The future of robotic surgery
Guest Renee Zhao works at the cutting-edge of robotic surgery – literally.
Emboldened by advances in 3D-printing and miniaturization, she builds “millibots,” magnet-controlled, millimeter-scale soft robots that navigate the bloodstream to remove blood clots and treat brain aneurysms. While the millibot’s promise is clear, much work remains before the devices are commonplace. Revolutionizing health care with surgical robots will require a delicate balance of design, buildability, and functionality, Zhao tells host Russ Altman on this episode of Stanford Engineering’s The Future of Everything podcast.
Transcript
[00:00:00] Russ Altman: This is Stanford Engineering's The Future of Everything, and I'm your host, Russ Altman. As we start the new year, I thought it would be good to revisit the original intent of this show. In 2017, when we started, we wanted to create a forum to dive into and discuss the motivations and the research that my colleagues do across the campus in science, technology, engineering, medicine, and other topics.
[00:00:23] Stanford University and all universities, for the most part, have a long history of doing important work that impacts the world. And it's a joy to share with you how this work is motivated by humans who are working hard to create a better future for everybody. In that spirit, I hope you will walk away from every episode with a deeper understanding of the work that's in progress here, and that you'll share it with your friends, family, neighbors, co workers as well.
[00:00:48] Renee Zhao: The robot itself is highly multifunctional, and first of all, we talk about the swimming capability. If it can swim, that's great, but it's like a toy, right? Swims in a blood vessel. Yeah, of course, it will be a lot of fun, but we need it to be able to treat diseases. So first of all, it can very easily deliver, because it's a hollow structure. It can actually carry drugs, and then, uh, diffuse drugs. to a specific site so that we have a very high concentration drug to be delivered to the site. For example, if we're treating blood clot, we can deliver a clot dissolving chemical.
[00:01:27] Russ Altman: Right.
[00:01:27] Renee Zhao: So it can dissolve the blood clot.
[00:01:35] Russ Altman: This is Stanford Engineering's The Future of Everything and I'm your host, Russ Altman. If you're enjoying the podcast, please follow it in whatever app you're listening to right now. That will guarantee that you never miss an episode and you're always clued in to The Future of Everything.
[00:01:50] Today, Renee Zhao will tell us that surgery is going to be very different in the future. It'll be done with soft robots that are tiny. It's the future of robotic surgery.
[00:02:02] Before we get started, remember to follow this podcast in the app that you're listening, so that you'll always be alerted to new episodes and you'll never miss the future of anything.
[00:02:17] So when you think about surgery, you usually think about a surgeon, you know, with their mask and their gown and a bunch of stainless steel tools, opening you up and doing stuff, hopefully, helpful stuff. But you know, even today there's increasingly robotic assisted surgery where surgeons are using robots to manipulate tiny spaces or difficult spaces. And they're controlling it almost like a video game where the robot does what they say, but they're kind of controlling it.
[00:02:48] Well, guess what? Now we're going to have to start thinking about a whole new type of surgery. This is a type of surgery with a millirobots, milli because they're at the millimeter scale. Millirobots will be able to enter your body and will be able to swim around in your bloodstream, find the target organs, and make the repairs. Is this fantasy? Absolutely not.
[00:03:11] In fact, Renee Zhao is a professor of mechanical engineering and material science and engineering at Stanford University, and she's an expert at soft, small robots. She's going to tell us that she's already built prototypes that can swim through the blood vessels in your brain, and if you've had a clot or a problem with your arteries or veins, she'll be able to fix them.
[00:03:34] Renee, you're an expert at mechanical engineering, material science, what draws you to robotic surgery and healthcare applications in general?
[00:03:43] Renee Zhao: That's a great question. So when I started my independent career, that was not actually not part of my research. When I started my career, my focus was always on, um, soft materials and mechanics of soft composites and soft robots. And for soft robotic systems if we think about the counterpart of robotic systems, really we're thinking about very rigid parts. And that's driven by, for example, I'm just using my arm as example. Um, most of the robotic systems that we have in mind are driven by motors controlling the degree of freedoms, right? So if I'm moving at my arm, uh, assuming this is a robotic arm, right? So here will be a motor controlling the rotational degree of freedom.
[00:04:24] Russ Altman: Right.
[00:04:25] Renee Zhao: So that's a hard the so called robotic system that are always driven by motors to control the number of degree of freedoms. Um, but, actually, when we think about in nature, for example, octopus, right? So their body is soft. They don't have bones and how the level of flexibility and the motion and movement they can achieve is really amazing. So the reason I was really interested in soft robotics is that we can naturally combine, um, smart materials that cannot respond to external stimulation. For example, stress or temperature change, pressure change, magnetic field, which is the most important and intensive research that we've been working on.
[00:05:11] So we can combine those materials with rationally designed structure to achieve different types of motion and movement. So everything actually started from, um, exploring different and soft robotic systems. And later on, we were thinking, okay, um, what if we can, uh, apply the soft robotic system to biomedical applications so that it's much more compatible with human body, which, because we have soft skins, we have very, very soft tissues.
[00:05:40] Russ Altman: Yes.
[00:05:40] Renee Zhao: So whether we can use soft systems to address problems in a soft body. So that was the inspiration and motivation of everything.
[00:05:49] Russ Altman: Great. So, um, to start out, maybe you can tell us what is the current status of robotic surgery? I know there are some systems that are deployed. I think they're what you would call the hard systems, not the soft systems. So what do you see as the current status? And most importantly, what challenges do they raise that you're specifically and your research group are trying to address?
[00:06:10] Renee Zhao: Yeah, that's an awesome question. So, um, robotic surgery right now is still largely focused on, uh, human using all types of different tools, like rigid tools, like robotic arms to control the navigation or operation in human body. And I think most recently the advances has been in the field of remote surgery. So for, um, patients that who can have, not have access to very skilled doctors. And if a doctor is like, um, in another country who is really, really skilled and who is an expert for certain types of procedure and surgery, and this person, this doctor can actually operate remotely on the patient.
[00:06:53] So that has been a very, very hot area and it will be a big change to the whole healthcare field because now we, um, so think about, I will give an example here because what we have been working on recently is developing a, um, new technology to treat, uh, blood clots in the body, uh, to be more specific, treating stroke.
[00:07:15] So stroke is a, um, is a disease. Physically, it requires immediate treatment. Otherwise, we have, the patient would have, um, millions of millions would die in a very short time if the patient is not being treated, uh, in a very short time. But for the surgeons who can, uh, who can basically practice this technology cause of thrombectomy, mechanical thrombectomy. So for, uh, patient who cannot be treated, um, by injecting, um, chemicals to dissolve the clots in the brain. And after four point five hours, mechanical thrombectomy will be used. So these type of technologies, it's essentially based on an interventional radiology. So using all very, very long catheters and guide wires that inserted from the legs or the arm and it goes all the way in the endovascular system.
[00:08:10] It goes all the way to the brain. And when it reaches the brain, a very skilled doctor is required to basically practice all these procedures. So it's how, well, it really, it takes like eight, ten years to train a doctor like this, highly skilled. And we don't have that many doctors. Even Stanford is actually the busiest site, um, in the West Coast. And we only have four to five doctors that can basically practice this type of surgery. So, that means that if we think about robotic surgery in the future, that people can be actually treated by robots. And by, so these robots can be trained. Nowadays, the state of the art is that the doctor will still need to practice on the patient remotely,
[00:09:04] Russ Altman: Yeah.
[00:09:04] Renee Zhao: Using tools like robotic arms. But in the future we can use because, of the recent, very recent, like um, like boost events of AI tools. And the AI can be used to train the robotic arm. So eventually we don't need doctor to practice on patient. So with the very highly advanced AI tools, the AI will train the robot and the robot, the right arm can directly practice on patients.
[00:09:32] Russ Altman: Okay, great. So, okay, so that's where we are. And that's a very specific vision. Um, but now I'm trying to combine the two things that you've told us this idea of robots doing surgery, but you also brought up the octopus and kind of the squishy robot. So what are these actually going to look like? And how are they going to actually be controlled? Because now I'm wondering, should I be thinking about an octopus that does surgery? Or is that a little crazy? So take me through a visualization of the future that you imagine for these soft materials and robotic surgeons.
[00:10:05] Renee Zhao: Yeah, of course. So when I mentioned this, uh, robotic arm inspired by octopus arm. So when we think about an animal, um, so this animal can actually move, can deform it's arm. And in the meantime, so this is a very interesting fact. We have our neurons in our brain, but for octopus, two thirds of its neurons are actually in their arms, not in their brain. So basically, they're a highly intelligent animal that has this, um, movement controlled by a flexible, uh, part and also the flexible part can in the meantime, think and make decisions.
[00:10:45] Russ Altman: Yes.
[00:10:45] Renee Zhao: So that's really the, where the inspiration is in, um, what we are, um, trying to develop here is to create a system that can respond to external stimuli. So I mentioned about the catheters at, as an example previously, and, uh, I can give you an example of, still the stroke for stroke treatment. Um, because so if you know, um, how the vasculature looks like in the brain, it's actually highly torturous, like the tree branches, it actually goes like this in the brain.
[00:11:19] When the doctors look at the vasculature, um, in the patient's brain. So they use the roadmap, basically how torturous the, um, blood vessel is to determine whether this patient can go through mechanical thrombectomy. Because eventually the catheters and the guide wires, they will need to be able to navigate in this highly torturous environment and then reach the disease.
[00:11:41] Russ Altman: Right. So they have to make all the same turns and stuff that the blood vessel makes. And that could be very tight turns.
[00:11:48] Renee Zhao: Yes. Those can be extremely challenging. And what we have been developing recently is to, um, have a robotic system. I am actually, uh, I don't know whether you can see it, but it's on my,
[00:11:59] Russ Altman: We're a podcast as well as a YouTube. So you have to describe it as best you can.
[00:12:04] Renee Zhao: All right. So it's a millimeter system. It's roughly a two millimeter. Um, in diameter. And so when we're talking about the vasculatures in the brain and we're talking about the vessel size that are three millimeter to five millimeter in diameter.
[00:12:22] Russ Altman: Okay. So this would fit in there.
[00:12:24] Renee Zhao: Right, right. Exactly. So recently we have this, um, uh, small robot developed, which is controlled by magnetic field. So essentially we're removing all the tethers and we're not using the guide wires, the catheters. Because, um, for the mechanical thrombectomy to work or for any type of, uh, any types of, uh, um, interventional radiology technologies to work they are all based on catheters. If we have catheters, the same challenges would, um, present to us, right? The navigation capability, the trackability of the catheters when it goes through torturous vessels. And now we have the system that is two millimeter big. And it can swim super fast in blood vessels.
[00:13:09] Russ Altman: Did you say swim?
[00:13:11] Renee Zhao: Yes, it can swim.
[00:13:12] Russ Altman: Okay.
[00:13:14] Renee Zhao: Basically, so, um, I can give you a number. It can very easily achieve a swimming speed of thirty centimeters per second.
[00:13:21] Russ Altman: Okay, so that's very rapid.
[00:13:23] Renee Zhao: Yeah.
[00:13:23] Russ Altman: Maybe even more rapid than blood flow, but definitely around the same rate, right?
[00:13:27] Renee Zhao: Yeah. It's designed to, um, be able to overcome blood flow
[00:13:33] Russ Altman: Okay.
[00:13:33] Renee Zhao: Because in, uh, for it to be able to navigate in blood vessels, we need to, um, have the robot to be able to swim upstream and downstream in a controllable manner.
[00:13:43] Russ Altman: So for those of us who can't see the picture of the item, you said it's two millimeters or so in diameter. How long is it? Is it like a little tiny worm, or is it a long piece of string, or is it a short, uh, like, um, cylinder?
[00:13:56] Renee Zhao: Okay, yeah, it looks pretty much like a hollow cylinder, but it has fangs. And also it has lateral cuts, so it, that creates, that structure, specific design structure, and overall it has a dimension of two millimeter in diameter and roughly three millimeter, uh, in height.
[00:14:14] Russ Altman: Okay. So it's like a Coca Cola, a tiny little Coca Cola can.
[00:14:18] Renee Zhao: Yeah. And it has, it's a very, uh, interestingly designed a structure that allows it to swim super fast. In the meantime, it generates a very interesting flow field. And let me tell you more about this flow field and what it can do. So if we have something that can really swim in blood vessels, that's really cool. It's like a computer game.
[00:14:38] Russ Altman: Yeah, yeah.
[00:14:38] Renee Zhao: You can play with it. So I really, my students love using a joystick to control the navigation of the swim.
[00:14:46] Russ Altman: So I was going to ask, do you have cameras on board? That seems too small. Or do you watch it from outside? How do you know where you are and control it?
[00:14:54] Renee Zhao: Yeah, good question. So at this point, we're at the stage of, uh, directly looking at the flow model. So we have a cerebral artery. There's a one to one cerebral artery flow model. That's a vasculature of the brain. And we can directly see through it because that's a 3D printed, transparent flow model that's basically transparent tubes that follows the vasculature, the curvatures of each point, uh, in the vessels of a human patient. And then we can directly see through it, and then we can use a robotic arm to control the navigation of a small robot. But in reality, this is why it comes, um, so important when it comes to the, this multidisciplinary, um, nature of the project. Because, um, eventually this will be controlled in a X-ray lab, in a CT lab.
[00:15:46] Russ Altman: Okay.
[00:15:46] Renee Zhao: So, because we cannot directly see through the skulls and everything.
[00:15:51] Russ Altman: Yes.
[00:15:51] Renee Zhao: Everything will still be based on X-ray and the X-ray will serve as the imaging, the vision system.
[00:15:57] Russ Altman: And you'll need to have 3D, of course, because you don't want to steer it into the wall or through the tissue.
[00:16:03] Renee Zhao: Exactly. Navigation is complete 3D. That's also one challenging aspect of it. We need to get the 3D vasculature first as a road map and then control the, um, robot to navigate in that 3D space.
[00:16:17] Russ Altman: Yeah, so this sounds like a very general purpose, uh, navigation tool within the body that can get to lots of places. Let me just ask a couple of questions. Is this made out of hard materials or is this one of your soft materials?
[00:16:29] Renee Zhao: So this material is 3D printed.
[00:16:32] Russ Altman: Okay.
[00:16:32] Renee Zhao: It has a soft flexible part and it also has its rigid part.
[00:16:36] Russ Altman: Okay.
[00:16:36] Renee Zhao: So as I mentioned previously, it's a very small system controlled by magnetic field. Basically we apply an external magnetic field that's spinning. So, and then the robot actually follows the spinning frequency of the external magnetic field, and then it swims. And the rigid part is actually those tiny magnets. That's, those are sub millimeter magnets
[00:16:58] Russ Altman: Right, right.
[00:16:58] Renee Zhao: Attached to, um, the flexible part. It's, it can be solved. It depends on, so the modulus, the stiffness of the robot depends on, um, the working environment. We can make it softer, we can make it, uh, like a more rigid depends on what kind of application we want to, uh, we want the robot to have.
[00:17:18] Russ Altman: Do you think that these, um, small robots could also deliver drugs to the sites where they're needed?
[00:17:24] Renee Zhao: Yeah, so this is really fun. The robot itself is highly multifunctional. And first of all, we talk about the swimming capability. If it can swim, that's great. But it's like a toy, right? Swims in a blood vessel. Yeah, of course, it will be a lot of fun. But we need it to be able to treat diseases. So first of all, it can very easily deliver because it's a hollow structure, it can actually carry drugs and then diffuse drugs to a specific site so that we have a very high concentration drug to be delivered to the site.
[00:18:00] For example, if we're treating blood clot, we can deliver a clot dissolving chemical can dissolve the blood clot. And what is really more interesting is that we currently we explore this capability in terms of treating blood clots in a mechanical way and also treating brain aneurysms. So that's the reason I'm seeing that it's multifunctional. It can not only navigate in highly tortious, uh, torturous vessel structures, but also apply treatment. So the really interesting thing about how it treats blood clot is based on the micro structure of the clot. Um, do you know how, uh, what a clot is made of?
[00:18:40] Russ Altman: I know a little bit about that, but I bet you lots of people don't. So what should we know?
[00:18:44] Renee Zhao: Yeah. So, um, a clot, of course, if you will know what a clot looks like, a clot is a red and a squishy. It's like a gel. And the microstructure of the clot is essentially fiber network. It's like a polymer chains. It's a network of fiber and fiber and then trapping or constraining all the red blood cells. And when the spinner is swimming in the blood vessel, if the patient has a blood clot in the blood vessel and the swimmer, so the milli spinner can actually swim into the target. And when it's in contact with a clot, because spinning motion creates a suction and in the meantime, a very big shear force. And that shear force, um, basically densifies the fibrin structure. And we can reduce the clot size, uh, to less than the ten percent of its initial volume.
[00:19:36] Russ Altman: And that's purely physical, right?
[00:19:38] Renee Zhao: Yeah, yeah.
[00:19:39] Russ Altman: No medication?
[00:19:39] Renee Zhao: No medication, no chemical reaction, no nothing, it's completely based on mechanical interaction with a clot.
[00:19:46] Russ Altman: Okay, well, we're going to go to a break in a minute, but before we go to the break, I just want to ask, how do the doctors like this future? Are they excited about this or are they resisting it when you tell them about it? You said there were four or five of them at Stanford. When you talk to them about this, do you hear excitement or nervousness?
[00:20:01] Renee Zhao: It's definitely a lot of excitement. So I'm working with, uh, two interventional radiologists, uh, neurointerventional radiologists on the projects that, uh, I just mentioned to you. And, uh, I can't say that because, um, current technology are out there, but it's not great. And then doctors, they definitely want to see new technologies to come.
[00:20:24] Russ Altman: This is The Future of Everything. And I'm Russ Altman. We'll have more with Renee Zhao next.
[00:20:43] Welcome back to The Future of Everything. I'm Russ Altman. I'm speaking with Renee Zhao from Stanford University.
[00:20:48] In the last segment, Renee told us about millie robots that she's built, how they can navigate and swim very quickly through blood vessels, even in the brain. She's also going to tell us that AI and machine learning are playing a big role in how she does her work.
[00:21:04] So I wanted to talk about size. We think of robots as pretty big. Some of them look like humans. Some of them are these arms that are articulated, that are assembling cars. Um, but I know that you've put a lot of thought into how big do these robots really need and what's the future of the size of these things. So tell me about the trends in robot size and how you're kind of contributing to that.
[00:21:26] Renee Zhao: Yeah, of course. And this is a great question. So when we, as a, how we started, I give an example using my own arm, right?
[00:21:32] Russ Altman: Yes.
[00:21:32] Renee Zhao: So these, considered this as a, the conventional robotic arm, which is like a human size robotic arm. Um, we have to use motors to drive the motion and the degree of freedoms. And the thing about for medical applications, we're basically operating in human body. We don't want a huge system to interact with tissue and organs. So that comes to the thought of whether we can downsize everything in the meantime still keep the functionality.
[00:22:01] So that comes to the miniaturization aspect of the work and the how can we achieve robotic systems are small in size and in the meantime still functional. And that's, I think, that's a very, that's still a challenge, and that's still a open question in many, many fields. And of course, we're working on miniaturization of the robots for metal applications, especially now we're working on of on endovascular robotic systems, right? So that really requires that the system is, of course, smaller than the vessel size, otherwise it will not fit in. And so how can we make a robotic system still functional when it's still small?
[00:22:40] Russ Altman: Especially, I'm like, I'm struck that you're going to need to solve the power issue, like they're doing stuff. And you talked about magnets in the first part of this of the interview.
[00:22:48] Renee Zhao: Yeah.
[00:22:48] Russ Altman: And then obviously that's a great idea because then you don't have to put batteries and motors. Um, but I'm sure there are other good ideas that you need to have.
[00:22:55] Renee Zhao: That's really a very very good point Russ. Um, so the reason that we were using magnetic field is because magnetic field can very easily penetrate human body And for example MRI is already intensely used for diagnosis, right? And now we're using magnetic field to drive the motion of a robotic system for applying medical treatment. So that's even more exciting. So the key point here is to have a type of stimulation that can separate the control units and the power source from the robotics system itself, right? So instead of having motors that had, it needs to be directly connected to the mechanical mechanism that actually drives the motion.
[00:23:39] If we can separate the control unit and the power from the robot itself, so that is the key way of miniaturization of the robotic system. So other than magnetic field, we can have thermal activation, to control the temperature and then use the temperature change to drive motion or it can release chemicals or it can have a certain type of motion for navigation in a very confined space. So that is another example. Or electrical, uh, electrical field. So these are different simulations that we can think of, um, for a miniaturization of the robotics.
[00:24:18] Russ Altman: You know, I'm thinking of, um, I've had some experience in the orthopedic, uh, realm where I've watched some orthopedic surgeries where they're cutting bones and those are very energetic requiring activities. Like the doctors are literally sweating sometimes because they're working so hard. So, um, I can imagine that you're, that the, these power sources are going to have to be able to deliver in some cases quite an amount of power. Because you could imagine that certain orthopedic procedures would be very attractive to have miniaturized, you know, you're floating around fixing the ACL or the MCL in the knee, but then you need a fair amount of juice. Are we going to be able to achieve that? Like, are these electrical or magnetic systems, have they been demonstrated to provide that kind of force?
[00:25:04] Renee Zhao: Yeah, that's a very good discussion point actually. So for when we talk about different stimulation for different functionalities, we need to know which problem we would like to address.
[00:25:15] So currently for these systems based on magnetic field or thermal actuation, we're looking at applications or specific functionalities that does not require a huge amount of energy. Basically a small motion or releasing chemicals would do the work and what we just mentioned, that's a very, very good example on the opposite side. So for the interaction with very hard bones and that you need a huge amount of force and breaking bones is even more, right?
[00:25:46] Russ Altman: Yeah.
[00:25:46] Renee Zhao: So we really need to consider those to decide which type of stimulation we will need to use for those applications.
[00:25:52] Russ Altman: That's great. Um, and I wanted, um, uh, from many, so miniaturization is very exciting. And you've given us a feel for how that could happen. Um, I also wanted to ask about the role of AI and ML. Almost everybody I talked to on this podcast is, has had their world rocked by AI ML. So let me just ask you honestly, is this changing the way you do your work or not so much?
[00:26:16] Renee Zhao: That's a wonderful question. Um, previously it's not changed much of the way we work. Well, of course, in my lab, we have been trying to use machine learning AI tools to guide some, um, material designs for some, um, projects. But there was the recent, uh, advances in AI. And, uh, we started to realize that especially for this specific topic on using, uh, miniaturization of robotic system to treat diseases, right? For medical applications, I think there are a huge design space in terms of how do you achieve? So we talk about the milli spinner, the millirobot that can swim in blood vessels, right? So that's an outcome. Okay, we have this specific design structure and they can swim in blood vessels. That's great.
[00:27:06] So swimming is a capability, but whether we can optimize a structural design so that it has the best performance, we never done that. We've never done that. The design space is huge, right? So how it can swim in different viscosity fluid, and how it can swim in different size of vessels. They will all behave differently. And previously, we were just, everything was based on a trial and error approach. We designed a structure. Oh, it can swim. That's great. But, so talking about structure of the robot, it's a hollow cylinder, it's like a, you mentioned it's a, it's sort of a can, right? So it's a can, but it has a thickness, it has things on it, how many things, and it has lateral slits that allows the interaction of the fluid outside and inside.
[00:27:53] So all these design parameters, there's like over twenty of them. And uh, by ourselves, it's impossible for us to figure out the best design. And so machine learning, this is a really, I think what can be super useful in the future to guide the optimization of the design. And then we can also, so think about the future, um, because these robots can be very easily 3D printed and into different shapes and we can design a system that is a specifically designed for each patient. For example, let me use the X-ray to get the 3D vasculature of the patient. And by looking at the vessel size and everything, the distribution and tortuosity, we can then use machine learning and AI tools to come up with a design that is best for this specific patient.
[00:28:42] Russ Altman: Oh, that's very exciting. So now we have personalized robots that are, they're made to solve the navigation challenges in the particular patient.
[00:28:51] Renee Zhao: Exactly. I think that will be super exciting for, well, which will revolutionize the future of, uh, uh, healthcare.
[00:28:59] Russ Altman: That's, that really is exciting. And one final question that I want to ask, maybe final, um, is, um, you're, you use the word millirobot and you were telling us that these robots are a couple of millimeters in diameter and three or four millimeters in length. Is millimeter where you are going to stay? Or do you think, um, micro or nano? Because we sometimes hear about these nano robots. I don't know if you think of that as a totally different area or if that is part of what you're interested in pursuing.
[00:29:27] Renee Zhao: Yeah, well that's another great question. There are many, many groups and it's actually a very, very big field of people working on micro robots and nano robots. Those are even smaller scales. When I really give talks at conferences and universities, I often get this question. Uh, do you think this millimeter size is, uh, already small enough? Are you thinking about even downsizing them?
[00:29:53] Russ Altman: Right. Right.
[00:29:55] Renee Zhao: So my answer to this question is that it really depends on the application. For example, if we are looking at this endovascular environment, it's not always the smaller, the better. So small is good as long as it can fit. So it's, uh, if we, I have this system, which is, uh, right now is two millimeter in diameter that swims in the blood vessels pretty nicely. If we downsize it even more, it doesn't mean that it will have a better performance. So the size really depends on the operation environment that we're looking at.
[00:30:27] Russ Altman: And is this because of things like viscosity and it becomes harder and harder to swim when you're smaller and smaller?
[00:30:33] Renee Zhao: Right. And also the boundary condition, how it interacts with objects. And especially, uh, we talk about it can swim, but in the meantime, we want it to do something. We want it to be functional, to treat diseases. If it's too small, it will be really challenging for it to interact with a clot. We still need a size and we need it to provide, um, the sufficient actuation force to interact with, um, the clot, object, like clot or brain aneurysm. If it's too small, then it's actually going the opposite way.
[00:31:03] Russ Altman: So for now, you're very excited about all the opportunities at the millimeter scale and uh, why not stay there and optimize, uh, the capabilities at that scale before we think about other scales, at least for you and your group.
[00:31:16] Renee Zhao: Yeah. And, uh, so back to the topic on machine learning and AI tools. The reason that I think the design space is huge is that because the swimming capability and all this capability of treating blood clots, brain aneurysm is all allowed by the structure design of the robot. And if we don't have a structure, so if we think about like sub millimeter micro scale and the nano scale, usually it goes down to a particle.
[00:31:41] Russ Altman: Right.
[00:31:41] Renee Zhao: So they no longer have a very, uh, specific or complicated structures because of, it comes to like manufacturability and those challenges as well.
[00:31:51] Russ Altman: Right, right.
[00:31:52] Renee Zhao: So when we're still in the millimeter scale, we are able to design very complicated structures that can enable a lot of functions. And that.
[00:32:00] Russ Altman: That's a great point. That the industrial capability for large scale, um, like manufacturing is very good at the millimeter scale. We have many things in our lives. I'm looking around my office. It's filled with millimeter scale things. So when you come up with an idea, the ability to make it is there.
[00:32:18] Renee Zhao: Yes, exactly. So combining, um, the design capability, the manufacturing capability and everything. So that's very, very important. We don't want to design something, uh, ideally it will work, but it will never be manufactured.
[00:32:34] Russ Altman: Thanks to Renee Zhao, that was the future of robotic surgery.
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