Video Transcription
Kei Wieland Müller 00:00
I remember how hard it was to breathe through one of those face masks. Now imagine this discomfort exacerbated to the point where you can't breathe at all anymore, and that is what 500 million patients in the world suffer through, because they suffer from severe lung diseases on a broad spectrum of different indications. They're responsible for hundreds of billions of healthcare costs every year, and we have developed technology that can improve treatment and outcomes in a variety of indications. And we'll start with acute complications, because they're common, lethal, and very costly, meaning that there is a medical need combined with a huge economic need. Complications like ALI and ARDS cause up to 30% of the total ICU costs in any hospital. They express themselves through a damaged lung that makes a patient unable to breathe and puts them on a mechanical ventilator for an average of 16 days. We're speaking about 13 million patients per year that suffer from an acute complication in the ICU, and 9 million patients are either dead or have some sort of chronic disease after four to six weeks of that complication. In our first markets, there are 3 million cases in the US, Europe, and Japan, and they are associated with $46 billion in healthcare costs every year. Now, why is the situation so dire? It is because ventilating damaged lungs is like flying blind, so your task as a physician or respiratory therapist is to oxygenate the lung, but at the same time, you need to avoid lung damage through overextension. Now, what's simply put into one sentence is key to reducing time on the ventilator, mortality, and chronic disease afterwards. The problem, however, is that while you can monitor a lot of things in the body, you have absolutely no clue what the mechanical ventilator does to the lung in a mechanical sense. That leads to trial and error through the treating physician, adds to lung injury, and increases mortality and morbidity, because in the end, unfortunately, the lung, even to the best physician, is a black box. That is why KOLs have unanimously been calling for what they call local lung mechanics, which they need as data to improve outcomes. So Ebenbuild has developed a digital twin platform technology to open that black box. We create digital twins of each single patient that produce data which is outcome-critical. So what you see here is the distribution of the local strain simulated on a computational model. And this is the single, albeit artificial, data source that links what is being set on the ventilator and how the lung reacts to it. We've just published accuracy and validation results in that and know that it is 95% or more accurate with respect to any lung that we look at. This digital twin will be at the core of a bedside AI-enabled medical service, which uses a routinely taken medical image and extracts anatomy and pathologies of the particular lung using AI and machine learning. It generates the airway tree beyond the image resolution and adds information on the material properties of the tissue. Now your digital twin of that patient is finished and done within minutes, and what you can now do is actually sample a broad space of different therapies, their perturbations, and basically distill and condense a sort of a safe zone for each single patient with regards to their specific need and the lung to be ventilated optimally. We can do this within one hour. This transformative information is absolutely compatible with current routine. You have initial settings that get set up, images being taken, the diagnosis of the acute complications based on those two components, and then the trial and error starts. And you must imagine this is three and a half hours of trial and error per patient, and if you do things well as a therapist, then you can wean the patient off the ventilator and extubate him or her. And where we come in is at the earliest possible point, meaning that what we do is we personalize the tidal volume, meaning the air going into the patient, based, for the first time, on true morphology data and local mechanics data of that lung, through the digital twin model. That way we will be able to reduce the number of adjustments per patient and ventilation day, thereby wean earlier, because the lung can recover faster and extubate earlier. And that's a key point, because earlier extubation leads to better outcomes if you protect—and that's our claim—if you protect the lung from day one of ventilation through objective, reproducible technology per patient, then you will be able to go towards enabling uniformly good outcomes anywhere. Because if you look at the statistics, it depends on where you end up having that complication. If you are at a good hospital, it's 40% mortality. If you happen to be elsewhere, it's 60%, right? And our claims have been shown to be well doable in expert centers, where they showed that ventilation time could be reduced, on average, by two days already, that protective settings—so good settings for the mechanical ventilator on day one—reduced mortality, and overall, they could show that by a slightly smarter, more informed trial and error, they could reduce mortality by 9%. So this is no crazy claim, but this is well possible if you have a better understanding of lung morphology and actually know how much air goes into the lung. So through that, we get stakeholders aligned, because our value proposition is better care at lower cost by enabling less lung damage that's been dealt to the patient, and they recover faster because therapists and physicians are able to treat more effectively. And these strong components combined will incur that there is less cost per patient for providers, and average providers can even retain patients longer, meaning that they can increase their income based on the patient journey that stays and the patient stays longer with them, leading to a reduction in cost per patient of $15,000 per patient in the US. Now these $15,000 is what we enable through aerogram as a cost savings, while the income via DRG stays the same. So it's the value that we generate, and we charge a fraction of that value as a price. We see it at $1,800 per patient on average, with a subscription or service fee per year. And if you look at the incidences of acute respiratory distress syndrome and other complications, this is a $6.5 billion opportunity in the US, Europe, and Japan as primary markets. This is the start. Now, where does it go from there? We will look at the acute complications first. But if you look at the next possible things that are doable with the technology platform, it is doing this every year, plotting trajectories of development of the disease, and then looking into the space of chronic respiratory diseases, and later on, even starting screening for example, high-risk groups that are at risk of getting chronic respiratory disease to stage by stage, step by step, increasing the TAM that we can address. If you look at the revenue projection that we have here, we are looking at roughly 5% market penetration with the first product, and they're driven by milestones, clearance, efficacy, and standard of care, each of which are associated with a customer archetype. When you look further and assume that we reach a similar amount of market penetration for the subsequent products, they add up to mark a revenue potential of over $2 billion. When we add the numbers up, we've raised $6 million in venture capital and public funding. So far, we have de-risked and validated the technology in two clinical studies. We are in constant exchange with the FDA. We have strong clinical KOL support in the US and Europe. Our internationally filed patent portfolio is growing, and we've shown the technology to have traction in contracts and partnerships with med tech and biotech already in the field of respiratory trials. We are raising $15 million to release an AI simulation platform, run the regulatory trials with them, expand our patent portfolio, and actually grow the CRD and silicon trials customer base with that. This company is based on 20 years of cutting-edge research at TU Munich. We have a benchmark for lung digital twins in that area. We're supported, for example, by Charlie Taylor, founder of Heartfull, creator of the most reimbursed AI healthcare product in the world, as well as very illustrious people from the medical community, which come from Cleveland Clinic, Stanford Medicine, Mass General, and other German KOLs and leading institutions. In the end, everyone needs to breathe. These are two children of my co-founder, Jonas; they were put on a mechanical ventilator because they were born prematurely, and they were lucky to be close to an expert center. So what we wanted to even build, initially and eventually, is that everyone gets expert treatment. And through the assistance by Ebenbuild, thank you. Thank you.
