Greetings, and wel come to the Perimeter Medical Imaging AI investor presentation. At this time, all participants are in a listen-only mode. The question and answer session will follow the formal presentation. As a reminder, this conference is being recorded. At this time, I would like to hand the call over to Glen Akselrod of Bristol Capital. Thank you. You may begin. Thanks, Darrell, and thank you everyone for joining our webcast today with Perimeter Medical Imaging. The purpose of today's presentation is to give our audience a better understanding of the business through a presentation and then questions with management. The presentation is gonna be led by Adrian Mendes, CEO. He's also joined by Andrew Berkeley, Co-Founder, and Sarah Brien, CFO. You'll see the presentation in the webcast. If you'd like a copy of this deck, simply email me at Glen, G-L-E-N, @bristolir.com. I'll be happy to send you one. When we do break for questions at the end of the management's presentation, we encourage those questions. As a reminder, we're only taking questions through the web portal. If you're listening over the telephone, please access the web link that I sent earlier to ask that question. Remember, you can submit a question using the portal within the webinar at any time. I'll ask the question on the air for everyone to hear, and then Adrian, Andrew, or Sarah will answer. I'm not gonna reference any names. We'll simply read the questions asked. As we have a fairly large audience today, if I can't get to your question online and it's not yet been addressed during the call and can be, I'll come back to you by email. I'm not gonna 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, thanks, once again 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 to everyone who joined. So it's, it's great to be here. I think Andrew, Sarah, and I are happy to walk through the business with you. Both Sarah and I are new to the organization, as of earlier this year, about six months now. Andrew is one of the co-founders of Perimeter, so he's been here since the beginning. So we're looking forward to kind of sharing with you where we're at, what our story's all about, and then any questions you might have, of course, you know, you've got, you've got access to all of us after we, after we go through a few slides here. Okay, so let's hop into it. There's our forward-looking statements. Okay, so Perimeter, what's our -- what's our... Why are we around? What are we up to, right? Why, what's our purpose? So we envision a world where patients no longer experience the emotional and physical trauma of being called back for a second surgery due to cancer left behind. Okay, this is a big problem, and what we're trying to do is we're trying to help improve this, help solve this problem. That's what we're, sort of, that's our reason of being. Let's talk about cancer for a few moments. So I don't think that we necessarily need to spend a whole lot of time talking about why cancer's bad. But cancer is, it's broad reaching, right? One in four or four in ten people have a lifetime risk of it. It's a tough one, because, you know, everyone knows if you get cancer, you know, one of the modes of treatment is to go to surgery to excise the tumor. The problem is that frequently, that surgery isn't fully successful on the first shot and then requires what they call a re-excision or a reoperation to get the rest of the cancer. So we've got some numbers down here at the bottom, which are rather large when you think about it. So almost 20, you know, 23% of all lumpectomies, breast cancer lumpectomies, require a reoperation. 11.5% of thyroid cancers, 21% of prostate cancers. It's a big problem, and it's very broad reaching. So, I'm gonna take a few slides to kind of delve into why this problem exists and then talk about how the Perimeter solution helps to address this. So the reason why these re-excision rates are so high really comes down to what they call margins, okay? So what the goal of a surgeon is, when they go and try to address a cancer situation, is to get the entire cancer out of the body. And one of the tests they use for that is what they call negative or clean margin. And so to picture that, like, a cancerous tumor fully encompassed with healthy cells, at least some amounts of healthy tissue around it. And if you can do that, then you can be fairly confident that what you've done is you've removed that entire tumor from the patient's body, and that's good. However, so that's good, and that's called a clean margin or a negative margin, just to get some terminology onto the table. However, if you don't quite achieve that, and you can see in that little cartoon right in the center of this slide, you have a situation which is called a positive margin. And a positive margin means that the tumor is exposed at the edge of the excised tissue, you know, the tissue that's removed from the body. And what that means is that more than likely, you've left some cancer cells behind in the patient's. And of course, that's a bad thing. That's, you know, if the cancer cells are there, they can continue to grow and spread, and you really haven't taken all that cancer out. And that's the root cause of many of these re-excisions, not having the required positive margin when the tumor is excised. Okay, so that's the goal, right? Getting a negative or a clean margin. The problem is it's very hard to tell from visual inspection, right? So when the surgeon takes it out, it's very, very challenging to take a look at that, you know, that tissue and determine whether your margins are clean or not. So, the surgeon does the best job that they can with preoperative imaging and other techniques. But what ends up happening is really what ends up being the standard of care, is they send that specimen down to pathology. Pathology takes a few days, up to a week, sometimes more than a week, to analyze it and then come back to the surgeon and tell them, you know, either your margins were clean or you're in negative, or you had a positive margin, and you have to call that patient back. Tough situation, right? Because that patient is now at home, maybe back in their job, back in their life, thinking that they've gone through the surgery and they're in good shape, and then they get a dreaded phone call. So that's the current standard of care. What our solution does, which I'll talk about in a few moments here, is it allows the surgeon to get the information in real time while the patient's still in the operating room, within about 15 minutes, to scan the specimen and to give the information to the surgeon for them to make clinical decisions before they send the patient home, to help reduce the chances that there's cancer left behind, and therefore reduce the chances that they would have to come back in for a reoperation. Okay? So there's the problem, right? The problem is this re-excision epidemic. For breast cancer, one in four patients will need a re-excision, which is huge. That's a huge defect rate, what I call a def ect rate. And the reason this problem still exists today, after all these years, is there really is a technology gap. And so Perimeter is a technology company. We've got a couple different modes of technology we bring to the party. And these things together are how we're addressing this technology gap. What we've got here is a picture of the device. That's that picture on the left-hand side available now. We've got two products listed here. On the left side is what we call our S-Series device. On the right side is what we call our B-Series device. The S-Series is today FDA cleared. It's in operating rooms. Surgeons are using it. The B-Series is not FDA cleared yet. It's in clinical trial right now. That's our next generation device. The base technology that both these devices use is something called optical coherence tomography. It's an imaging technology. If you look at the device picture on the left side, this OCT, what we call OCT, optical coherence tomography. The OCT module sits in the cabinet, looks upwards, and that little circular thing you see sitting on that desktop is where the tissue goes, and it images the tissue, and it looks for those margins that we talked about a couple slides ago. It provides that insight to the surgeon in real time, without having to wait for pathology to get, you know, to get to the analysis a week later. So in real time, the surgeon can look at that. What's key to this is really the resolution. So OCT technology has much better resolution, much higher resolution than X-ray than MRI, you know, 10x or 100x. There's a little diagram here, which, depending on your size of your computer screen, you can kind of see that the image on the top, which is an OCT image, you can actually see much more detail than the ultrasound image, which is below. And that's and that difference allows a surgeon to distinguish between different types of tissue. And if they can tell the difference between different types of tissue, they can tell the difference between cancerous tissue and healthy tissue, and then you can just imagine how that lends itself to identifying margins. Okay? So that's the base OCT technology. That's what we have in market today. What we have in trial, in clinical trial, the B-Series, takes that, and adds an AI layer on top of it. And so I'll, I'll talk about that a little bit more in a moment. Before we go there, I want to explain... So what's very important for new technologies in really any workflow, but in the surgeon's workflow in this situation, is does it fit in or is this going to require the surgeon to learn an entirely new way of doing business? And the good thing is that it doesn't require them to learn an entirely new way of, you know, doing their procedures. So these five steps are kind of the workflow. The first two steps are standard today. They do this with or without our technology. So step one, remove the tissue. Step two, X-ray it to make sure that they actually got the biopsy clip or the seeds that are the localization seeds out of the patients. So there's an X-ray that's done. Step three is where we come in. So take that specimen, put it onto the device and image it. Based off that information, the surgeon can make a decision of whether they believe that they don't have the margins they require. And if they don't, because the patient's right there, they can take what's called shaves. They can take a little bit more tissue in the area that the image is suggesting that the margins might not be where they need to be. Take that shave, scan that again. Hopefully, it's good. If not, take another shave, but if it is good, then you can close and with a higher degree of confidence that that patient is now cancer-free. And then, of course, so all of that stuff happens in the operating room. And then, of course, afterwards, post-operatively, step five, where all of that tissue gets sent down to pathology, where they examine it to confirm that the margins are how they should be. So this fits right into the surgeon's workflow. Okay, great. How are the surgeons actually reacting to it? I'm not going to read these quotes. I will pause for a moment so you can read it without me talking, but these are just two of many surgeons that we've got currently using the S-S eries and having positive results from it. So we're getting positive feedback from our surgeons, which is good news, and this is all on the current S-Series device. This is the one that does not have the AI in-built into it yet. Moving over to that, this is a slide that describes the B-Series, which is the thing in trial right now, with AI. It takes the same basic hardware, adds an AI software layer on top of that, which what it's really doing is it is looking at the images that are created and then indicating, sort of highlighting those areas which the AI is picking up as being suspicious, based off of all the training data we've given the model. And it's allowing the surgeon then to quickly go to that place that the, you know, that the algorithms are saying: There's a suspicious thing here in this picture, and inspect that. So rather than on the current S-S eries, the surgeon has to kind of look through everything. Here, it... With the AI, it really has the surgeon spend time looking at the things that are most suspicious. So it improves the workflow. It allows us to expand the user base because, you know, you don't have to be... It's less effortful as a surgeon to use it if you've got an AI assist with you. And it speeds up the time in the operating room. So this is what's going through FDA right now and... Or through clinical trial right now. Here are the sites that the trial is being run at. So we're, you know, we're really working very closely with some of the top cancer centers in the country, Mayo, MD Anderson, Moffitt, kind of a good swath of hospitals across the country that are really specialized in cancer research and cancer care. Okay, so that's the quick summary on the problem statement. You know, re-excisions, the next level down, margins, the solution, real-time imaging of the tissue to understand the margins, and then some validation of how things go in the field, and a little bit of roadmap for how we're adding AI on top of the products, and running that through a clinical trial right now. Okay, flipping over to the business strategy. How do we create value with this product? Like, kind of intuitive, but let's talk about it a little bit more concretely. There's four constituents, I think, that we create value for, right? The top two are kind of obvious. A patient that doesn't need to come back into the operating room and has a, you know, the first time they have the surgery, they're a success, immense value from a, from just an emotional standpoint, from a time off work standpoint, from a, you know, from all the patient care type standpoint, from pain, from risk of complications, from having to go through a second surgery. Just massive benefit accrues to the patient from only having to go through one surgery versus multiple. Benefit accrues also to the surgeon. They can provide better patient care. It's less, you know, it's less traumatic for that surgeon to have to call up the patient and tell them, you know, "I promised you I was going to do this for you, and I did it, and unfortunately, I have to do it again." Right? That's a horrible phone call that the surgeons have to make. So there's a value that accrues there. Then the bottom two is, you know, the facility and the payer. So the facility obviously would like a reputation of being able to do, you know, have a high success rate on the first time there's cancer surgery. There's all kinds of marketing benefits from that. There's OR efficiency benefits from that. So a lot of value accrues to the facility. And then the payer also, because, if you have to bring a patient in for a re-excision, that costs money. And it costs about $16,000 for every re-excision you need to bring in. And if you can eliminate that $16,000, that's just a straight up savings for whoever the payer is. So there're these four constituents. They all receive benefit from being able to reduce re-excisions. And let me go from there. Let me, like, segue from there into the actual economics behind this. Okay, so this graph is focused on breast cancer, lumpectomy specifically, and U.S. only. Okay, so these numbers you see here are in that range. So there's about 230-ish lumpectomies a year in the U.S. That's the pie, that's the full pie that you see on the left, in the little left box there. There's data that shows that there's about a 23% re-excision rate, right? So 23% of 230,000. There's 53,000 surgeries that happen that don't need to happen, but they have to happen because the margins weren't clean when that first surgery was completed. If you take those 53,000 reoperations and you multiply it by the $16,000 cost per reoperation, you get that 857. So $857 million per year spent just on fixing something that unfortunately wasn't done correct, you know, correctly the first time. $850 million dollars.... Okay, so that's the, that's sort of the total, what I call waste in the system around this particular area, in the U.S. alone every year, just for breast cancer. Okay? Doesn't even talk about any other cancer, just breast. What we're targeting. Okay, so now move your eyeballs over to the right side. What we're targeting is, and we think we can achieve this, and we've got some reasons to believe, from some things that we've seen, that we can achieve a sub-5%, 3%'s a very reasonable re-excision rate, based off of using our technology. Surgeons can get to that, we believe. With that, and then you just run the math on that, right? With that, we can move the re-excision rate, you know, if we were everywhere in surgical units for everything, right? Move it from 53,000 re-excisions a year, down to a little bit less than 7,000. And so if you just do the math on that, the difference between that, almost $750 million in healthcare savings, if we could take our re-excision rate from 23% down to 3%, which is a very reasonable goal, with the tech from what we've seen out there, sort of, you know, as we've been using this. So massive amounts of savings for healthcare. And if I flip to the next slide, it kind of shows... It kind of puts that number then in context of the whole problem. So one quick thing on the right side, you know, the U.S. only represents 10% of global cancer diagnoses and only 13% of global breast cancer, right? So there's a massive market beyond the U.S. that of course, this would apply to. The dollar figures will change because U.S. healthcare is more expensive, but, at only 13% and 10%, there's great areas to expand outside the U.S. And then we're just talking about breast right now, right? But as you can see in this pie chart, so breast cancer is that dark blue pie slice, sort of near the top. The one right beside is colorectal in gray. The pinkish sort of color is lung. The salmon, I guess color is prostate. All of those are approximately the same size. Head and neck is below that. So, about the same amount of dollars is spent in those other cancer types as in breast. So there's a way for us to take this tech and march it through the other types of tissues, to increase our SAM and our TAM, by expanding beyond just breast. So it's a massive market, and we're just starting in it with breast. Okay, this is a recognized problem. The fact that, there's a very, very high re-excision rate in cancer, the fact that this isn't just breast cancer, but most other cancer types or tissue types, is a problem that has been recognized not just with us, but most recently, through the U.S. government. Okay. So, Biden has a Cancer Moonshot fund that they funded. Through that, they're providing, and Congress, this is money that's already been appropriated by Congress. $330 million is a little quote here from Jill Biden. $330 million, focused or sort of like, sort of put towards ARPA-H. ARPA-H is a department within the NIH, and they've got a program that's focused on what they call Precision Surgical Interventions or PSI. There's a fund there. What they're doing is they are pushing this money to industry to try to help solve the problem that, you know, I just spent the last 10 minutes or 15 minutes talking about. They're running a process right now. We're in the middle of the process. They requested abstracts in August. We submitted an abstract. We got approved for a to go to the next level, so our abstract was accepted for a full proposal. We'll be submitting our full proposal November sixteenth, is when that's due, and we expect to hear back from that in January, February, Q1 of next year sometime. There's significant money behind this. We've been working on this for 10 years. We've got a great solution that actually addresses this problem directly. So we're pretty excited about this. We're excited by the fact that there is a widespread acknowledgment of this being a problem. We're excited about the fact that we have technology that addresses this firsthand, and we're excited by the fact that there's actual funding coming in, you know, non-dilutive funding coming in, to help, to help solve this. So, so we'll keep everyone updated as we progress through this process. But this is, this is good news for us. Moving on to how we monetize. So what we do right now is we have a managed solution type of approach, where we place a device into a facility for free, and then we charge for the consumables. That little round thing that you see in the picture there, that's consumable, that's where the tissue goes into, and then that whole thing gets put on top of the machine. And of course, you can only use one of those per patient, so it gets thrown out afterwards. So the model we're trying to build the company around is a recurring revenue model, where we place the machine, and then every procedure, you know, we make some money off of every procedure. The device is kind of a platform device, right? Like, which means, although we've been focused on breast right now, it does expand beyond that. Which should be the same hardware, same, same thing sitting in the operating room can expand to other tissue types as we continue to step through that process. Okay. Our go-to-market strategy. Right now, we're seeding the market with the S-Series. That's the product that is FDA cleared right now. That's, you know, out there in the world. In parallel, we've got our AI product in clinical trials. Once that goes through, we move to step three, where we actually launch the B-Series, you know, the, the, the product with AI. So we, we get to launch side of that-... Broadens our user base in the US. And then beyond that, we expand the market, right? In two directions. One is expand by indication, which means start to target other cancer types. And then we can also expand by geography, so move outside the US and tackle the rest of the markets. So that's sort of our go-to market over the next few years. We're in pretty good shape right now from a cash on the balance sheet standpoint. We've got over $20 million in the bank right now. That'll take us into 2025. So we're in a pretty secure shape that'll take us through, you know, through this trial and everything. So I think we've got the cash right now to be able to execute this strategy. So just to wrap up, not only are we envisioning a world where there are no re-excisions and all of the trauma is eliminated from that aspect of the whole, you know, ordeal of getting cancer, not just are we envisioning, we're actually creating it. We're seeing it happening based off of our technology. And that's, you know, that's our story. And so with that, I'll... I think we head over to Q&A, and I'll hand it over to Glen. Super. Thank you for that, Adrian. So we've got quite a few questions in the queue already, but to our audience, remember, if you have a question, please use the question box within the webinar portal, and we'll try to get to it before the end of the call. So first question for you, Adrian, is: What is the learning curve for a surgeon to implement the OCT imaging? I find specialized doctors to be some of the hardest humans to convince to learn something new. How are you exposing new surgeons? Are your devices used as a teaching hospitals? This seems the key to becoming an industry standard. Yeah. Okay. So, what we've-- So, yes, it's a great question. Surgeons are some of the, the hardest humans to, to change their patterns of behavior. And so what we've found... Okay, so there's two things. One is you have to find the right surgeons who are willing to use, you know, like, bring new tools into their practice. You have to target them. The stage where we're with the company, that's super key from a sales process standpoint, targeting the right surgeons. Once you identify and target those surgeons, what we found is it takes two... There's two elements to the training. One is, the ability to read the images. So you s- you know, you saw the screen with the, the OCT image. We've got a whole training curriculum that allows the surgeon to look at many of those images, and then start to train their eyes, do the pattern recognition in their brains, to identify, you know, like, what, what the different tissue types are. We have a curriculum around that. In addition to that, we find that it takes about 5, maybe 10 cases, where we have a clinician in the operating room with the surgeon, and at around that time, around 10, is when they get comfortable. So we're able to, you know, they're able to, at that point in time, after about 5 or 10 cases, able to then be on their own. We have been successful in this manner with, with surgeons. Our RCT, the clinical trial we're going through right now, that's been very successful. In those trial cases, we're not allowed to be in that room, and this is the exact procedure that we've used. So I think you're right. The premise of the question is absolutely correct. The key is finding the right surgeons and then having a trainer curriculum, which we do have and we've exercised. Okay, thank you. Next question is: What are the FDA requirements for sensitivity, specificity of the AI? Okay, I'm gonna hand that question to Andrew. Andrew, are you able to get into that in a little bit more detail? Sure. The FDA haven't outlined a specific sensitivity and specificity, but in our clinical trial, you know, the primary endpoints and the secondary endpoints lead to or demonstrating that using the technology can reduce positive margins and then subsequently reoperation rates versus what the standard of care is today. So we... I can tell you at the offline level, that our AI works in the mid-90s for sensitivity and specificity at the patch level. That doesn't necessarily translate into the operating room, but it's, the FDA are not asking us for specific sensitivity and specificity numbers. They are asking us to demonstrate that the technology is an improvement on the standard of care, and that it also demonstrates safety. So safety and effectiveness is what they're looking at, not specifically sensitivity and specificity. Okay, thank you. We've got some other questions in the queue that relate to this topic, so I'll just ask now. I think part of it you've answered already, but is there a timeline around your FDA process? So, yeah, the FDA process doesn't have a time associated with it. It has a number of patients we need to enroll and into the study. We're expecting that to wrap up towards the end of 2024. We have the opportunity to have what they call an interim analysis when we get halfway through, to take a look at the results, and if we've met the primary endpoints, then potentially stop the trial early. And we expect to hit that in the Q2 of next year sometime. So those are sort of our two dates that we're moving towards for the trial. ... Okay, thank you. I'm gonna ask another question, then I'm gonna come back to the FDA. What might be the next logical cancer indication for expansion? And how long might it take to be where you're in breast cancer today? Andrew, I'll give that to you. Yeah, I think, you know, there's, there's a couple, where we felt to getting a device into the hands of a thoracic surgeon who's gonna start looking at wedge, wedge resections for, for lung cancer. Also, we have a device right now at New York-Presbyterian, where we're looking at head and neck cancers. We've also done some work at, Columbia University in the head and neck in, in the past. And then we're also looking at, you know, liver and pancreas. I think over the next six months, we will be able to identify which one of those, cancer types is the next, you know, the most appropriate one for us to, to go after. In terms of how do we get from there to breast, the level where we are with breast. When you think about, you know, these cancer types, a lot of them will be... I would reckon they're gonna be kind of easier to image and identify where cancers are. Breast cancer is pretty difficult because of the kind of heterogeneity of the tissue and how breast cancer can grow. When you have a solid organ under the lesion in there, like the tumor in there, you have very well-defined structures. So it can be... I'm assuming it'll be a shorter path to get our second tissue type to having an indication than what we've experienced with breast. And also, we will be able to use all of our AI models through using transfer learning. All of the technology behind the AI models will be the same. We will just be putting in new data volumes for training and applying that. We'll be able to get the AI component out of it much faster as well. Okay, thank you. I'm gonna come back to the FDA portion of it: are you gonna have to take both the S-Series and the B-Series through another FDA process for all in other indications? Yeah, maybe I can- Andrew, if you want- If we want- Yeah, go for it. If we want to add an indication, yes, we will have to go back to the FDA and prove that we can be both safe and effective in using the technology on those patient groups. However, we will be able to use the existing technology, the S-S eries and then the B-S eries as a predicate. So the level of clinical validation, that i.e., the clinical trial that we would have to do, would probably have to be substantially smaller than what the initial breast indications or the original breast regulatory approach was. Okay, thank you. Switching to the technology, a couple of questions here that I'll combine into one. What is your IP around the technology? And then the second part, is how defensible is that IP? So I'll start, and then Andrew can go into more detail. We've got six patents right now, and a couple more in the pipeline, mostly covering the OCT portion of the technology in various aspects. And then... Okay, so that's part of the IP that we've got in the company. The other part of the IP that we have in the company is on the AI side of the house. And that's of course, you know, patent, it end up being trade secrets. And so, you know, those, that sort of describes the IP portfolio. Andrew, do you want to go to the next level of detail on any of that? Yeah. So the technology we use is OCT. OCT, you know, has been used in ophthalmology for the last 20 years. There's about 30 million eye exams done every year with OCT. But the field of imaging with OCT is generally very small, on the order of one centimeter by one centimeter, like the back of your eye or for cardiovascular imaging in order to image inside a vessel. What we've done is we've taken OCT, and we've applied some translation to the imaging probe. So that allows us to image much larger complex tissue specimens in a really fast amount of time. So from an imaging perspective, we are the creators of wide-field OCT. We can scan up to a 10 by 10 centimeter area, which is 100 times bigger than the footprint of traditional OCT. We have IP around all of the stitching algorithms that, you know, take all the images and put them together to create a uniform output. We also have, and it's quite significant, IP around tissue handling. When you're imaging at really high resolution, you need to have a very steady, still flattened surface, for these, you know, tissue specimens. So we created a tissue management system that holds the specimen using vacuum very steady and allows us to do rapid scanning. So that's also part of our IP. We've also looked at combining OCT with specimen X-ray, which is part of the workflow, as kind of like a protection against some of the specimen X-ray companies. So we have IP around combining those. And then we also have IP in the AI space, for the work that we're doing with our, you know, cancer, you know, suspicious, feature finding, technology, which is not specific to breast. So that IP will be, you know, related as we go into other indications as well. ... Okay, thank you for that. Next question: Will the launch of AI technology require any changes to existing, I guess, S-S eries hardware, or can existing machines be given a software update? Yeah, the short answer is- Sorry. Yeah. We're about to say the same thing, is that the hardware is the same. We will have to do... It's mostly software, so we'll have to bring the machines back because they are gonna be different FDA part numbers, right? You have to distinguish them very clearly. So we do a software upgrade, but all the hardware is the same, the supply chain is the same, the manufacturing of the hardware is the same. So we get to, it is a big leverage that we get to use across both the S-Series and the B-Series. Okay, thank you. On slide seven, for the OCT view, what is cancer and what is non-cancer, and how easy is it for surgeons to differentiate? On slide seven. This slide? I think that's slide five. Slide five. Yes, this slide. What is cancer and what is non-cancer? Anybody, yeah. Yeah. This is actually an image comparing resolution between OCT and ultrasound. This is the cross-section of a human fingernail. You can see on the OCT image, on the very left, you can see the top of the nail, bottom, and then the nail roots. Then you can see on the right side, that's the layer of skin and tiny blood vessels. When you look at ultrasounds, you don't see any of that architecture. You can see deeper, but you can see bone at the bottom, but you can't see any of the microscopic features that you can see in OCT. So when it comes to visualizing cancer, we don't actually have an image of it in here, but- Actually, we do, Andrew. If we go over here, I think this is an image, right? Yeah. Yeah, in the middle, we actually have a very early stage breast cancer example, where there is a cancer cell inside of a breast duct. So normal ducts would not be expanded. They would to be very flat and thin. Here we can see that the cells have expanded the duct, and there's even a tiny little microcalcification inside of this. And when you're panning through the volumes, you can track this, and you can see how it has evolved, how it evolves. So it's more or less an exercise in, you know, first of all, identifying something that looks abnormal, and then just applying some pattern recognition techniques to confirm that it is a suspicious area. You know, cancer looks very different to most normal tissue. It's unorganized. You know, the cells are all different shapes and sizes, and they're densely packed together where, you know, normal tissue features have much more of a structure to them, and that's what helps us differentiate between one and the other. Okay, thank you. You've talked about surgeons. Question is, is the radiology department in the hospital involved in reading any of these tests? The answer for now is no. Radiology generally is not involved in the surgery. Sometimes there's a pathologist who will help the surgeon post-excision. They will cut up the specimen and look at it. So we are actually looking at, you know, maybe in those particular sites, using the pathologist and their skills, to be the image reader. But from a radiology perspective, the answer is no. I will say, though, that we do have a device at Columbia University in New York, where there's a prominent radiologist in the breast space who is looking at using our technology for almost immediate diagnosis at the biopsy stage. So what happens today is, during a biopsy, tissue comes out, it goes to the lab. It takes a week, two weeks, for them to get a diagnosis on whether or not the patient has cancer. The patient is at home. You know, it's a very stressful time for two weeks. What the premise of the technology would be is we would be able to have a really high confidence that the patient either does not have cancer or does have cancer whilst they're getting their biopsy. And that would fast-track the treatment path for the patients who do have cancer, and it would put the patients who have benign features, you know, in a much more relaxed scenario until they've got their final diagnosis and pathology. Okay, super. And actually sticking on the same theme, will the histopathologist see his or her workload decrease? Great. Yeah, that's a really interesting one. You know, throughout the whole development of this technology, we've worked more probably with pathology, especially on the AI side of things. Because we're creating images that almost replicate what the pathologist sees on the microscope. Where we see the value for the pathologist is the information that's captured in the operating room to, you know, identify positive margins can be exported to pathology. So as they begin their processing of the tissue, they can call up the images that the surgeon captured in the operating room and almost use our technology like a Google Maps. They will be able to see where the highlighted areas that are suspicious are, and then they will be able to see below the surface and actually have an indication of what they expect to see when they create their histology slides. So right now, you know, they are not randomly sampling, but they sample all over the tissue surface. With our technology and with our information it potentially would make them more accurate as to where they want to sample, and then it makes them more efficient because they could spend less time processing tissue that had no disease or had no suspicious areas in it, and focus on areas that, you know, our AI or with a human eye have found to be suspicious. Thank you. Next question: In your trial/testing, have you had any surgeons reject the use of this protocol, and if so, why? And further, were you able to change their opinions? So in the trial, the answer is no, because, you know, by virtue of the trial, they sign up to the protocol, and then they, you know, they perform according to the protocol. So, so there hasn't been that situation. Now, I think the question underneath the question is, are there surgeons out there that don't want to take the time, don't want to take the effort, to bring this in? And the answer is yes, of course, there are those surgeons. I think maybe a previous question sort of alluded to this. And so I think there's a couple, you know, reasons for that as we, as we look through this. One is those surgeons that, don't believe that their re-excision rates are as bad, perhaps, as the average. And this is, I don't know, this is a little bit like the joke where, you know, everyone is a better than average driver. People often underestimate, or overestimate, I guess, their performance. And so when we've worked with, you know, some surgeons to start to track or go back and take a look, frequently, you know, they're a little bit surprised at how high their re-excision rates actually are. So, so that's one of the barriers we have to, to adoption that we have to work through. And then the other is identifying those surgeons that are actually open to learning new tools and learning new techniques. And at the stage of sort of go-to-market we're at right now, that's a very important filter for us, where we need to focus on the early adopters, the ones that are willing to take on the effort of learning it and then get the results from that. And there's a specific surgeon profile that fits that or type of surgeon that sort of fits that profile. And so we focus our sales efforts on those types of surgeons. Okay, thank you. I know you've covered this in your formal remarks, but maybe a little bit more time spent on this. Can you review your commercialization strategy in terms of cost and current placements, target placements and the sales force trainers in place and that you require? Sure. So right now, from a sort of how do we present this to our customers is, the most common model we have right now is we'll replace the unit for free. And, it's like a managed service, basically. And then we charge at the rate of $900 generally per consumable. So this is what we've been experimenting with so far. I think there are opportunities for us to evolve that as well. So not only have exactly what I just said, but ways, you know, some hospitals actually don't like placements where they don't actually buy the capital for different financial reasons. So I think there's opportunities to explore actually selling the product and perhaps having a lower consumable price, perhaps having a higher consumable price. This is an area for improvement for the company, just us experimenting with go-to-market in a way that is, you know, that allows us to really explore the boundaries of what's possible on the commercial side. Okay, so that's number one. Number two is, we do right now in the early stages of placing product with our customers. It does require clinician support. So our sales force looks like what we call sellers or MDMs, market development managers, which are really the salespeople, the relationship salespeople. And then the clinical support people, what we call clinical application specialists, who are actually in the OR rooms with the surgeons, helping them to understand how to use a product, helping them with using the product, and making them comfortable with it. One of the challenges we have had that we're working through right now is the speed by which we get our surgeons to that truly being independent stage and feeling confident. So I described earlier in the prepared remarks the fact that for the clinical trial, it takes maybe 5-10, what we call roll-in cases, where our clinical specialists are in the operating room with the surgeon, and by the end of that, they're comfortable. Depending, the motivations for a clinical trial are sometimes different than the motivations for a commercial site. And so, what we're working through is how do we get surgeons to that independent stage quicker? The quicker we can get them there, the cost structure gets better because now we don't have to place, you know, a permanent employee in the operating room for, for as many procedures as we used to. We get to reuse that resource to, you know, get the next customer on board. And so this is, this is a key part of how we build out our commercial team. Right now, probably for the next 6 months to 6 to 9 months, we're really focusing the commercial team on getting these high-value users, high, high, what do you call it? Early adopter users on board, advocates for us, those surgeons that can then talk to their peers, and help promote our product with their peers, and trying to make sure that every customer we get on board over the next little while is a highly leveraged customer, that almost becomes sort of an extension of our sales force, and can advocate for us, advocate for the product, advocate for the use of the product, and then also, frankly, give, usability feedback back into our innovation and engineering team, so that we can build out our, our pipeline of product development, of how we make this product easier and easier as we sort of scrap through the entire market base. ... Thank you. Next question. Perhaps it's too cynical, but aren't doctors and facilities going to be hurt by the reduction of $750 million in revenues? Are all of their benefits reputational? Yes. Cynical, but there is some truth to it. On one hand, in a myopic or a short-term phase, if you take business away, then that can hurt the top line of the customer or sorry, at the hospital. But you know, from our position, you know, bringing efficiencies is always a good thing. So then the question is: how do you manifest that in the real world, such that you get through the short-termism or the cynicism that you know, the question just indicated? And there's a couple ways you do that. One is through the payers, right? So, working with those hospitals that are integrated health networks, where they self-insure, right? So that's, like, an obvious benefit to those people if they don't have to pay an extra $60,000 for every re-excision. It hits their bottom line, and it doesn't affect their top line, so that's a big deal. The possibility of bringing this technology in and then advocating, with patients, you know, with, with surgeons, that there is this hospital that has this technology, that reduces your risk of having to go through a re-excision, and drives more business, so to speak, to that hospital. There's opportunities there, that we are exploring, that we continue to explore. So on one hand, it does, and it's something that we do have to work through, and we are working through. But on the other hand, the benefit to the overall system is such that, it's... That's more of a tactical concern than a strategic concern. Strategically, we need to work through it, and we are. Good. Thank you. Next question is: What type of prostate cancer surgery can lead to a positive margins? Is it radical prostatectomy? Andrew, do you... Can you take that question? Sorry, can you just repeat that for me, please? Yes, I guess I deleted the question, but just talk about what type of prostate cancer could potentially use your OCT technology. Yeah. So I think there's different stages of prostate cancer and depending on the stage of cancer, you know, that can impact the type of or how the surgery is performed. If the cancer extends past the capsule, you know, then there's additional tissue that needs to be taken around the neurovascular bundle, but at the high sensitivity of taking tissue there. Generally, they don't go back for second surgeries when they have a positive margin, but they will administer additional radiation therapy when there's a positive margin, which is actually quite a significant cost. So, you know, in the long term, what we would look at for prostate is, you know, can we establish for some prostate cancer surgeries if there was a positive margin, and can the surgeon go back and actually take more tissue that's not going to be damaging? Okay, thank you. Next question: Can you talk a little bit about the competitive landscape? Are there competing products? If not current, are there any in development, and how far are they from the market? Yeah. There's no directly competing product, like, in, in the way that we're approaching the problem. There's other types of solutions that try to address it. And Andrew, yeah, I think you're probably best positioned to be able to go into details about those. The closest one to us in terms of where they are in their development would be, there's a couple of fluorescence companies, where they inject an agent into the patients a number of hours before the surgery. That agent then binds to the cancer cells. They remove the specimen. If there is cancer left in the patients, then they illuminate the cavity with a special light, and it's supposed to allow those cancer cells to fluoresce so that you can see them. You know, it's, it has potential for large, you know, invasive tumors, but when there's microscopic disease, particularly when it's in situ disease, where there's not a lot of cells and they're already inside of a duct, let's say, for example, it's very hard to get a fluorescent signal from, you know, that type of a situation. And then what would happen is you need to turn up the sensitivity of the camera to see that very low-level fluorescent signal, and then you get a lot of false positives. So fluorescence, I think, will have a place in cancer surgery, but I don't think it's going to solve the problem in breast cancer, which is our primary approach. And there's also some technical issues, you know, like a lot of these surgeries are done as an outpatient. If you have to inject these, the agents into a patient 6, 8 hours before a surgery, that means they have to come in the day before, which is a completely different in the U.S., it's a completely different, payment system. There's different codes, you know, an inpatient as opposed to an outpatient. It's much more expensive surgery, when you're in within the hospital system as opposed to being in an outpatient ambulatory care center. So there's many different factors of, you know, the workflow and, the actual clinical efficacy when it comes to fluorescence. But that would be the kind of most competitive technology or technique out there. Okay, thanks. And sticking, I guess, on this theme, here's the question: I read that Microsoft is getting into AI for breast cancer. Is this competition or potential partner? This is potential partner as opposed to competition. So what Microsoft's intent is, is twofold. One is they're trying to sell cloud services, right? So they would love to get everyone's data onto their cloud Azure. That's number one. Number two is they're trying to sell AI services. And obviously, you know, as we've discussed, AI has a strong place in cancer detection. So this is how they're looking at it. They're not, they're not getting into the imaging business. They're not getting into sort of like placing things in hospitals or being that close, you know, in the direct line with the surgeons. So really what they're trying to provide is, you know, compute services, cloud services, and AI services to the companies like us. So there's a strong, you know, potential for partnership there, which, you know, which is helpful for a company like us. Okay. Thank you. Next question. Can you explain more about the ARPA-H? What was the $15 million-$20 million in the slide referring to? Oh, yeah. So the 15-20 million in the slide was referring to what's... It, it's an estimate right now of what we think could be the, you know, order of magnitude of what we might get awarded if we are successful in, you know, in our proposal. So there's, I think $240 million is what we've been indicated has been allocated to the PSI fund. And then we're estimating somewhere between 10-15 proposals being accepted for funding. And so that's sort of like the estimate. If we had to, like, you know, there's a $330 million total fund, but our estimate is something on the order of 15-20 million dollars might be what, if we are successful, could be, you know, our piece of it. Okay, thank you. Next question: What is your current availability of inventory of the S-Series units? We have, I think, in our inventory something like 30 or 40, maybe 30, 30 devices sitting inventory, both in our own facility and in our manufacturing facility. So we're fairly well established to be able to ramp the business without having to put more capital, you know, more capital expenditures, in to ramp. So, I'm not concerned about supply chain issues at this point. I'm not concerned about, you know, needing to find financing to be able to buy a bunch of new machines, to grow the business. So I think we're in pretty decent shape on that front. Okay, great. I see we're coming to the top of the hour, so we've got, quite a few more questions in the queue. We'll get back to those individuals that didn't get an answer by email. I think we covered most of it, but here's a good topic that we haven't covered yet. Can you clarify the cash runway, and do you have cash to end 2025 or, or the beginning of 2025? Yeah, we've got cash into the beginning of 2025, with the, you know, 2024, a little bit over $20 million we have right now in the bank, or we had at the last quarter close in the bank. And looking at our projected, you know, cash spend, it's kind of in line with historicals. So, yeah, that takes us into the beginning of 2025, which takes us over the boundaries of a few key items for us, right? It takes us over the threshold of when, the ARPA-H decision comes in. It takes us over the threshold of us clearing the RCT trial for the B-S eries. So it gets us, you know, it gets us a little bit of ways into the future. Okay, perfect. Thanks, Adrian. What I'm gonna do is ask you now if you have any closing remarks, and again, if your question has not been answered, I'll be sure to get back to you via email. Since we're at the top of the hour, some closing remarks from you, Adrian, and then we'll end the call. Yeah. No, thanks. First of all, I appreciate everyone's, you know, time coming and joining and listening. I think fundamentally, the way... You know, my overall summary view of the business is, the technology's unique. The technology gets the job done, that we need to get done. The small portion of the market that we're attacking right now has a huge economic sort of benefit, and what we have between OCT and AI can address that. The challenges we have in front of us right now is, learning how to, not learning, but, continuing to evolve how we bring this product to market, ramping up the sales process, getting more and more surgeon advocates, you know, talking about this product, and really ramping up the commercial side of the business. I think that's, you know, a big job for all of us here inside the company over the next year. And then on the trial side, is ensuring that we've given everything we can to have a successful trial on the AI product. But, you know, that's the work we've got cut out for us over the next 12 months. The fundamentals of the company is strong, the cash position is strong, the team is strong, so, I'm encouraged. A lot of work to do, but I think we're heading in the right direction here. Perfect. Thank you for that. Thank you, Adrian. Thank you, Sarah. Thank you, Andrew. Thanks to our audience, and this concludes this presentation. Thank you. This does conclude today's teleconference. 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