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Fouad Noor Presents ThinkSono at LSI Europe '23

The company's ThinkSono AI platform uses artificial intelligence to guide and empower training with ultrasound at the point-of-care – allowing non-specialists to rapidly learn how to perform ultrasound examinations.
Speakers
Fouad Noor
Fouad Noor
CEO, ThinkSono

 

Transcription

Fouad Noor  0:05  
Hello, everyone, I'm pleased to be here. And I'll try to split to get to the point quite quickly. So what we've built is the world's first software for detection of deep vein thrombosis, which is a blood clot in the leg. A little bit of context, it's, it's the number one cause of preventable hospital death in the world. So, the blood clot leads to something called a pulmonary embolism, which can lead to death. More people die from this than AIDS, breast cancer and car accidents combined. So it's a huge problem. And the crux of the issue is the diagnosis of these patients, which is why we built our software. And the technology is quite simple. In a sense, of course, it took us almost seven years to build it, but it's quite simple. In principle, which is it's an app you download it onto your phone, and with one of the already commercially available handheld ultrasound devices. A non specialist such as a nurse or a doctor can perform a compression ultrasound to detect the vein thrombosis, we hope to the same accuracy as a radiologist, the data can be sent for radiology confirmation remotely, which simplifies the clinical pathway, which I'll go into a second. And so here you see another image of a doctor performing a compression ultrasound scan, guided by our software to perform a diagnosis. And there's also the remote radiology review, we've done quite a lot of work already. In fact, we've scanned more than 600 patients across the UK, Germany and Greece, you can see some of the Logos there of the NHS Trusts we're working on. So we've done multicenter double blinded clinical trials. In fact, as far as we know, they are the first in the world in terms of comparing an AI guided ultrasound for DVT detection to standard of care. Some of the papers we've already published, including a nature shows very strong sensitivity, specificity and health economics, again, demonstrating the use of our technology and clinical practice would be extremely cost effective and helpful. Finally, we've already partnered with obviously, multiple hospitals, we've also have ultrasound vendors that we've partnered with using you can see clarius, one of the prominent handheld ultrasound vendors in the world. This is part of our commercial model as well. We're ISO 13485. And we're getting ready for FDA and class IIB CE clearance right now. In fact, we're starting our FDA pivotal clinical studies, hopefully this year. And I want to show you practically how the technology works. If I'm very lucky, this video will start I didn't test this, so let's find out. Okay, it does. So here you can see the arteries and red veins in green. And in real time, for the first time, you're able to scan a suspected DVT patients be guided by the software and perform what's called compression. So you physically push on the vein to look for a thrombus. That's the gold standard diagnosis of DVT. With ultrasound, you can see the software automatically analyzes the compression gives feedback to the user, the user agrees, and then the process moves on. Here's an example of an actual clot, you see the vein in green doesn't compress and hear the data goes up to the cloud for review and confirmation. So it's very, very quick, you can do it in about five to 15 minutes. And clearly that's going to transform patient care. The reason this matters a lot, not just obviously saving the life of the patient, which we shouldn't take for granted, also, because the current clinical pathway is broken, it's it's it takes a long time to get diagnosed, patient goes from suspicion to refer to the emergency department, they get risk assessed, they have to do a blood test, then they have to wait a few hours. If you're in the NHS, you have to wait many, many hours indeed. And then you get an ultrasound scan, which is essentially the scan that I just showed you guys. And then you get a diagnosis at the end. So we thought well, if the if the non expert, the nurse, for example, or even the GP can do the scan, well that transforms the entire clinical pathway, you can skip a bunch of these steps. And so therefore, that's exactly what we built. And that's what we publish our health economics on. So that the scan can take five to 15 minutes instead of done by any nurse or doctor can reduce radiology admissions by up to 90%. The reason is 90% of patients are negative anyway, the blood test may not be required, and it's low cost. So really we're feeling at home at this point that this is a better way of managing DVT patients. And we've raised a little bit more than I just said here because I didn't update this slide but it's about $5.6 million to date, but that's okay. And then obviously we've we've already published peer reviewed papers etc. And the next step of the company is to raise another funding round which I'm happy to talk to those of you that are interested after this call after this talk, and then we will be starting our FDA pivotal studies, hopefully this year to get clearance. And we should also be getting our CE class II clearance either at the end of this year or early next year. And this is quite a big market. In fact, it's an entirely new category of ultrasound software. So we estimate that to be about $12.5 billion a year, it can even go higher than that that's actually a fairly conservative estimate. We believe that the ultrasound AI market is larger than the entire hardware market itself. And when you think a little bit more about it, it's accessing a different pool of money. That's why it's it's larger, you're accessing diagnostics instead of just sending purchasing equipment, which is why it's so much larger. That tends to be the case in software anyway. And so a little bit about the leadership. So I'm an electronic engineer by background my co founder is a hardware engineer. We have machine learning professors like Bernard coins at Imperial College London, Professor Nicola curry consultant haematologist is running and supporting our studies in the UK. Professor Evergrande nose is a vascular surgeon at UPMC. And he's supporting us with the US clinical studies as well as our CMO, Professor Mike Blivus, who is the one of the world's prominent point of care ultrasound experts. And that's pretty much it. I have two minutes. I'm happy to take questions. But I'm also happy to leave the stage with that. If there's no questions. Go ahead. No, okay. All right. Well, thank you so much.

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