And so — and there’s a very strong movement in this direction to kind of get people to kind of align their knees — we call it personalized alignment. And so there’s a huge opportunity for technology to make it more possible to get there, to get to those targets. The current technology platforms that are out there, they really weren’t designed for that. They were designed to do a better job of kind of giving everybody that straight knee and they do that. But the problem is that it hasn’t — giving everybody that perfectly straight knee and hitting that target every time, it hasn’t actually really moved the needle on how well people do after a knee replacement. Even with robotic knees done with the kind of traditional alignment, the same 20% of people are still not super happy with their knees.
But if you use the right robot to kind of give people back their own anatomy, all of a sudden, there’s data coming out saying that they actually do much, much better. And you have 95% plus very, very happy with their knees. So, we think that there’s — we’re designing a system that really is looking at where the puck is going, as Ben likes to say, and that is at personalized alignment. So, we’re designing into it the ability to resurface the joint to restore their patient’s anatomy. It’s one of the reasons we think having a CAT scan is important because you’re trying to resurface their knee. And without that CAT scan, there are a lot of systems out there that are imageless that don’t use a CAT scan and we think a CAT scan is important as we move more toward personalized alignment.
So, — anything else you want to say about that, Ben?
Benjamin Sexson: I think you nailed it, Doug. I’m seeing a couple of questions that relate to — are we basically downgrading our product to appeal in the FDA, I would say, definitively no, right? In anything in life, in any engineering or there’s trade-offs, right? So, the benefits of a fully active system, we think I articulated those, there’s also benefits to the current state-of-the-art. And so what we envision is a system that can do both. So, we want a system that could be active when it needs to be active and its most efficient to be active. And we want a system that has other modalities when it suits the surgeon for the other modalities to be working. We do not believe everything we do at Monogram is for basically the benefit of the patient. If we didn’t believe that we had a compelling product, I don’t think we would be here. I mean, Doug, you used it on this weekend. Do you think it’s — without saying too much, do you think it’s a downgrade?
Doug Unis: No, because what we’ve done is we’ve kept the safety features. We’ve kept the kind of the pluses of the active system. We’ve kept all those safety features. The hand-guided a bit of it just allows you to go faster, which is ultimately a premium. And as Ben was saying, we — there are some things that you want to be active. For example, our ultimate vision for the company is — and the reason it’s called Monogram is that we ultimately think that pairing a robot with custom 3D-printed implants is where things are going. And custom milling for example, where you swap a saw out for essentially a drem [ph] or a bur, for custom milling, we think active is a much better way to go for that part of it. So, we want to keep the active fit for that type of thing. But for cutting with saw, hand guiding definitely helps
Benjamin Sexson: Yes. If hand guiding is the way we go. Is semi-active, just kind of broader. We have a different embodiment that we think enhances the equivalent, but we think we ultimately will have a more robust product. And just kind of to recap the critical design features of our product and this is why we’re here. So, when you look at the current kind of state-of-the-art, right, every system basically follows these workflows. And maybe, Kamran, you can talk a little bit about the — our novel navigation that you’ve developed?
Kamran Shamaei: Specifically about the — next Gen or the current one?
Benjamin Sexson: mVision.
Kamran Shamaei: Okay. Yes. So, one of the major steps in joint replacement is basically called registration. Registration is a process that allows the robot to know where the patient’s anatomy are that means where the bones are, you name it. So, currently, the — all the robots in the market, they use a technology called optical tracking. And what that means is that we place a series of markers on the bone or the anatomy that we want to track. And then we run some algorithms to register the bone. We basically tell the bone where the bone is and then from that point on we track those markers on the bone and then that’s how we know where the anatomy of the patient, all the bones and all the different tissues are during the procedure.
This part of the workflow takes time. There is — we need time to expose the bone. We need to mount those markers on the bone, and then we need to register those — the patient anatomy to their markers and so on and so forth. And these procedures entire, all these parts of the workflow could take around 15 minutes with installation of the markers all the way through the actual data collection and registration. It could go between 15 to 20 minutes depending on the system. So, the technology that we have developed here and we’re now productizing it, basically removes that step. And in a matter of a couple of seconds, we basically register and start tracking the bone. And so what that means is that 15 minutes-ish of the procedure — 15 to 20 minutes of the — I mean based on the data you see here is more than that.