No surprise, almost 60% of women miss their annual mammogram. If it's only that accurate, why go? And I already mentioned every 14 seconds, someone is diagnosed with breast cancer around the world. We know, according to statistics from the American Cancer Society, that if we can catch that breast cancer at stage one, the woman or the man (yes, 1% of all breast cancers occur in men) has a 99% survival rate. So that's our goal. Our goal is to catch all of those cancers in stage one. And not only is it better for the patient, but it's better for the healthcare system as a whole. By catching cancers just one stage earlier in their progression, we can save over $3.7 billion a year. So why invest? You know what we do, you know why it's needed. Why invest in iCAD? Here are some of the key highlights of our performance over the past few years. If you know anything about mammography, there are two types of mammography images that are available. One is two-dimensional or 2D. That used to be predominant in the U.S. It's now been replaced by 3D images, which, yes, they're more accurate, but there's also many more images. In 2D, four images are taken. In 3D, 150 approximately images are taken. Yet radiologists are still pressured to see the same quantity of patients each day and be able to return results almost in real time. We are transitioning to a software-as-a-service-based organization. We are a software company and moving from a traditional or more legacy perpetual license model to a subscription and cloud-based model, and our subscription revenues are growing. You're going to see a chart showing you our progress here in just a minute. It's also still a growing market. Only 37% of all mammography clinics in the United States use AI, and that's an even smaller percentage when you look at it from a global basis. We have over 4,000 customers around the world, and we recently executed a 20-year partnership with Google Health. When's the last time you heard of a 20-year tech partnership? We're in it for the long haul with Google Health, and we're going to be developing some game-changing algorithms. So it is a technology partnership to develop world-class, more accurate algorithms with Google. And we have a strong cash position, so we actually don't need to raise additional funding in order to deliver on our initiatives. Just taking a step and looking at iCAD by the numbers, we have $9.2 million as of the end of Q2 in our recurring revenue model. It's about 46% of our GAAP revenue. And since we've transitioned to a subscription-based business, our customer retention is over 93%. I already mentioned our strong cash position. We also have a strong and healthy gross margin. I've already mentioned we have 4,000 customers. Only 10 customers make up 25% of our revenue base, and our Net Promoter Score amongst our customers is 90, so a high customer satisfaction. A couple more numbers, charts, and then we'll take a step back and look a little bit more at what the technology actually delivers. We've had a couple of inflection points in the company. If you're familiar with our story, we have been around for a while, but we did two key things in the last 18 months, and those are represented by these vertical lines in these charts. Starting with the EBITDA chart on your left-hand side, you can see that we did a big change in our management team and brought in a new strategy just about 18 months ago, and that's kind of that pink line. Two things that we did at that time. One, we divested of a business unit that really wasn't synergistic with our AI software business, and so that helped us strengthen the cash balance sheet, also helped with our focus and reduced our cash burn, and then we also right-sized the AI software business. Particularly with moving to a subscription-based business, we could be a lot more efficient in our operations, and then you can see how that's translated into stability and to our cash burn. Looking at that ARR number, starting with the left-hand side, you can see the bar chart. It's actually made up of three components, the last of which is very new, so the darker blue is our recurring revenue from maintenance and service agreements on a traditional license model. The light blue, we began offering our software on a subscription basis in Q1 of 2022, and at the end of Q1 of 2024, we finally rolled out our cloud solution, so if you have really great eyesight, you can probably see a thin gray top to the very last bar. You can see what percentage then of GAAP revenue is made up by our recurring revenue. You're actually going to see over time that dark blue is going to be able to transition into the light blue and gray as legacy customers who don't want the care and feeding of on-premise hardware-based, server-based software begin transitioning to subscription and to cloud. Let's take a look a little bit deeper at what our solution actually does. We have a suite of software solutions that help radiologists in the breast cancer space, and there are four key solutions, and they answer four critical questions. Cancer detection algorithm answers the question, "Do I have cancer today?" So that algorithm is ran at the time that a woman comes in for her annual screening mammogram. Breast density actually scores and categorizes how dense a woman's breasts are. This is something that is critically important because over 50% of all breast cancers occur in women with dense breasts. And believe it or not, as a woman, as the target market, you don't know, right? There was a very radical diversity in how breast density was characterized in the past. And from provider to provider, you could actually get a different answer as a patient. Our risk solution is able to honestly detect cancer before it's cancer, so our risk solution can pattern match on mammograms and be able to predict the likelihood of developing cancer within the next one to two years, and then last but not least, our heart health solution is an algorithm that can detect and score the presence of calcification in breast tissues by looking at a mammogram and predict the onset of cardiovascular disease, which, by the way, is the number one killer of women, and breast cancer is number two, and then the proof behind our organization and our algorithm you can see on the right-hand side, we read about eight million mammograms a year on a global basis, and we pull data to support training our algorithm and our regulatory submission processes from over 100 sites. Just really quickly dive a bit deeper into each of the four algorithms. I mentioned our detection algorithm is our foundation algorithm. It's ran each time a woman comes in for a mammogram and helps answer the question of, "Do I have cancer today?" It is two times more accurate than a radiologist reading without AI and compared to other AI algorithms out there. It has an 8% improvement in sensitivity, which is the ability to get it right, to actually accurately detect that there's breast cancer. It has a 7% reduction in recall rate. Why is that important? One, it's very important for patient satisfaction. No one wants to be told, "I'm not sure. You're going to have to come back in three weeks. I need to take your mammograms again." Do I have cancer? I don't know. Was it just that the image was a little blurry? You don't know why you're being recalled, and it's additional time-consuming and expensive for the imaging center itself, and then last but not least, we're able to reduce reading time by 52%. Why is this important? In that transition from 2D mammography, a radiologist had to look at four pictures. Now in 3D mammography, he or she has to look at 150, and guess what? They look at 150 in that same four to six minutes. I don't know about you, but if somebody's telling me if I have cancer or not, I'd love for them to spend more than four to six minutes looking at 150 images, and if I can go in as an AI algorithm and very accurately process those images, enable the patients to still get that real-time feedback, then that's a win-win. I already mentioned breast density and the fact that density is one of the leading risk factors for breast cancer. Our solution is able to look at not only the structure of the breast tissue, but take some other demographic information into consideration, like age and menopausal status, and actually score the density of a woman's breast. This is so important that finally in the United States, in September of this year, so just about 30 days ago, it's now a standard that every woman needs to know her breast density categorization, and it's now a standard part of her health record. I mentioned our risk algorithm. So the detection algorithm and the density algorithm are cleared on a global basis. Our risk algorithm is cleared for use in Europe. It's cleared in Canada, and it's making its way through the FDA. For the FDA, there are about 830 health-related artificial intelligence solutions that they regulate or provide guidelines. Only 22 are De novo or first of their kind. Our risk solution is a De Novo device, which is great. First of its kind, we get to set the standard. It also takes a bit longer to get cleared through the FDA when you are first of its kind, especially when your solution, its claims, are that we can detect cancer one to two years before cancer would ever appear on a standard screening mammogram. And if you know that many cancers are caught one to two years after they actually begin forming, up to four years earlier, we can stop cancer in its tracks. That's what it's going to take to create a world where cancer can't hide. And then, last but not least, our newest algorithm, it is also making its way through FDA clearance. And this algorithm also looks at a mammogram. And as I mentioned earlier, it's able to detect, to circle and identify, and then quantify the presence of calcification in breast tissues, which, again, being that target market, I wish I would have known that I had early onset of cardiovascular disease from my mammogram until I had to wait until I had a health incident to find it. So just coming back up now from the solution set and looking at the company, iCAD has been around for a while. Many of you may have followed the story of iCAD over the past several years. We have an amazing track record in innovation and delivering game-changing solutions for breast cancer. We developed and actually received our first FDA clearance for an AI solution to detect breast cancer in 2002. That was a very different form of artificial intelligence than what we have available today. And today we have making its way through FDA clearance, our fourth-generation AI built on a brand new neural network that we developed in conjunction with Duke University. And you can see on this timeline the critical milestones for the other solutions that I've mentioned to you today. In the end of first quarter of this year, so first quarter 2024, the very last week of the quarter, if you're keeping track, we actually launched our cloud solution. And our cloud solution not only provides benefits for our customers, or you guys can see the batteries running low too. I can't see the mouse, actually, to be able to - oh, I found it. There it is. Okay, great. We launched our cloud solution at the end of first quarter of 2024. We built our cloud solution also in partnership with Google Health. We actually have a two-part partnership with Google. It has enabled us to, in just a short time, it's been available very quickly onboard brand new customers, as well as for existing customers. And we've had some existing customers that have migrated to cloud, been able to get updates to the technology very quickly. It provides many benefits for our shareholders and stakeholders. Moving to a cloud or a traditional software as a service business has created a very predictable high-margin revenue stream. It gives us long-term predictability in terms of understanding our profitability and our cash flow, and then building this ARR pipeline. For example, the 10 cloud deals alone that we closed in Q2 added $1.2 million, right, to our billings backlog and then future GAAP revenue. We also play very well with others. You can't have been around in this business for as long as iCAD have to have the reputation and the proven expertise in order to warrant garnering a 20-year partnership with Google Health and not know your role in the ecosystem and play well with the major players. So today we work on all of the gantry systems, major gantry systems that are available. A gantry system is the actual machine that, as a woman, you get your image, right, your mammography, your mammogram taken. So that's companies like Hologic or GE or Siemens or Fuji. And that's in the 9:00 A.M. to noon quadrant of this circle. Next are some of our key customers. So I already mentioned Cleveland Clinic. Baylor Scott and White is a major customer. Solis Mammography with 115 sites. Simon Med with over 300 sites. Last quarter, we announced winning US Radiology, which, as they roll out, will be 3,000 sites. They do about 10% of all mammograms in the United States. We have a high degree of interoperability, and actually this has been a competitive differentiator for us. There are numerous technology systems that all get put together to create the end-to-end workflow for a radiologist, and we have to interop with all of those. And then last but not least, we don't do this alone. We want the best and brightest minds out there helping to challenge and improve our algorithms. So we have co-development and co-research partnerships with a variety of institutions. I specifically mentioned Duke as an example of the one that we signed a most recent partnership with and developed our 4.0 algorithm. Our US market opportunity, I mentioned earlier, in the United States, believe it or not, only 37% of all mammography centers use AI. That means 63% of all mammography in the United States is not being read with AI. Of that 37%, we have about half of that market. The other half is largely Hologic. Hologic does bundle the algorithm with their gantry. So there's plenty of room for expansion within the United States, not to mention globally. So we're in about 50 countries today. Those are represented in the pink, which collectively it's about 1,500 facilities or imaging centers around the world. We read about 8 million mammograms on an annual basis. You can see, for example, the countries in blue. There are seven of those, and they represent a total of an additional 42 million women that we could serve. So there's still very large untapped markets around the world. Just to summarize here and then take a few questions, I talked about our expanding customer base, particularly with the advance of our cloud solution and its availability happening at the end of first quarter. It's opened up new markets for us, both here in the U.S. and around the world. Our strategic partnerships, not only with companies like Google Health, but Duke University and others. We also have co-development relationships with some of our largest customers. They're pushing the edge in terms of women's health and the breast health space. It's interesting to see how much additional health information we can mine from a single mammogram. And then just by way of an example, a new metric that we began reporting was our deal count to help people understand what percentage of our revenue base is our traditional licensing approach, which is perpetual, represented by the 60 here. 29 customers signed up a subscription license last quarter and 10 to cloud. So we're seeing growing adoption happening across all of those ways that we can sell, support, and deploy our technology. And with that, I thank you. It looks like on the clock we got about four minutes left if you have any questions. Yeah, gentlemen in the back. So just want to clarify on the breast density. Yes. The The number that you cite as 40% going unrecognized or undiagnosed, that's primarily in density type C and D. There's more muscle than fat. It's very hard to pick up those spots on those images. What you're doing targets primarily the readouts on density A and D. Would that be a fair statement? No, actually, because breast density is also a bit of biology. There's a bit of race and ethnicity. And so we have imaging centers that work in those communities, and the solution is helping them equally well. So that would be those women that are scoring in the C and the D levels. Yeah. Then I guess my second question is more so about valuation and market opportunity. You said you have about 15% of the market today, and you guys are doing a thing on an annualized basis close to $20 million in the market. If you were to even capture, say, 50% of the market in the US, and we have the highest spend out of anywhere else in the world, probably we spend more here on that than everywhere else in the world combined, is my guess. What is the path to additional growth for the business if you hit that 50% milestone, then what? Yeah. Oh, there's so much to do. So let's say outside of license-based revenue, right, what are additional opportunities to grow? Data. So there are huge markets for data monetization. So we've shown in just some early exploratory conversations the ability for our data to help inform life science companies to better field their clinical trials. So many clinical trials today don't conclude successfully, generally for one, a lack of patient participation, and two, a lack of appropriate diversity in the patients that are participating. So we're able to very quickly assess inclusion and exclusion criteria. We can pass that information over to life sciences, including the geographic and the demographic diversity that's required, right, for them to get clearance, right, for their trial. So data monetization is a huge forward path for us. The other thing that's very interesting is more and more providers are looking for ways to go online and virtual clinics, and they're able to do that on a global basis. So it's a bit of like thinking kind of direct to the patient, not having to have the patient necessarily go in through brick and mortar. So again, if a patient has a mammogram, they can upload those images, and there's lots of other health information and risk analysis we can do and provide those outcomes and care treatment paths back to the providers and the patients. Thank you. That one minute. Yes. What percentage of your model's training data is proprietary to you guys? It's actually about 50/50. Of those 100 sites, there's a large number of them, and a number of them have very large amounts of data that's outside of the US, which is important because when we talk about these clinical trials, it's equally important for life science companies. We may think of them as US-based, but they also want to do their trials on populations outside of the US, right? So they have global applicability and can get that ROI for that drug development. Great. Well, thanks, everyone.