Hello and welcome to Virtual Investor Conferences. On behalf of OTC Markets, we are very pleased you have joined us for our quarterly small-cap growth conference. Our next presentation of the day is from Perimeter Medical Imaging. Please note you may submit questions for the presenter in the box to the left of the slides. You can also view a company's availability for a one-on-one meeting by clicking "Book a Meeting" in the top toolbar. At this point, I'm very pleased to welcome Adrian Mendes, Chief Executive Officer of Perimeter Medical Imaging, which trades on the OTCQX Best Market under the symbol PYNKF and on the TSXV under the symbol PINK. Welcome back, Adrian. Thank you, Greg, and thanks for having me. This is great. I appreciate it, so welcome, everyone. Perimeter Medical Imaging. We'll jump right into it, so I'll, of course, be making forward-looking statements, so treat them as such. We do have a product that is regulated by the FDA, so I'll be talking about a current product and future products, and these are indications for use. You can find these slides on our website as well, so no need to read it, so this is it. This is why we're here. We didn't get it all. That phrase, I don't think, needs any explanation. Anyone who hears it understands exactly, precisely what that means, and unfortunately, so many of us have either heard it ourselves or have had loved ones who have heard that. It basically is telling people that, "Hey, look, you have cancer. We did a surgery, and unfortunately, we didn't get it all, and now we have to do something more." That's the problem we're here to solve with Perimeter. So let me just jump into a little bit to explain the problem in a bit more detail and what's going on here. So if you've been involved around cancer, you might have heard this thing called margins. What the surgeon's trying to do during an operation is get clean margins. And what a clean margin means is basically that when they remove the cancer, it's fully surrounded by a margin of healthy tissues. And if it is surrounded by healthy tissue, that means a successful surgery. On the other hand, if not, if some of those cancer cells are right up against the edge of the tissue that was removed, it means it's more than likely that there are cancer cells left back in the patient, and that's an unsuccessful surgery, which then leads to the phrase I was at before, "You know, unfortunately, we didn't get it all." And so this is really the goal of the surgeon, right, to make sure that they've got a clean margin. Unfortunately, that's very hard to do. In breast cancer, which I'll talk a bit about because that's our first sort of indication we're spending a lot of our time in, a 23% rate of re-operations where the surgery on the first shot was not successful. But it's not just breast cancer. Really, any type of solid tumor cancer has these challenges. A high percentage rate, thyroid 12%, prostate 21%, and it doesn't stop there. The challenge that surgeons have is that it's very hard to detect any cancer cells that might be remaining in the body. You can't really see it. It's very hard to feel it. And so you don't really know. You're flying a little bit blind as a surgeon. And the imaging technologies that the surgeons have in the operating room, say X-ray, ultrasound, MRI, doesn't have the resolution to see down at that very microscopic level to be able to see cancerous cells. So the surgeon does as best they can with the tools they have. But unfortunately, with all of that, even in this day and age, there still is a double-digit re-operation rate for cancer surgeries. With a high impact, of course. I mean, this is perhaps common sense, but at the risk of repeating what people probably already know. Think of this from the patient's standpoint. The patient has to go back in for a re-operation, a re-excision, let's say breast cancer. The risk of pre-operative complications increases drastically after repeated breast surgery. So up to 66% more risk, higher risk of a complication. Any sort of therapies that have to happen afterwards, be it chemo or radiation, is now delayed, giving more time for that cancer to potentially continue to propagate. For lumpectomies, especially, there's a cosmesis element to it for the patient. So that's compromised if there has to be multiple surgeries. And then, of course, the emotional trauma of having woken up after the surgery, believing, hoping that everything's okay, and having the doctor come back and say a few weeks later, "I'm sorry, you've got to go back under the knife again." So that's from the patient's perspective. But it goes beyond that. The hospital system gets strained. Hospital ratings get dinged when they have to bring patients back. Their satisfaction scores go down. Their operating rooms are constrained resources. Nurses are constrained resources. Those have to get reused for a repeat surgery where they could possibly be used more profitably and better in different surgeries. So that's a problem from the hospital's perspective. And then from the insurer, the payer's perspective, of course, that's expensive. You're paying for another surgery. If it had been successful on the first shot, there would be no need for a second surgery. So it really is problematic across the board. All right, so that's the problem. What is our approach for solving it? We use a type of imaging technology called optical coherence tomography, or OCT. The best way to think about this is it's like ultrasound, except it uses infrared light waves instead of sound waves. And what that provides is a very high-resolution image. If you want to compare to other things, it's 10x higher resolution than X-ray. It's 100x more than MRI. And what that allows a doctor to do or a user to do is be able to see at that microscopic level of detail in real time in an operating room while the patient's still laying on the table. So you can see cellular level detail. You can see within the tissue. You remember the margin is you have to be able to see in some sort of depth. So you can see a few millimeters deep with this technology, and it's non-invasive. There's no injectables. So it's a very, very nice technology for solving this very, very big problem. Okay. Okay, so we take that basic imaging technology, and we put it into this form. So this cart that you see on the right-hand side, the camera is inside the body of the cart. It points upwards. The surgeon places the tissue on the top of the cart, and then images are taken and displayed on the screen. So what the surgeon sees is the black-and-white image you see on the left-hand side there. They can see very clearly from the top to the bottom is about two millimeters. So if they see anything suspicious anywhere in that picture, they know they've got a problem where they don't have clean margins. Remember, the margin needs to be clean to the depth of 2 millimeters. Okay. So what this provides the surgeon is the ability to see in real time what's happening in that margin. And with a high degree of correlation, confidence that what they're seeing in real time is what the pathologist is going to see a few weeks later, which is really the final say. Okay. So in this picture, what you see in the black and white is what the surgeon sees. And that little white dot, sort of white circles that are up sort of towards the top right-hand side are cancerous cells. And that correlates with the purple image down below, which was what the pathologist sees a few weeks later. So with that information, the surgeon can be confident that what they're seeing in the operating room is actually what the pathologist will see later. Okay, great. So now they have that information. They can make decisions and then move on with the surgery. What are they interacting with? This is what they're interacting with. They're interacting with that cart, which you see in the far left, number one there. What we call the S-Series is what's in the market right now, FDA cleared. What we call a Specimen Immobilizer, which is a single-use container into which they place a tumor because it's tissue that they remove out of the patient, walk a few steps over, and put it onto the machine. That container that holds the tissue cannot be reused, right? So that has to be thrown away, which is good from a business perspective because it gives us a recurring revenue stream and gives us very good insight into exactly what usage the surgeon has on the machine. So we can be certain that machines aren't sitting idling in an operating room. And it's a very high-margin piece of product for us. Attached to that, we have a very large library of images. Right now, our largest library is breast images, breast tissue, both cancer and non-cancerous cells. So we use that image library to train human readers right now. And then what I'll talk about in a moment is where we're going in the future, which is, it's obvious, right? An AI algorithm that looks at all those images as they're being created, has been trained, and then provides real-time information to the surgeon while in the operating room. That product is going through FDA approval as we speak. So I'll get to that in a moment. So what does this mean in the real operating room? So the top horizontal pathway here is a current standard of care. So the patient's on the table, unconscious. The surgeon takes tissue out and sends it down to the pathology lab. They then close the patient up and send them home. The pathology lab takes a week, maybe up to two weeks, to review all of the tissue that has been sent to them. They put that under a microscope. They only look at about 1% of that tissue. And based off of that, they feed back to the surgeon whether that surgery was successful or not. One in every five times, the answer is it wasn't successful for breast cancer surgery. The surgeon then makes that call and says those words that no one wants to hear. Okay? That's standard of care. So what's the problem with this? Part of the problem with this is there's a long waiting period. Part of the problem with this is there's a high defect rate 20% of the time. But this is the best that we have right now as an industry. Move to what we're able to provide to our patients is that machine you saw on the previous slide. In the operating room, a couple of feet away from the bed, the surgeon, again, takes the tissue out. They then place it on the machine. They scan all sides of that tumor to get a 360 view of it. And within about 10, 15 minutes, they have their answer of what they see on the screen. Then, based off of that information, since the patient's still there, the surgeon can take a little bit extra tissue if they see something suspicious. Or if not, no problem. Then, just like in standard of care, all that tissue goes down to pathology. And then pathology, a few weeks later, comes back with the answer. But the surgeon's walking out of the operating room with a high degree of confidence that they've got cleared as much as they have been able to see. And they know that what they've been able to see correlates with what the pathologist sees. So they have that confidence walking in the operating room, and they can communicate that to the patient. And then the patient, when they finally get the answer back from the surgeon a few weeks later, we've seen improvements, pretty big improvements in the re-operation rate at that point in time. Okay? So that's what we have right now in markets. And what this slide is showing is sort of the generation-after-generation product that we've got coming through the pipe. The first far left column is what we have today, which is the S-series, which provides real-time visualization, provides that image on the screen, 10-15 minutes to scan, and then a manual review of those images, maybe 1,000 images, up to 1,000 images per margin. The surgeon reviews them all. They can do it fairly quickly. It kind of is like a movie. They can scan through everything. But it's still some work that they have to do looking for that needle in the haystack. Takes a bit of concentration. What we have running through an FDA approval right now, we're just a few months away from getting the review to be completed, is that same basic technology with an AI algorithm attached to it. So what that does is, while the images are being created from the scanned tissue, in real time, in the body of that machine, there's a GPU in there, and it is applying AI to all the images being generated. Then up on the screen, projecting not just the black and white image itself, but what you see, those two little red bars, which in this case, the AI is saying, "I found something. Take a look at these images, and then make your clinical decision based off of that." So what does that do for the surgeon? It does two things. It allows them, instead of having to search through hundreds and hundreds or thousands of images, look at maybe half a dozen or a dozen images that the AI has chosen as being suspicious and really focus on those to make their clinical decision. So it reduces the workload in the operating room and also reduces the time in the operating room. That's the number one thing it does. The number two thing it does, which is the most exciting for me, is it reduces the barrier to entry for new surgeons that want to use our device. They don't have to worry about having to look for that, what I call the needle in the haystack, without any sort of assistance. They can look through that entire volume along with the AI assistant helping them, which makes it a lot more approachable for them and a lot easier for them to adopt it. So that's a huge motivator for us to get that into market quickly, such that we can expand our market adoption much more quickly. And then in development, of course, we have further generations, which improve the accuracy of the data, of the AI. Makes it a bit easier to use from a UI standpoint. We can increase the number of images that we have in our library over time. It's always increased since we can feed that in. Right now, we are at 2 million images, over 2 million images. But that grows every single day. So this AI just keeps getting stronger and stronger. I touched upon the fact that we do have our FDA approval review happening as we speak. So that's running through the gamut. It's a PMA application, which provides actually a lot of protections. It's a little bit harder to get through, but we're over halfway, about three quarters of the way through the process right now. And at the other end, it'll give us a lot of protections in terms of who else can come up and copy us. A lot of regulatory protections. It's a much higher bar, which then adds, again, confidence to surgeons, to hospitals, to patients that it's a very good technology. We ran a very broad clinical trial. You can see some of the logos of the top cancer institutions in the country at the bottom. And we were successful. We hit our endpoints. Basically, the endpoints at a very high level were to be able to improve standard of care by catching patients that would have had cancer if they had just done standard of care and gone home, but catching them and notifying the surgeon and allowing the surgeon to remove that, such that those patients, after they went through the trial and went through our device, ended up not having cancer left behind, as per the pathologist's reports. So that's a very big deal because that means we're able to get through the system without having cancer left behind. Okay. So now that we've got the problem established, re-operations, and all the impacts of that, and the solution of how we're attacking it, let's talk about the market and how things are looking for us. So it's a very large market, over $1 billion annually every year in healthcare costs due to re-excisions. And that's just the hard costs. That has nothing to do with the emotional costs. That has nothing to do with survival rates. That's just over $1 billion of actual cost savings in the healthcare system. That's the left-hand graph. And you can see from this graph that it really covers a lot of different breast - sorry, a lot of different cancers, breast being the largest, but it's proliferative across the board. On the right-hand graph is perhaps an even more important piece of information, which is the five-year survival rates for different types of cancers if the first surgery is successful, which is the blue bar, versus if the first surgery is not successful, which is the red bar. Okay? So let's just take one. Let's take head and neck, which is the second bar because it's the largest difference. If a patient has head and neck cancer and they have a successful initial surgery where the margins are clean and they don't have to have a re-operation, the five-year survival rate isn't great, but it's 55%. Okay? If, on the other hand, that first cancer surgery is not successful, that survival rate falls all the way down to 10%. So it's so important to make sure that first surgery is successful. So I chose the biggest difference as an example, but it's across the board. You can see that blue bar is higher, and in some cases, much, much higher than the red bar, really across many, many different cancer types: breast, prostate, colorectal, lung, kidney. So this is a big deal, both medically as well as from a clinical perspective. If we then expand the market, not just for intraoperative cancer care inside the United States, but really where we see the application of this technology, which is worldwide, and also moving out of the operating room to add into that what can be done in the biopsy office as well as what can be done in pathology, this TAM grows to $17 billion by our estimate. So it's huge. It's a very massive TAM with just the core technologies we have right now, and the core technologies being the imaging technology as well as the AI. So you take that. If we just look at the U.S. market, it's a $4 billion TAM, so still massive. And then if you just look at only the beachhead market, which we're in right now, which is just breast cancer and just surgery, it's $650 million. So a very, very large market. Moving to how we are performing in that market, there's a lot here, but let me just preface this by saying the demand that we're seeing from our customers is really being driven by the surgeons. It's surgeon-led. And the demand is going very, very well. So if I just start with the left-hand graph here, there was a national study done about a year ago, very, very broad study, over 17,000 patients across the United States, Medicare, commercial payments, older patients, younger patients. And about 20% of those patients had to come back in for another operation, 19.9%. Okay? We did a little study with one of our surgeons, and their first 72 patients, their re-operation had fallen all the way down to just about 5% in their first set of patients. This surgeon is still with us. It was one of the first surgeons still with us. Has now gone through hundreds and hundreds of patients and showing a phenomenal re-operation rate. This data, we actually look at it internally, although this is on a white paper on our website. We look at this data internally for the thousands and thousands of patients that we've got over 20 surgeons now using our technology across thousands of patients. And internally, we're seeing re-operation rates that are even lower than what we've seen in this first 72 patients from this particular surgeon. So there's a very, very big impact here to go from 20% down to these low numbers. And surgeons see this, and this is what's driving this demand. And they're telling their colleagues and driving a very, very strong demand for us. We talked about the benefits to the payer, right? Many, many dollars to be saved. And when the payer sees these types of performance, they get very interested in making sure that their patients are able to get access to this technology. And then from a hospital perspective, right, operational efficiency, free up operating rooms, patient satisfaction scores, of course, go way up when they can be successful in first operation. And then the other nice thing is that the hospital healthcare business is a competitive market. And so when surgeons find a new tool that helps them do their job better, they can then use that to help drive business to their practices with the referring and drive their growth in their business, which is a huge competitive tool. And we've seen our surgeons start to use that in their communities. And we're gaining market share, and we're growing revenue. You can see that just over the past four quarters, we've gone from $200,000 of revenue a quarter up to over $500,000 a quarter, and this is with our pre-AI device. When the AI device comes out, we expect that to be even stronger. We've gotten to the point now where we've started to sell the capital at a few hundred thousand dollars of capital sales. But really, the majority of this revenue is actually coming from recurring revenue, very high gross margin, 90% gross margin, recurring revenue on those consumables. A little bit of lumpiness in the revenue when you put in sort of lumpy capital sales, but this is a very monotonic increase on the consumable revenue, which, again, is that high gross margin revenue, and we're expanding. So right now, we are in the United States, where you see those red dots is where we've got some traction. But in the South, we're a small sales group. We're a small company. We've got a very small sales team. So we're not even in these very, very massive markets, $145 million market, just in the Midwest, New England area, New York, almost $100 million market, Pacific Northwest. We have no presence there. These are massive markets. If you ever look at medical device companies, they tend to start in those markets and then work their way out through some events that happened early in our sort of life as a company. We ended up headquartering in Texas. We got a grant from the state of Texas, and we expanded out from there and we've been quite successful across the country, but huge opportunities waiting for us to tackle up in the northern part. Our IP is well protected. We've got a bunch of patents around the imaging system that's proprietary to us. So that's very strongly protected. And then the AI, we've got that massive image library I talked about, over two million images and growing every day. And then, of course, the algorithms that we have underneath are trade secret protected. So it's going to be very, very difficult for anyone to come up behind us. Here's our leadership team. You can see my picture there. Andrew Berkeley, number two, is our co-founder and Chief Innovation Officer, still involved with the company. Sarah Bryan, our CFO. Dr. Ted James, in the middle of this picture right here, is our Chief Medical Officer. He ran the breast program at Harvard Medical School for a long time, has recently moved to Chicago. What his job at this large system in Chicago is to really bring best practices for breast care, breast cancer surgery to that hospital system. So he comes from a very, very, he's got a very, very nice resume. Carl has been with us. He runs our engineering group up in Toronto, been with us almost near the start. Abby Goodman 's our VP of Sales. She has a lot of experience growing companies like this from a revenue standpoint. And then Paolo Di Pasquale is our VP of Corporate Development, really running lead on our capital markets and strategic side of the business. Okay. We are publicly listed on the OTC as well as the TSXV. We're covered by three different banks. You can see on the right-hand side there, and you can see who our major shareholders Social Capital is the largest of our shareholders with a market cap of CAD 22 million Canadian, so a little bit under $20 million US. All right, so with that, just to quickly summarize before we go to questions, a very large TAM and SAM, overall, over 70% gross margin, recurring revenue model on the business. The S- Series, which is our current product we have in market, we've got a very positive reception. We're seeing great clinical results from it and a strong revenue growth on that business. The nice thing about that is what that does is it seeds the market for the next generation AI-enabled device, which is what we're calling right now a B-S eries, which hit its primary endpoints with superiority in the clinical trial we ran. And by early next year, we'll be on market pending the FDA approval. And we're growing the business from there. Right now, what we are is not demand limited. We're actually sales force limited. And so this is the challenge that we're running through right now. And it's also the opportunity for where we are right now. Great performance differentiation versus everything else that's out there with a product that's high gross margin as well as very well protected from competition. Okay. And with that, I think I'll jump over to questions. So let's see. We've got a few questions here, and I've got a few minutes, so I'll try to get through as many of them as I can. The first one here, can you clarify the timeline and regulatory steps required for U.S. commercialization of the B-Series OCT system? So yes. So we completed the trial last November. We submitted our PMA application to the FDA in March. We are in the process of interacting with the FDA in real time with that. Many of the, what they call inspections have been completed. Where we expect the final approval to be done is in early next year, Q1, let's say, late Q1, I think, is when we'll get the final decision. We have a Breakthrough Device Designation, which means that every interaction we have with the FDA moves us to the top of the list because the FDA, by giving us designation, has designated the product as solving a major problem for medicine as well as solving it in a unique manner that can be like a real dislocation in the market, so I expect that the B-Series will be in market by Q2 of next year at the latest, and so we'll see. We continue to work through the FDA, and we continue to track to that. Okay. What is driving the recurrent revenue growth? How do you expect the mix of service fees, consumables, capital assist to evolve as device placements increase? So what's driving the recurring revenue growth is that consumable. Every time a surgeon uses it with a patient, they have to use one of those consumables. And so really, it's being driven by more machines being put into the field as well as surgeons using each machine more frequently on more cases. So that's driving the growth across two dimensions. What I expect is I expect consumable sales to continue to ramp as we continue to get in market. I expect capital sales to move from a small portion of the business to a bigger portion of the business. Now that we've got a little bit more demand coming in than we did in our early days, our business strategy is actually never to sacrifice a customer because of a capital sale. So we will work with customers who, if they don't have the capital budget or they don't have whatever reasons, to get product into their hands because the consumable is actually where the high gross margin piece of the business comes from. So with minimum order quantities and things like that, we'd be willing to work with the customer in terms of capital sales just to make sure we're getting that consumable business. So I think that's it. And we're hitting the top of the clock here. So I want to wrap up by saying thank you to everyone for joining. I appreciate your time and attention. It's a great technology, very well defensible, very high gross margin. And it really solves a really big problem. That's both profitable as well as has a huge impact to human health. So thanks for the time. Please reach out if you want to talk further. I think there's some time slots over the rest of the day that we can talk. Or you can get me through our website or through the email addresses that are there, and thanks for the time.