Key Takeaways
- AI-driven mass spectrometry of urine samples offers a scalable, non-invasive alternative to genetic sequencing that can reduce screening costs to under $50.
Revolutionizing cancer detection with affordable cancer screening technology is no longer a distant dream.
TOBY, an innovative healthcare startup, is leading the charge in developing a urine-based cancer detection method that promises to transform early-stage cancer screening. This groundbreaking approach combines mass spectrometry and artificial intelligence to create a cost-effective, non-invasive screening solution that could save countless lives.
Toby's innovative approach to cancer detection leverages existing technology in a novel way. By using mass spectrometry and AI, they've developed a urine-based test that can screen for multiple types of cancer simultaneously. This method offers several advantages over traditional screening methods:
Matthew Laskowski, CEO and co-founder of TOBY, explains, "We take all that data and we have different flavors of algorithms or different pattern recognition algorithms. We've already created ours for prostate cancer, we've also created it for kidney cancer, and we promised our investors we could develop a screening test for bladder cancer for under $100,000 in under 90 days."
Artificial intelligence is at the heart of Toby's innovative approach to cancer detection. Unlike genetic sequencing, which can be costly and time-consuming, Toby's AI-powered system analyzes mass spectrometry data to identify cancer markers quickly and efficiently.
The use of AI in cancer diagnostics offers several key advantages:
Laskowski notes, "We generate 3.5 million data sets, and normally today, you take all that data, and in that, there's hundreds and hundreds of biomarkers. You throw everything away except the biomarker for what you're using. What we do is we take all that data and we have different algorithms for the cancers to see if our test pops for that other cancer."
Toby's approach to product development sets it apart from traditional diagnostic companies. By focusing on commercialization and scalability from the outset, Toby aims to avoid the pitfalls that have hindered other startups in this space.
Key aspects of Toby's innovative approach include:
This strategy has already garnered recognition within the industry. Toby recently won first place in the 42 plus one competition organized by the Association of Diagnostic and Laboratory Medicine, securing approximately $1 million in financing.
Toby's vision extends beyond its current capabilities. The company aims to develop a comprehensive multi-cancer screening test that could revolutionize preventive care. Some of the potential developments include:
Laskowski emphasizes the potential impact: "We want to create a 10 or multi-cancer test along with a few other neurological diseases where there's a lot of research indicating that this works. Someone just has to bring it to market."
One of the most significant advantages of Toby's technology is its cost-effectiveness. Traditional cancer screening methods can be prohibitively expensive, limiting their availability and frequency of use. Toby's approach changes this paradigm:
This cost-effectiveness could make regular cancer screening a reality for a much larger portion of the population, potentially saving countless lives through early detection.
Despite its promising technology, Toby faces several challenges in bringing its product to market. These include:
However, Toby's leadership team believes their unique approach and focus on commercialization will help them overcome these obstacles. As Laskowski states, "We have a very unique commercial proposition here."
The potential impact of Toby's technology on public health is significant. By making cancer screening more accessible and affordable, it could lead to:
As Laskowski points out, "When you find cancer early, and you know it's much cheaper to do preventative care than acute care, and so I think that business case is pretty clear."
In conclusion, Toby's affordable cancer screening technology represents a significant step forward in the fight against cancer. By combining AI, mass spectrometry, and a focus on commercialization, Toby is poised to transform cancer detection and potentially save countless lives. As healthcare providers and institutions consider adopting this technology, we may be on the brink of a new era in preventive care and early cancer detection.
Toby's technology uses mass spectrometry to analyze urine samples, combined with AI algorithms to detect cancer markers. This non-invasive method can screen for multiple types of cancer simultaneously.
While specific accuracy rates may vary, Toby reports high accuracy in differentiating between cancer samples and healthy samples, with area under the curve values of over 0.95 in initial studies.
The test costs less than $50 to perform, making it significantly more affordable than many traditional cancer screening methods.
Yes, Toby's technology shows promise in detecting early-stage cancers, which is crucial for improving treatment outcomes.
Toby's approach is faster and more cost-effective than genetic sequencing, while still providing high accuracy in cancer detection.
As of now, Toby's test can detect prostate and kidney cancer, with bladder cancer detection in development. The company aims to expand to a 10-cancer screening test in the future.
Toby's test is designed to be easily integrated into routine health check-ups, requiring only a small urine sample that can be analyzed using existing equipment in many hospitals and labs.
<p>hello everyone this is Cole from the American Journal of healthc care strategy joined by a very special guest from a unique organization Matthew Matthew please introduce yourself and your current role here and uh thanks for having me Cole I'm Matthew Laskowski CEO and co-founder of Toby we are a diagnostic startup named after Sherlock Holmes's Blood Hound my background is in data science I have a masters of Science in computer science where I focused in Ai and ml moved to London worked in the fintech scene for a little bit um also SAS companies backed by SoftBank in the startup scene and now I'm in [Music] healthcare thank you so much for coming on it's a great name of a company as well and you have really impressive kind of organ uh education and um you know a history of of work here one thing I want to ask real quick the Masters of Science uh in computer science you said was in Ai and ml when you finished that in 2018 was that ahead of its time still did you feel like and also how hard is it to keep up with everything that's coming out well I will say that I think I am completely behind everything right now um what was really interesting in that time is the first elective you took in at that you know in that time was actually the state-of-the-art and there was only one class there was no secondary elective that went after that so I was at the state-of-the-art but you know this this industry moves so fast it is hard right now to keep up with all of it absolutely and that's one of the the key points as well is with that background at the time you probably could have gone and gotten a position at a company like open AI or Nvidia and made you know all this money in terms of you know your your stockholding and all this but instead you went into kind of a very different area uh which is Healthcare and Healthcare sometimes struggles with AI adoption we're not always the fastest to it so what motivated you and what gave you this idea to found this company yeah so I've actually been following the space for about a decade maybe a little bit longer um it's a space where people have used animals very successfully to smell various diseases um it's not only dogs as people kind of know about but there's also um wasp and bees actually DARPA did some really interesting work um post 911 where they legitimately had bees in what kind of looked like a handheld canister to look for explosive devices and they train them to stick their tongue out like they were you know looking in a in a flower for for the nectar um and when enough uh bees stuck their tongue out an explo an explosive was there there's also work with giant pouch um giant African pouch uh rats and my interest was reignited a few years ago when they started getting ants to smell cancer and what we've learned is what we can do is we can replace an an animal's nose with a instrument that's much more sensitive and in animal's brain with machine learning or artificial intelligence and I went through a personal health story where it took five six years to find a diagnosis and during that time I very much got reabsorbed in the literature and no one had really not no one had really no one had actually launched a product in the space with this underlying technology I personally think it's the most underleveraged technology in healthcare and after calling and emailing and Linkedin people asking why no one had put a product in this space out yet I realized there was really no good reason it was mainly an execution Gap wow that's so what is execution Gap what does that mean they just haven't taken the knowledge and actually executed on it yeah there there's really a few main pitfalls that these companies have um have have made um two of them I'm kind to keep secret and proprietary because we don't we don't want too many people following our path but basically just the right team wasn't assembled yet it was very very um academic uh it was very heavily academic and I think they just had the wrong business model and strategy in place so it was really just putting out putting a number of pieces that were already out there together in the right way I think we've now found our path forward that is really cool yeah nice and of course you would think initially right you need a large academic team but sometimes right the Academic Teams don't always translate into actual products right away and so that's kind of where your team is is unique because you have people who are experienced in these exact laboratory tests right yeah absolutely so my co-founder um Dragon sanovich he has been part of three successful diagnostic exits he was the second scientist actually second employee in first scientist at Garden Health now one of the leading companies in in blood biopsies and before starting the company we actually not only researched but we interviewed about 8 to 10 Founders in the space and it's not that no one has done this before it's that 's been successful and one of the big pitfalls again is people get bill money from from a ground like the bill and middle IND The Gates Foundation they have really good results for about a year and then that grant money Runs Out they've never even tried to transition to a revenue generating business model and they go back to Academia so really it's about assembling the right team including our other co-founder who's been part of startups for decades and had many successful exits and it's just about creating the technology that's right for commercialization which might have a few small technical drawbacks but overall in order to bring the technology to the masses it's the right call yep that makes a lot of sense and so you're you're in this space where companies have failed one of the reasons like you said it's about that team you know the science is there the product or the you know underlying idea works really well but the actual implementation of those products in the commercial space had not really been successful before you guys um and that kind of asks the question where do you think this is is going to go what is that that goal for you do you want this to be in every single institution uh I mean what does that look like yeah that's that's a great question so the goal and there's I think a few different axises here we can talk about um right now we have a two cancer test again this is analytically validated it's at the Prototype stage we're not in market yet um but we have a two cancer test for prostate and kidney cancer and in the next few weeks we will have uh completed that Urologic Trifecta and added bladder cancer we want to create a say 10 or multi-cancer test along with a few other neurological diseases where there's a lot of research indicating that this works someone just has to bring it to Market and I do want to add that our 10 cancer test is going to cost us we might charge people differently but it's going to cost us the same as our one cancer test so we want to be able to screen for a whole bunch of tests and screening is not the same as Diagnostics and it's not the same as prognostics and we just want to act as the central data science platform so there's all these hospitals in the world with the technology that we need the instrument it's very standard MPC like when you get a drug test for cocaine it's that exact same instrument we just want to act as the centralized data science platform whereby hospitals can run the tests send the data to us centrally we analyze it and send back results so can you explain real quick why is it costing the same for 13 or or I think he was it 13 or 11 as it is for one why is it scalable that way because our Innovations on the data science side so um basically what we do is is we run Mass spectrometry G gas chomatography mass spectrometry and we generate a whole bunch of data we generate 3.5 million data sets and gener normally today what you do you do is you take all that data and in that there's hundreds and hundreds of biomarkers you throw everything away except the biomarker for what you're using usually today it's for Toxicology so again I'm going to use the cocaine example and all this other data is just not us used so what we do is we take all that data and we have different let's call them flavors of algorithms or different pattern recognition algorithms so we've already created ours for prostate cancer we've also created it for kidney cancer and we promised our preed investors we could develop a screening test for bladder cancer which is very very aggressive for under $100,000 in under 90 days and we're very much coming under both of those targets so we just run our separate algorithms for the cancers to see if our test pops for that other cancer we also know that our prostate and kidney cancer tests are highly differentiated and we can we can basically parse those apart the prostate and kidney are probably functionally and physically the two closest organs so arguably and this is what we're trying to prove our hypothesis is that kidney and say lung cancer are even more separated so maybe it's not no cost maybe it's an extra nth of a scent for the electricity to run those other algorithms but it's it's fairly trivial that's what I was wondering is what the the compute cost would be on the extra algorithms but it it's not as expensive as let's say you know some of the new algorithms coming out we just read about you know 01 right and uh you know we're limited to to 30 messages per week unless we start paying that two grand a month um so it's not as complex because it's it's just a smaller amount of of data that is looking for right yeah um like in genetics uh you will deal with terabytes of data per person we we're dealing with multiple orders of magnitude lower than that you can run the inference basically the predictions on your laptop you could probably even run it on a Raspberry Pi pretty easily I think there was a hypothesis one to two decades ago that your genome was significantly affected with diseases such as cancer I think we now have one to two decades of data proving that the signal of cancers in genetics is actually quite weak um and it's very expensive to get it at least our initial findings are showing that the signal is very easy to find and it's very strong and we don't need to generate a ton of data to be able to predict if someone does or doesn't have cancer okay so that that's interesting so instead of looking at sequencing some or you know going through terabytes of data you've you're only going through this little tiny bit and and that's through mass spectrometry is what you what you said um of course as the audience knows and probably can tell I am not an expert on laboratory tests um which kind of brings us to the next question is you you know can you share some recent achievements you know we we read about uh in doing the research for this episode I read about the association of Diagnostic and laboratory medicine competition um which kind of signal to me that you guys were were legit right so can you tell us abouts that you've done and um and also why you're getting recognition there's thousands of products every year that are coming out right so what are you guys doing that's that's special that's getting this recognition yeah so first I'll I'll go through the kind of two recent Awards we've won uh there was actually one um a few days ago as well um and then also why why we're unique so first uh we won the 42 plus one competition so adlm the association of Diagnostic and laboratory medicine is the largest um IND event in our industry there's about 10 20,000 people at the event um they rented out the entire mccormic Center in Chicago and we won first place out of about a hundred companies and it comes with about a million dollars of financing it's dilutive financing it's not prize money and and some of the best judges in all of Diagnostics were there uh separately actually just um late last week we won the Cambridge Innovation uh the Cambridge Innovation Center Public Health Grant in order to develop our software because they see the societal good and I think the reason we're winning these is because we have a few main advantages over the competition uh first we are an easy urine test and not a more invasive of blood test I know the the new term guess it's not so new but is a blood biopsy a lot of people still have issues with blood and needles second it's affordable um it's going to be a sub 50 cost at least to us I don't know again what we're going to charge but the real only difference we have versus a drug test just so you can think about price parity is they will run the instrument for 15 minutes we run ours for 40 minutes and we can probably bring that 40 minute mark down so as far as the costs involved it should be priced parody with a standard drug test like you do before a new job also got world class accuracy at least our initial results we're getting kind of near perfect differentiation between Cancer samples and healthy samples again this can always follow aart in future studies but at least our initial studies we're getting extremely powerful results area under the curves of plus .95 it's also as I said a platform technology just to your previous question around price um or cost or our price per indication do not increase to be fair we might lose some um accuracy with some kind of confounding diseases but our internal price will not increase and patent pending we uh we actually do have the first broad patent in the space that covers the family of analytical chemistry so so that's very different right especially what you said about it being that close in cost to do these tests as it would be to a standard drug test one of the issues that we had found with other U organizations doing different types of tests but very similar in terms of the overall idea um is that the costs end up getting staggering right so now you know you're not just having to charge somebody for the R&D so that everybody can can make their money back but you're then going to have to charge $400 on top of that for this kit or this device or whatever it is but what you're saying is not only do you not have to charge for any of these devices because institutions are already utilizing this technology the most part um the base level technology from what I understand but your cost on your end is just as low as you know widely accepted insurance tests like a drug test yeah absolutely so the incremental R&D for our bladder cancer study is coming in at $86,000 I think $4 like $113 somewhere somewhere in that range which really is also going to allow us to attack the longtail diseases that normally are cost prohibitive but on top of that you know if you think about the past decade of um investment into Diagnostics everything's gone into sequencing they can make money in two ways they can make money in patient selection for big Pharma where they push people to the right buckets or selling data to Big Pharma so you even look at these huge sequencing companies and they've done 30,000 tests a year after being in business for eight nine years and they actually cannot be in the Diagnostics uh business they they they don't want to be so really it's not a viable solution um for for these sequencing companies to be in in um in screening whereas for us we have an extremely lowcost platform we generate a ton of data cheaply it may not be as precise but with data science and just with a volume of data a lot of that cancels out and so you know we have a very unique um commercial proposition here well so I'm I'm interested how did you get your costs for R&D so low uh how how long did the R&D if you don't mind sharing that yeah so specifically with bladder cancer again we said we could finish it in a 100 days in under sorry under 90 days and under $100,000 we're coming in at about $86,000 uh for this validation and I want to say we're not done yet but maybe 79 to 84 84 days we're cutting we're cutting it a little bit close and just from an R&D perspective there's almost no line items to think of normally in Diagnostics you have to generate a new piece of hardware and that takes years and tens of millions of dollars for us the hardware and the wet lab side have already been figured out we've just pieced them together in interesting ways we need money to purchase urine and that's on the order of in some cases tens of dollars and some cases hundreds um probably the highest would be $1,500 a sample about $50 to analyze the sample on an instrument again we're using retail pricing there and then the data science um which is maybe a few thousand dollars and maybe $1,000 doar for shipping so you're really not talking from an R&D perspective about anything that's expensive and those costs are coming down as we do you know work with kind of more volume discounted pricing uh for purchasing our bio samples uh the R&D is quite negligible to our overall business model that's really impressive because you know a lot of these startups but even established R&D is the largest thing that you're going to see on their their balance sheets right um so that that's impressive and and it kind of goes into the next question um a lot of Institutions I've spoken with are wary of of working with startups for many reasons one of the reasons um is because they don't want to adopt this platform and then you go away right you you become part of a larger conglomerate and then all of a sudden they're beholden to this larger conglomerate is your intention to kind of grow the company and to expand and expand or is it to be kind of acquired uh um you know what are your thoughts on that it's a great question um ultimately we want to bring this to as many people as possible and I maybe am arrogant but I don't think I'm so arrogant that I think I can bring this to the entire world and at a certain point I mean the goal for most startups is to get acquired I I perfectly accept that but we do want to get it to a place where it has legs of its own and people can can keep using it you know I think the beauty here is is a lot of hospitals have the instrument and even if you want want to buy the instrument people hear massp and what they think of is the $800,000 million piece of equipment that someone uses for their PHD to just do extremely precise work we are using what's known as a nominal Mass instrument or single quadruple nominal being a very the operative word it's the cheap one it's $160,000 with all the bells and whistles it's not extremely sensitive and because we are just acting as a software platform we don't really have our hooks into these organiz a like other companies we just have to do the data processing so you know whether we run ourselves or we get acquired that data science portion should always be very easily available for us to do or even for them to bring in house if eventually it does come to that I appreciate the honest answer right because you could have just said no we'll never you know but you said is honest right you your your purpose and your mission are gonna of course continue on and come first it'll be available to everyone but I also that's an important part right you're not going to have this proprietary you know like epic computer right you know like all of your systems are on this you know this epic software computer something like that if you leave it you have to throw them in the trash and it's not that way at all um so I think that's a really important point to to a lot of these institutions um and that kind of goes into one of the other questions here which is um you know talking about kind of this this Market potential this market demand why why is there a demand for this in in the current institutions why do people want to invest why do hospitals want to be a part of this um what's the reason if you can just fill us maybe like the the environment fill us in on the current business environment if that makes sense yeah that that makes perfect sense um so we are targeting a few different market segments um hospitals and insurance I think Pharma right now is more opportunistic in terms of a companion diagnostic but hospitals if you think about it Specialty Care and especially cancer Specialty Care is one of the last remaining revenue and profit engines in hospitals and with our test you know today it's it's quite difficult to make a business case to hospitals just on savings right and with us we could remove a ton of biopsies and and necessary specialty care for for prostate cancer but really by doing a yearly screening test and finding people with ladder cancer for the first time with kidney cancer early with prostate cancer and eventually more diseases we can really funnel them into those cancer centers in order to generate revenue and the flip side of that coin on the insurance side um when you find cancer early and you know it's much it's always much cheaper to do preventative care than acute care and so I think that business case is pretty clear but for the hospitals you know there's a huge Revenue upside for them here in order to drive that specialty care I think a bladder cancer patient will spend $90,000 of incremental Health Care spend and in their first year and then between1 and a half to $2,000 a month thereafter so even if the test is a complete wash or even maybe at a slight loss and potentially even for free there's a very strong business case to be made here that makes a lot of sense and and because of your low cost it's even more reinforced right because you're not you know like you said we're not we're not risking this huge sum of money for negative tests uh or for a positive test that only makes you know $10,000 I mean these are hundreds of thousand dollars in losses as well as lost lives right I mean there is a potential for life saving here as well right yeah and and you know that's definitely our our primary goal uh you know when you when you screen early you detect early and you have improved outcomes across the board and you know if there are any CFOs listening to this um there's just a ton of knock on Downstream effects right when when you find these These Things Early cost savings live savings um and and so on and so forth we we do have some quite unique data and this is where I think our test could be quite revolutionary we are finding that we can detect early stage cancer now that's not the same as detecting cancer early early for that you need a longitudinal or a prospective study it's a different type of study but we're finding we can diagnose or screen for early stage cancer just as well as late stage we do have a separate issue where we're actually not great at differentiating between early and late stage but the competitors cannot screen for early stage cancer so you know we're going to take that win with the loss as well so we are a screening technology we're not a diagnostic technology where you know the stage and we're not a prog prognostic technology and so why is that important to to distinguish those things for potential hospitals who are looking to to get involved with your organization yeah um it's where we sit in the workflow so there's a lot of very strong and good incumbent tests for Diagnostics and they're accurate but they're also extremely expensive let's take lung cancer screening as as a very clear example you need a lowd dose CT scan to screen for lung cancer the highest estimates put um at 4% adoption every other year lower estimates would I'd say more fair estimates might even be lower than that but it's not like you're going to be able to test every American or every smoker in the US in a lowd dose CT scan a year I think that's pretty unreasonable just from a cost um friction standpoint but you can you already do collect urine every year when someone comes in for their you know yearly health check say you already you collect 50 mil Ms of urine we just need to siphon off between three and five of those Mills and we can screen for a number of diseases we're by no means going to be perfect but we believe that our current accuracy makes a very compelling case where we can then take the highest risk individuals we find and put them on better treatment paths what does the actual workflow in the clinic look like if we can just kind of walk us through start to finish um yeah especially for our physician leaders listening you know they love probably the idea of the technology but some of them might really worry is this gonna add this huge complex computer workflow for my staff yeah no it's a it's a very very good question so let let me just first differentiate uh how we change the standard of tear so we take something like bladder kidney cancer today it's really only found mid to late stage the porcelain of your toilet is quite quite white and what will happen is a patient will start um will have blood under their urine and it will be very strong red contrast and then you will go into the doctor and then you will get a diagnostic test that says yes you do in fact have ladder cancer and it's and it's Advanced with our test what would happen is you would go in for your yearly Health visit you're hopefully already collecting um sorry your your yearly Health visit you're already collecting hopefully urine that urine is already sent to the lab put on a number of different instruments or or workflows or processes and we just need to siphon three of those Mills off maybe five at the high end uh to the instrument that hopefully these organizations already have or they would purchase a new one for not a $3 million MRI that's used to screen but a $160,000 instrument the data would go through their lym system or their EHR directly to us the data science is quite simple it gets sent back and the patient can know within 48 Hours um or some very compressed time frame if they are flagged or not flagged for cancer that point then they would go to the existing Diagnostic and prognostic workflows so we sit on top of all the existing work uh existing Diagnostic workflows in order to find people early but then once we flag them for specific cancer they go to those more accurate but also more expensive tests that they would never do yearly it's just too cost prohibitive so it's like in a way like a like these at home tests for some cancers um they do the test it says you might possibly have cancer you're you're high risk right based on the results you need to go in for the full examination and get kind of a full rundown to diagnose exactly what's going on yes absolutely um and you can definitely use those to drive patient volumes at hospitals one unique thing about our technology and probably a lot of leaders listening to this are thinking of genetics uh where you know basically the the material you're looking for is extremely fragile voc's and urine are incredibly stable there's really no issues around shipping Etc now we actually don't have a any short or medium-term plans to go direct direct to Consumer um it's Capital intensive and direct to Consumer is not performed very well in the past decade or maybe five to seven years so we don't have that right now our goal is really to get the technology into the clinic so they can use it through their basically their system or maybe do some mailers around their area in order to generate patient volume um and revenue and help a lot of people in the process yeah I appreciate that as well because it makes you I think for some people a more solid physician partner right because you're not putting all of your your eggs in in the marketing towards consumer basket right I mean you're working with hospitals and Physicians and institutions and I love that workflow um last question because I know really nothing about um these these machines the $160,000 machine is it used for for many other things as well or is it because you know the value preposition for hospital is to buy one of these machines from you know wherever they get it yeah yeah that that's a great question so normally these machines sit idle most of the time and you know how a lot of CFOs will rightfully so complain about the utility and utilization rates of their instruments especially for drug testing machines right so what happens for a drug testing machine is you never want to tell your employees when that you're doing a mass drug test and but you need to be able to uh work through that Peak demand and so everyone will get surprised right it might happen over a few weeks or you know however long but then for the next few months the machine will sit idle so you know as long as you can use that unused capacity you could use it for for our test I think what's probably going to happen with our first few Partnerships and sales and anyone want you know listening here wants to reach out to us in order to be those individuals we will probably use their initial instruments although we do have have a preferred instrument that we think is Mo is optimized for our workflow but at a certain point our instrument will basically have quote unquote preloaded algorithms where we have a three or a five cancer test that's been clinically validated in which case I think you know anyone should want to use our instrument and not their existing one because of just the time to Market and time to Value but we can always keep that as an open point of conversation okay so there is a physical instrument that you will have your software on it as a as an option we won't have our software on it all we need is the data from those instruments sent to our algorithm and so we can work across many instruments you know if if we think about like the longterm highing the sky sky 20 years from now what our goal is is we just want to be that Central platform and even outside of the US and in countries where they can afford new instruments and but they do have these math spe devices and and they all do um they're just sending us the data and we might have to calibrate it to the specific manufacturer or the specific model um but you know there there are there is a lot of um crossover and basically pre-trained models that that can be had between those different instruments okay and it's just that you have a preferred instrument where for many reasons probably I'm assuming was kind of the best option for this kind of thing it's only 160 Grand from what I'm I'm grasping cor correct the the full setup is less than $200,000 we think it's the most optimized if someone wants to do this at volume but there are plenty of other technologies that are very good just not um what I would say is the most optimized process you can run but there's no reason someone could not use their existing instrument it just might require a bit more manual work or really the manual work they're already doing yeah and and that's great though because like you said so many already have it and it's it can really I think one of the things two people think of is well um you know the only drug tests you know the only spectrometers are in urban areas let's say they're not in the rurals but when you kind of reduce that that workflow of these tests in the city it also can expand to the the areas throughout the US right I mean how how many how much of a of a savings or a reduction in work are we expecting from this just so I can get a number around the savings yeah it's it's really it's really going to vary so I don't even think we should think about it in terms of savings I think it should be thought about in terms of access right so so say you want a mamogram there are some areas in the US where it's really prohibitive like you would have to drive 300 miles to to find the nearest unit there's also not enough mamogramas and so you know those salaries they're asking for are rightfully very high um but you know these mobile Vans are really the only option and those are a great option but you're not going to be able to test in most urban areas at volume across the entire us really the only option you have have is to find a local Health Hub and send out mailers so you know I think a lot of savings can be had but the bigger opportunity here is giving people access to healthcare who before never would have had and never would have gone in for their normal screening Tech uh the normal screenings they should have yeah no I I agree so so this actually could impact population Health this could impact Public Health uh on a wider scale um so really really cool technology thank you so much for coming on and sharing this Matthew excited to see what happens uh and I'm also just impressed because I've spoken with so many startup Founders you know the funding that you guys have had has been much lower compared to so many of the other organizations and the product is so uh well done right so uh I'm really impressed and thank you so much for your time thank you thank you for having me</p>
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