probably the biggest challenge we have from a business standpoint is just the length of time it takes to get through a PMA process, which is what we're moving through. From a risk of acceptance or rejection, we see that as being very low, especially when we compare the results of our trial and our technology to others that are similar, addressed in the same space with different technology approaches and the approval path that they had been on with the FDA where they had got approved with much worse data and even some safety issues. I think from our standpoint, we see it as relatively low risk. We have what's called breakthrough device designation on the product. What that allows us to do is to have continuous, what they call sprint conversations with the review team. We've taken advantage of that over the past year. What that means is that the review team is very familiar with their technology, is familiar with what we're doing with it, what our goals are. They were very involved with the trial design at the onset of the trial. That greatly reduces the risk. I don't see any major risk items here. I don't know if Andrew, if you want to chime in with anything additional to that. Andrew, you're on mute. I wouldn't categorize it as risk, but when you're developing something like AI and you're putting it through a trial, and then you have to go through the formal process of submission and getting it approved. By the time you get to the end of all that, it's logical to think that your AI has evolved and that you've generated new data and you've made new models. So your technology is generally going to perform at a higher level. The FDA does have a what's called PCCP, a predetermined change control plan, where it allows you preemptively to try to state what you think the changes that you're going to make in a more forward-looking manner. The challenge is to make sure that we can include all of these advancements that we've made into our PCC plan so that we get the best version of the device out there and that it also allows us to iterate going into the future. It's a relatively new program. Just navigating our way through that. We've brought in a lot of external help to help us navigate it. It's a challenge more than a risk, but it's just something that we're working through. Super. Thank you. Excuse me. What role does the S-Series play once the B-Series is commercial? How do you aim to drive adoption of the B-Series OCT system along surgeons and hospitals? Is the S-Series essentially paving the road for the B-Series in terms of growth and adoption? Does the S-Series continue to have an impact in the business, i.e., driving any meaningful sales once the B-Series is out in the market? Yeah, great question. The answer is yes. S-Series does have a place in our portfolio after the B-Series is in market. It comes down to label claims. What the S-Series is authorized for, what the FDA cleared for, is what they call a general tissue indication, which means that it can be used for any tissue type, not just breast. What the B-Series will be cleared for is for specific to breast, especially with those AI algorithms that are trained on breast tissue. That being said, the hardware is basically the same. There are some upgrades to it, but it is essentially the same hardware. That provides a few different advantages to us. One, it allows us to reuse a lot of the capital that we've already built, a lot of the equipment we've already built without having to restock brand new inventory where we can refurbish existing devices. That has a positive effect on capital outlay, number one. Number two, it allows from a workflow usability, what does it mean to customers who are currently using S-Series, has it in their operating room? It looks very similar, takes the same floor space. The UI is quite similar to the current one. The upgrade path for a surgeon using the S-Series, the B-Series is relatively easy. The B-Series will get all our current surgeons, our customers, our breast surgeons, not all of them, most of them are that. They will upgrade to the B-Series because it will make their workflow easier. The S-Series we will continue to have in market to expand those other tissue types, those other markets we're talking about. Head and neck cancer, lung cancer, liver cancer, et cetera. We will continue to market that for the purpose of expanding the portfolio into those other areas. These will live beside each other. They'll target different types of surgeons, and there'll be a preference for one or the other depending on what that surgeon's doing. Super. Thank you. Are there other OCT imaging technologies in the market or in development that can pose a competitive challenge? What advantages does your technology have over those OCTs if yes? Is anyone else exploring an AI-powered device? Ours is the only OCT device in the market, or as far as we're aware of it being developed for cancer. There's nothing else there that we're aware of in that space. Andrew, maybe you can answer at the next level in terms of competitive threats and then AI. For OCT, we don't have any visibility to anybody who's developing a device that will then leverage AI on top of it. There are competitors in our space. There are two approved technologies available on the market right now. One has been available for quite some time, and one is relatively recent. After that, I think we will be the first to market with an AI solution. In fact, I think we're going to be one of the first companies to bring an AI solution to surgical oncology if you look just broadly across that market. Okay. Thank you. Sticking on this theme, are there any challenges associated with faults in imaging captured by AI? I think you already answered this, but I'll read the question. Has this happened with other companies advancing AI medical devices, and what have you done to address such issues? Sorry, what was the fourth part of the question, Glen? Sorry, I didn't hear. Are there challenges associated with faults in imaging captured by AI? I think maybe I'm trying to interpret that question a little bit. Maybe it's about liability, like what if the AI is wrong? I think an important thing to note about the way our product works is it's not a diagnostic tool. At the end of the day, the surgeon is the one who makes the diagnostic decision. There are other devices in the market where it moves more to a red-light green-light sort of approach. This is cancer. This is not cancer. That's really not what our product is designed to do. It's not cleared for it. It's not how we necessarily want our surgeons to be using the product. The surgeons are able with the OCT to visualize, to see things that they're unable to with their naked eye, and then with that, make the final decision. What that means then is that is our device, is the OCT actually driving a clinical decision? No. It's helping the surgeon inform them to make the clinical decision, which then has the obvious sort of liability implications based off of that. Okay. Super. Can you please explain the economic model of your system that is going into the operating room with breast cancer surgeons? And then also, as somebody else's question, if you could touch on costs, what hospitals pay, reimbursement, your pricing strategy? Yeah. Today, what we've been doing, the way we've been sort of introducing this to market, is we've been placing the device in the operating room at no charge and then selling the consumable container, which is used one per patient, and monetizing that. It is very high margin. That container is at a 90%+ gross margin. The surgeons we target, or the hospitals, I guess we target, are those where we can get over 100, about 120 procedures per year on the device, and then monetize that through the $1,000 consumable container that we ship at those gross margins. What that results in, the model we're going for is to break even on the devices that we place for free within the first year. That is how we've gone to market up until last year. Last year, we added to that a service contract, which added an additional revenue stream to the business, which then allows us to move a little bit more into the black, even with that model, or be able to attract surgeons whose volumes are a little bit lesser. Okay. As we move forward, what we are now starting to explore as we get ready for the B-Series, what we've seen is that the market, it's about market power, right? The market power is starting to shift ever so slightly towards us as we're getting the data out there and the word is getting out among the surgeon community about the performance of the product. This year will be about us trying to see, one, our ability to sell the device, the capital at some amount of money to get a little bit of cash upfront for that placement, number one. Number two, as we bring the AI onto market, what we want to offer our customers is the ability to always have the latest trained AI available for themselves as we get more data, as our algorithms get better, implementing a type of subscription agreement on the algorithms as we get the B-Series in market. This is how, from a revenue standpoint, the model is today and how it will evolve in the future. With respect to the reimbursements question, right now, we do have tracking codes for our surgeons, and they're able to track for the purpose of getting to reimbursements in time usage. That is ongoing right now. There are pathways that we can follow that will allow us, once we get approval on the B-Series, to quite quickly get to a temporary reimbursement code where our hospitals can start to get reimbursements right away while we continue to go down the traditional path of reimbursement, which is to get widespread adoption, to get societal support, and to further strengthen up the clinical evidence. We have a pretty clear path that we know we have to walk. We have other companies, I guess, that have followed that same path successfully that we are going to march ourselves down as we get the B-Series in the market. Super. Thank you. I think you addressed a large chunk of this next question in your just current answer or last answer, but I'm going to ask it in case you want to add on to it. I understand the performance and time benefits for the patient, surgeon, and pathologist, but help me understand any sticking points that may prohibit hospitals from widely adopting the expenditure for the machine. Yeah. I think maybe the biggest sticking point from just a purely cost perspective, and I'll address that in a moment, the biggest sticking point right now is a little bit with the way the U.S. healthcare system works. Every procedure that a hospital performs, they get paid for, right? Even if it's a second surgery. Until the system moves more strongly towards a value-based care model for breast cancer, that will be the case. It is moving that way, and perhaps now it's going to move faster with some of the political leanings of the way things are going. Today, that is one of the, I wouldn't say it's a challenge for us yet, but as we continue to saturate the market, I think it will end up being a little bit of a backwards pressure on us. The reimbursements will help that in time. The way we're able to be successful right now with the hospitals paying for the product, even though they're not getting reimbursed, is by working very closely with surgeons, right? The surgeons don't want to go through this. The hospital groups do want to keep their top surgeons. As the surgeons start to push for this, we're able to get it into the hospital through that sort of door. Additionally, there is value to a hospital to be able to attract more patients to their group. One of the ways to attract patients is to talk about re-excision rates as compared to what their competitors across the street or the national average is. The full treatment, cost of treatment for breast cancer is over $100,000. A hospital is very well incentivized to bring more patients to their group versus losing them. This is a way for them to attract that. In addition to that, typically, the healthcare decisions in a family are made by the mother, the female of the family. Being able to attract those patients in is actually a very high ROI because they'll tend to bring the rest of the family along with them. We are starting to see this becoming a bigger part of our discussions with different hospital groups as they are seeing the performance with their surgeons about how now to leverage that within their communities to drive more patients to their facilities and therefore drive that top-line revenue, even though the reimbursements are not in place yet. They are starting to see the economic value of that. Okay. Super. Can you talk about your A, a manufacturing strategy, and then B, your partner strategy for distribution? Yeah. Right now, we manufacture—our manufacturing is third-party, US-based. We have one manufacturer in Minnesota that we use today for the S-Series. We're spinning up a second manufacturer in the Midwest so that by the time we come to market with the B-Series, we will be dual-sourced from a manufacturing standpoint. Our supply chain is fairly secure. We have dual-sourced on almost everything. Our OCT engine is the light source of the sensors. That's probably the biggest chunk of our—it's not probably; it is the biggest chunk of our device. That is single-sourced. However, at their level, they're dual-sourced on components, and we're a relatively small portion of their supply. We don't see much risk in the supply chain. We will continue to—the nice thing about being able to reuse the S-Series in B-Series was from refurbishment. It means it will take capital to grow the business, but it's not like all the product we've built, we now have to table and shelf and re-spend new capital. We are able to reuse all of it, essentially, for the B-Series launch. I think that's an advantage we have with this model. Super. On the same, I guess, path, what is your current unit-per-month delivery capacity? Where do you expect that to go? You mentioned the supply chain, I guess, topic, but then perhaps a comment on the recent tariff discussions. Yeah. I'll start with the easy one first. The tariffs is a non-issue for us since we're manufacturing in the US, our customers in the US. There's no cross-border shipping of our product, so we don't have to worry about tariffs. In terms of capacity, it's a pretty easy answer. We've got capacity of dozens per month on the device side without much of a stretch as we get our second source up and running. On the procedures or the consumables side, that's also a very simple product for us to manufacture, although there's a lot of IP around it. We've really got no practical limitations on that. It's all very, very simple material source and very simple to assemble. I would say hundreds up to thousands per month we can get to over time if we need it. No real capacity constraints that we can see right now. Okay. Thank you. Can you—sorry, how many different types of cancers do you see this technology being applied to? In other words, what is your total potential addressable market? Yeah. Andrew, do you want to talk about it from a tissue type standpoint? Sure. I guess the one that we're focused on primarily next is head and neck. We have done some feasibility testing. Like I said earlier, we're in the process of publishing a second publication in that space. We have interest from thoracic surgeons about looking at lung and specifically lobectomies. Those are the two that really stand out at the moment. Beyond that, I don't think there is a limitation. Once there's a solid tumor removal procedure that needs an immediate validation of the margins, I think Perimeter has a role that we can play in that procedure. A lot of those procedures leverage frozen sectioning where they actually freeze the tissue in real time and try to slice it very thinly and look at it under the microscope. Even in those cases, the surgeons are telling us that OCT could help provide guidance as to where exactly the frozen sectioning sample is taken from. I think the reach can be pretty far. We're not tying ourselves down to one particular tissue type at the moment, but I think the next one in line is head and neck. As the graph that Adrian showed earlier on, the impact on reducing positive margins from a mortality perspective with head and neck patients is tremendously high. The head and neck surgeons really see the value in that. From a market size standpoint, if you look at sort of all the cancer diagnoses, breast cancer is about 10-15% of those. There's a vast number of other—so the market's quite large beyond breast from a count standpoint. From a pricing standpoint, the impact of the tech on the other cancer types is potentially much higher than it is in breast cancer. There will be more pricing power as we enter into those markets. Okay. Super. Thank you. I think you've already discussed your business model in terms of the razor-razor blade approach. Can you put any dollar figures on both hardware, what it looks like potentially to either sell or lease, and the cost of the reusables? Yeah. We can. The device itself right now, the cart, the cart non-consumable device costs us about $130,000 right now for us to manufacture, kind of a fully loaded cost. The consumables are around $100. We sell the consumables for about $1,000, a little bit less than $1,000 is our ASP at the moment, pushing up at this point. In those costs that I quoted you are right now, we have not done any cost reduction or anything yet. We are at very low volume, and we have not done any. There is no volume sort of cost reduction built into that, nor is there any engineering work yet that has gone into substituting components. There is a lot of room for cost reduction on those prices I quoted to you at this point. Okay. Thank you. Can software be added to your recurring, I guess, revenue model with your product? Yes. Yes. Yeah. That's the idea. As we get the B-Series onto market to have a subscription model on that software. Thank you. I'm going to read this, and I'm not sure I understand the question entirely, but I think you will. Is SOC real-time path confirmation of margin-free resection in the operating room? If so, doesn't imaging create a redundancy, and does this preclude lymph node sampling? Okay. SOC standard of care, the standard of care imaging is really not in the operating room. It's after the fact in the pathology lab, which is that 7-10 day latency for the patient and surgeon to get the answer back. The exception to that, which I would not call standard of care, but the exception to that for breast cancer is frozen sectioning, which Andrew alluded to when he was talking about the different tissue types. That's very rarely done with breast cancer and is expensive, and it's just not standard of care. That would be the closest thing to that, I think. The other imaging that's done inside breast cancer procedures is X-ray, which is used for a different purpose. That is the morning of the surgery, a little metal clip is inserted into the middle of the tumor. It's called a localization device. That helps a surgeon find where exactly the tumor is. After the tumor is removed, it's put on an X-ray to ensure that the metal clip is removed from the patients for safety reasons. The challenge that they have with the X-ray image with respect to margin assessment goes back to one of the first points that we have made, which is the resolution of X-ray is very, very poor when looking at sort of features at that submicroscopic level. That's where OCT comes in. There is some imaging done inside in the middle of the procedure, but it's not fit for purpose both from a cost standpoint as well as from a margin assessment standpoint. Thank you. What technology is currently being used for pathology, and can your technology eventually replace pathology? Andrew, do you want to take that? The process for pathology is to take the tissue, it gets dehydrated, it gets put into a paraffin block, and then it's sliced into a very, very thin slice up between two pieces of glass. It is looked under a microscope, like a manual microscope. Some advancements are being made where these slides are now being imaged, and you can have a digital view, and then they can apply some AI to it. It still is a very, very manual process. I don't think OCT is going to replace pathology because they're looking at a lot more than just margins. They're looking to grade the tumor. They're looking at all sorts of different stains that give them information that we would not get with OCT. Where we provide the advantages is in the operating room is to be able to highlight areas that are suspicious to be positive margins to reduce the amount of second surgeries. Where we can help in the pathology lab is these specimens that come into the pathology labs are large, complex. To sample 100% of the specimen, you would need tens of thousands of slides. They literally only make 30-50 samples and look at that under a microscope. If we have a comprehensive full specimen view of the exterior using OCT, then the pathologist can use that to guide where they're going to take their samples and potentially make that process more efficient and also more accurate. There are lots of times we find stuff in OCT, and we go check the pathology afterwards, and it says that there was nothing in the margin. We can clearly see it in OCT. We even have a study that we did with Mount Sinai in New York where we found a number of cases where we asked them to go and take additional tissue, and they were subsequently able to find that there was cancer near the margin based on the presence of it in OCT. There is something there that we can work on where we can potentially guide some of the actions of pathology. Our intention right now is never to replace pathology. Okay. Super. I'm just doing a time check. I noticed we're at 3:00 P.M., and I've got a few questions in that I think we could still address. I'll just ask you one or two more questions, and then we'll ask for some closing remarks. Maybe some balance sheet type questions, Sarah, because we've got a few people asking similar type questions. Maybe you could just talk about your current burn rate, your current, I guess, balance sheet, and how you see the need for cash as you grow the business. I'll just mute myself. Yeah. Thanks, Glen. I'll talk in relative terms to our Q3 reporting as we're in mid-audit right now of our year-end financial results. At the end of Q3, we reported $9.5 million US dollars cash on hand, plus an additional $1.9 million in outstanding CPRIT grant receivables. It gives you an idea of where our cash position was at the end of Q3. We burn about $1 million US dollars a month. It can give you an idea of what that runway looks like. In terms of capital raising strategy, as a company, we're getting much more forward-looking in our planning of where we're going to forecast the business to be. We're looking at a time point where we will still continue to have capital needs and fundraising for the next probably two years. From there, we should be in a position where we're cash positive and growing the business on our own. Super. Thank you. One more capital markets-related question is you're currently listed on the TSX Venture. What is your sort of long-term strategy to get off the OTC and move into a Nasdaq or New York Stock Exchange listing? Yeah. Yeah. We have plans to move both off of the TSX Venture to graduate to the TSX as well as moving off of the OTC Pink Sheets with an interim step to the OTC QX with an eventual Nasdaq uplisting when the timing's right with the business. Okay. Super. There are some additional questions in the queue. If you feel your question has not been answered, simply email me, glen@bristol.ir.com, and I'll make sure that I get the answer to you from Adrian, Sarah, or Andrew. Guys, closing remarks, and then we'll end the call. Yeah. No, I think this is a, some of you have heard the story before. Some of you, the story will be new. I think we're a little bit at an inflection point at this point. The most recent very big de-risking of the business was hitting the primary endpoint a few months ago, back in December, November. Getting the information was a great de-risking of this business in terms of our ability to bring the AI onto the marketplace. That was big news for the team, very energizing for us. The line of sight from where we are now to getting widespread adoption of this technology became much, much clearer. I guess, even more importantly, from a customer base, both hospitals as well as surgeons, the fact that we did hit that primary endpoint so they can see this AI coming to market very, very soon really energized them also. We are seeing a lot of that engagement now in our commercial side of the house. Over the course of this year, what we are seeing inside of the company is a lot of energy both from the surgeon community into us, which will manifest itself in some of the conferences we are going through, which manifests itself through our sales engagements with them, which manifests itself with some of those hospital groups coming in and asking us to expand within their hospital network in advance of the B-Series. 2025 is going to be a really exciting year for that. The other piece is that as we get to the point where we get the label claims approved for the B-Series, it allows us to be much more forward-leaning in how we're marketing the company in terms of being able to talk more clearly about breast cancer, talk more clearly about re-excision reduction. Getting this successful trial at the end of last year was very exciting and really unlocks a lot of what we can do going forward and just takes a huge risk off the table for us. The team's super excited, super excited where we're going now. Thanks for the time. Let us know if you've got more questions. We're always happy to tell our story. Super. Thank you, Adrian, Sarah, Andrew, and thank you to our audience. This concludes this presentation. Thank you.