For nearly a hundred years, science fiction stories have been giving us an idea of what living with artificial intelligence might be like. But we don't have to look to our favorite sci-fi to see artificial intelligence, also called AI, in action. It's already making an impact in our everyday lives whether we realize it or not. When you ask Alexa or Siri a question, unlock your phone using face recognition, or get a notice from your bank about possible fraudulent activity on your account, AI is working in the background to offer us an opportunity or information that we didn't have before. AI uses computers and machines to solve problems and make decisions in the same way human minds do, faster and often with more accuracy. This offers incredible opportunities in biomedicine, where AI can not only help us understand more about how the human body works, it can help us discover the best ways to treat patients, leading to better outcomes.

In this episode of Science Never Sleeps, we're joined by Dr. Hai Yao, a professor of oral health sciences in the College of Dental Medicine at the Medical University of South Carolina and associate department chair for the Clemson-MUSC Bioengineering Program. He also serves as the Ernest R. Norville Endowed Chair and professor of bioengineering at Clemson University. His research studies tempera mandibular joint function and disorders, also called TMJ, and why risk factors for this issue impact treatment and prevention. The TMJ makes it possible to move the lower jaw, which is important for eating and speaking.

We are also joined by Shuchun Sun, who at the time of recording, was a senior PhD engineering student in Dr. Yao's lab, studying machine learning and biomechanics. He is currently a research associate in the Clemson-MUSC Bioengineering Program.

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Explainable deep learning and biomechanical modeling for TMJ disorder morphological risk factors

University of South Carolina, this is Science Never Sleeps, a show that explores the science,

the people, and the stories behind the scenes of biomedical research happening at MUSC. I'm

your host, Gwen Bushey. For nearly a hundred years, science fiction stories have been giving

us an idea of what living with artificial intelligence might be like. But we don't have to look to

our favorite sci-fi to see artificial intelligence, also called AI, in action. It's already making

an impact in our everyday lives whether we realize it or not. When you ask Alexa or Siri a question,

unlock your phone using face recognition, or get a notice from your bank about possible

fraudulent activity on your account, AI is working in the background to offer us an opportunity

or information that we didn't have before. AI uses computers and machines to solve problems

and make decisions in the same way human minds do, faster and often with more accuracy. This

offers incredible opportunities in biomedicine, where AI can not only help us understand more

about how the human body works, it can help us discover the best ways to treat patients,

leading to better outcomes. Our guests in this episode are researchers in this exciting biomedical

engineering space and are using AI in their work to improve lives. Dr. Hai Yao is a professor

of oral health sciences in the College of Dental Medicine at the Medical University of South

Carolina and is the associate department chair for the Clemson MUSC Bioengineering Program.

He also serves as the Ernest R. Norville Endowed Chair and professor of bioengineering at Clemson

University. His research studies tempera mandibular joint function and disorders, also called TMJ,

and why risk factors for this issue impact treatment and prevention. The TMJ makes it possible to

move the lower jaw, which is important for eating and speaking. Shunshun Sun is a senior PhD

engineering student in Dr. Yao's lab, studying machine learning and biomechanics. Stay with

us.

Yao and Shenshen, thank you so much for joining me on Science Never Sleeps. Thank you, Gwen,

and we're happy to be here. It's great to be here. So in our intro, we talked very briefly

about what artificial intelligence or AI is, but what, Dr. Yao, how would you explain what

AI is? So AI also we call the artificial

science and focus on developing a computer program basically, so which can think or behavior with

the human level intelligence. So that's the kind of how we defined the AI or the artificial

intelligence. So AI actually is right now it's everywhere and so for example I think we all

have this kind of experience. So if we... go to the Amazon and on the YouTube to watch the

videos. And so you will see the apps can really know what you like and put the kind of content

you like to see in front of you. So that was amazing, how these kind of apps can do these

kind of things. So actually, yes, they are using the AI models to predict your behaviors. And

also other examples, for example, you are using your cell phone, iPhone, Android phone, you're

using the voice assistant, these kind of functions. So you will find actually they are very clever,

right? So they can identify your voice and know what you want to do. So how this kind of app

can do these kind of things so accurately, and so it's because they also include the AI models

in their apps. So that's the kind of, you know, So the AI, how would impact this kind of daily

life at this moment? So we have these applications in our daily life, but there are really also

applications that are in the medical space, which we are going to talk about today. But

one of the strengths of AI is that it allows us to process lots of data. in a very short

period of time. So can you talk about that a little bit? Yeah, so this is a great question.

So actually, so if you look at the AI, actually right now, you know, it's a very popular and

the super hot topic. But actually, so, it's not that kind of new idea. So AI concept that

was initially introduced actually decades ago, but their application was hugely limited. by

the computational power at that moment. By the ability for the computers to do what they needed

to do because of the technology. Technology was not there. Right. But now, actually, with

the advancement of this kind of computer technology, basically, we focus on the two things, hardware

and software. So they are so powerful now. And also, the other kind of component the AIs depend

on the existing data set because they try to train their intelligence, the programs, based

on this kind of data set. So now we're entering into this kind of digital era. So yes, now

it becomes possible. So with the supercomputing, the power computer is so powerful and also

the data is everywhere. Why data is everywhere? Because we have the internet. So the internet

is a great platform and a vehicle. to generate those kind of content in the data. And also,

it's a great platform to distribute all these kind of AI applications. So that's why it's

a time the AI can really introduce to the daily life. And also, we have basically a lot of

successful story already. So one of the things that you will look at, for example, because

so the data is so complex, The AI itself is a very broad actually discipline. And you have

a multiple way you can achieve the AI. So one of the ways you try to achieve that is we call

it machine learning. So machine learning basically is to try to build that intelligence program.

And it's basically by allowing this kind of program, try to learn from the data set, existing

data set, all the self-generated data set. And for a very complex problem, for example, you're

trying to drive a car automatically or recognizing a very complex pattern. For example, just recognize

somebody from the pictures, for example. Right, right. The facial recognition in our phones.

So for those kind of applications, AI has achieved such kind of very promising results. Yeah,

so that's why, for example, these kind of things can apply to the health care. For example,

people already try to use this kind of machine learning approach, try to analyze the CT or

MR images. Right. Several groups already try to use this kind of approach, try to develop

these kind of diagnostic tools and to look at the pathology, for example, in the diabetes

or the cardiovascular disease. Right. So... And for our lab, so yes, so we also try to

use this machine learning, these kind of powerful tools and combine with the traditional approach.

For example, in our lab, we're using the multi-skilled biomechanics modeling, try to study the musculoskeletal

disease. Right, right. So the AI is only as good as the data set that goes into it, correct?

So what you're saying is that because now we have this tremendous amount of data, now we

are able to utilize AI in a way that we haven't been able to do before, because whether it's

data around our shopping behaviors on Amazon, and Amazon can serve up to us something that

we might be interested in shopping for that we didn't even know we wanted or needed, but

also on the medical side, there's also a tremendous amount of data being generated there in terms

of... of data around different health issues. You mentioned MRI and CT scan, you know, as

far as x-ray imaging and those kind of things that allow us to utilize AI in the medical

space. Exactly. So one thing actually, so we'll look at it here, is there any kind of, you

know, the data available and also those data can be used. And so. In the meantime, do we

have the computational power to analyze those kind of, process those kind of data? So that's

why at this moment, those two components looks like it's available now. So yeah, so the AI

research or the real applications become exponentially with growth and they're doing the past couple

of years. And we're envisioning actually for the next decade. coming decade, I think the

AI could reach out to a lot of different kind of aspects of the daily life and also we expect

the AI and with this kind of powerful tools, they're going to have very beneficial change

for the healthcare and research and practice. Because we're going to hit a place where we

have computers and technology reaching a level that they just simply haven't been at before.

So in your lab, you're using AI, but you are a bioengineer, which we'll talk about that

in a moment, but you are looking at TMJ. So can you just talk to us about what TMJ, or

this temporomandibular joint is, and why it's important to you to be studying it? Yeah, so,

you know, first actually, how we get into study actually this very special joint. So... So

the major reason actually is because of the faculty in the dental school. And so this basically

the joint actually is handled by mostly by the dentist. So the TM, temporal memory joint,

and simply we call the TMJ, is a very unique joint. So that's the only joint actually with

the one piece of bone, but you have two joints in the left and right. And also they are providing

the soul critical function related to our daily life. So for example, when you eat, when you

speak, and even time, actually you want to laugh on something, and also it's kind of a facial

expression, all you have to use these two joints, the temporal medibular joints. Right. So unfortunately,

actually, you know, a lot of people, actually, so they are trying to move their jaw not that

easily. So... So for those people, actually, so they have so-called temporal metabolic disorders.

So temporal metabolic disorders are a group of muscular skeletal functional disorders and

relate to the temporal metabolic joint. And so the people with these kind of problems,

so estimated in the United States, we're about to have 10 million to 15 million people have

this kind of problem. And also the TMD also contributed to the a large actually basically

a group of people with chronic pain disease. So that group actually is you know have a huge

impact on the economy. So probably annual cost is around $500 to $600 billion annual cost.

Right, because people experience a lot of pain when they have these disorders and often are

seeking out their dentists in order to get support for trying to figure out how to get relief.

Yes, because there are impacts of these kind of routine daily functions. Right. So, right

now the challenge is, yeah, so, yeah, it's a very significant clinical problem, but unfortunately,

so, why it's caused this kind of problem? And in other words, the TMD etiology is not fully

understand. Right. To a certain extent, it's poorly understand. Right. So that's why at

this moment we don't have very targeted treatment approach. to handle those kind of problems.

Right. So for a patient who has this issue, there may not be a lot of approaches for them

in order to try to solve it or get relief because, to your point, it's not very well understood.

Yeah, so the reason actually we're not fully understand actually the disease mechanism.

And right now the treatment is mostly non-target. And so for example, we have the, you know,

conservatory treatment including, you know, give the, with the pain medications. Right.

And some kind of time actually in nerve block, you know, joint nerve block, and also a little

bit kind of rehabilitation, you know, strategy. But it's only dealing with these kind of symptoms,

the pain, and also only offer the short term symptom relief. Right. So. And also from the

surgical treatment side, yes, so we do have the procedure called the orthognathic surgery

or the other kind of craniofacial surgery can treat those patients. But the problem is a

long-term outcome, still very uncertain and many patients that are still gonna continue

to have these kind of symptoms in the long run. So. Right now the need is to really develop

this kind of targeted treatment and also have this kind of preventive kind of options. So

we have to fully understand the disease mechanism. And currently in our lab we try to focus on

the identified risk factors. And also to understand what kind of magnetic relationships between

those risk factors and the TMJ, the joint mechanical functions. You know, by understanding those

kind of fundamental relationships, so we'll be able to develop target treatment strategies.

Right. So that's the kind of things, you know, we're doing. And the question here is how to

identify those kind of risk factors. Right. And also how to understand these kind of risk

factors, you know, go through what kind of pathways to impact joint function. And also down the

road, how can it impact the bioarches. Right. And I think that's a really great point. Shanshan,

I want to turn to you for this one because I think you are a bioengineering student and

you are looking at, particularly at the machine learning side of this and the biomechanics

of it. And I think that's really important is when you're looking at risk factors, you're

also looking at, you're looking at the biomechanical risk factors. What are the structures within

the jaw? joints that may put someone more at risk. Can you talk a little bit about why being

a bioengineer in looking at this issue is helpful? Well, there are several aspects in here. The

first thing is that we are as engineers, but we are not only doing the engineering part.

So for engineers, we typically basically seek a solution for a problem. And we are also doing

part of the science problem. We are also trying to figure out what is going on in this world,

what is going on within this patient. So to solve this problem, it is necessary. So the

first thing we are going to do is we talk with surgeons. We try to figure out what is going

on in their observation. And we try to figure out what is the problem. So then we use engineering

tools to try to target those problems. That is engineers' skills is needed. And after that,

we're going to use our engineering skills to really try to solve that problem. So although

we are called engineers, but it's really a combination of science and engineering skills together

to solve this problem. And engineering can help us design instrumentations. For example, how

we can track the motion, how we can efficiently measure the electron

And it can also help us design solutions using our engineering skills. So that's really interesting

because when we think about an engineer, we think about things like buildings, we think

about bridges, we think about these type of construction, at least I do. But when you think

about bioengineering, it really is a little bit of the same thing because it's about the

strength of the thing and how the thing operates in space and in the world in order to be efficient,

I guess, is kind of how I think of it. And so when we look at a mechanism like the jaw as

it works along with the rest of the skull, you know, it's a little bit like the same questions

you would ask about a bridge, I guess, maybe. Is it strong? Are there places where it's weak?

How are those places that are weak? increasing risk for things like pain or injury or you

know these type of things. So can you tell us a little bit about what are some of the features

of the of the jaw that are maybe predisposing people to have pain? Have you discovered some

things that seem like pretty unique risk factors in the jaw that can kind of indicate this?

Yes, of course. So I think I may want to start this with two interesting stories. So when

we made the presentation at some conferences, as well as the scholars day in MUSC, so we

presented our work and someone came to us quite excited because either themselves or someone

in their family has the feature we described and that is basically people with small mandible

and people typically with the mandible shorter than the upper part, And typically, women are

more likely to get that. And very often, people came to us saying, oh, I have someone in my

family, or I am this type, and I do have TMG problem. So that's exciting moment that we

have. So we identified, actually, what we find is multiple features. Just like I have already

described three, there are even more to describe. For example, the condyle size is also another

factor. It is exactly because there are so many factors, and each factor is different. patient

could have a unique combination that makes this thing so difficult to study. That is also why

we rely on machine learning to give us an answer, because it's pretty good to look at a large

volume of data, and to try to look at the very complex data to make a connection between things

like a structure and the diagnosis result. So that is why we use this machine learning tool

to study that. But briefly, the several factors that we identified, including the mandible

size, people with small mandible are more likely to get TMD. And the mandible is the actual

jaw itself? Yeah, it's the jaw. The jaw itself, OK. And also, women are more likely to get

TMD. So that also matches the clinical observation results. And also, people with small condyle

is also another risk factor. Those are just a structure aspect. We are talking about a

structure. There are also other reasons. For example, people have been talking about mental

stress as well as trauma. Those also could be problems as well. Because sometimes we might

grit our teeth or we hold stress in our jaw, which then may lead to inflammation or other

things that might exacerbate the issue. Yeah, that's also one that is what we call that is

also one of the non structural aspects is about behavior. Well, things like the one you said,

whether we grind our teeth during sleep or whether we well, we like to eat hard food, those kind

of things. Those are related. So again, this is pretty multifactorial. So we start that

is why we start with the structural side. But actually we're expanding the structure side

to other sides as well to include things like behavior or stress, the other components into

this. So that's really fantastic because that means as a bioengineer, you're not just looking

at the strength of the structure, but particularly as a biomedical engineer, you're looking at

the strength of the structure also surrounded by the behavioral forces that might be impacting.

impacting that joint or in your case, you know, the jaw bone, but also or the jaw joint, but

also, you could look at it in other skeletal features as well, I would guess. Yep. So the

same technique could be applied to other joints, but found that because fundamentally the joints

are mechanical system, you have structure to fulfill a function. If the structure has problem

has problem in either is originally or because of usage, you have problem in the structure,

it will influence the function. So to recover the function, one of the best ways is to figure

out the problem in structure and try to fix that structure. That is why we start with a

structure. And the structure is also related with how you use it. That's why, as you mentioned,

the behavior is another important aspect of this thing. So structure and behavior and function,

they are closely related concepts. So that's a really great point too that you just hit

on and that I want to draw out, which is that the goal is really. to look at how we treat

what's happening at the source versus continuing with symptom management that might not get

us very far. Dr. Yao, did you wanna say something about that? Yeah, so as Su-Chu mentioned, so

this is the truly actually a multifactory actually the problem and so. So we try to understand

actually how those kind of, you know, first identify the risk factors and also to understand

how those kind of risk factors impact the joint mechanical function. As Suh-Tsu mentioned,

this, you know, the structure, behavior, and function, this axis. So first actually, so

we did this machine learning and we identified several risk factors, you know, in terms of

the structure. So the thing here is This kind of machine learning based work is great, so

it helps us to systematically go through all the data sets, identify these kind of morphological

risk factors. For example, the medibaric size, condor size, and also rammer size. But behind

that, actually, we want to understand what's the mechanism, how it impacts the functions.

So as a bioengineer, we have to understand. and try to relate this kind of risk factor

to the joint functions. So one of the approaches we're doing right now, we try to integrate

the machine learning and with the conventional so-called deterministic approach, the modeling.

This is the focus on the biomechanics model. So combine the machine learning with the mechanics

model to build the relationship between the risk factor and the mechanical. functions.

So that makes the machine learning even more powerful in these specific problems. And for

example, so here we identify those risk factors from the machine learning, like maneuver size.

So we built a computational model to look at how this risk factor, for example, maneuver

size and the counter size, could impact over mechanical functions. is they're going to overload,

generate a big joint force, or generate a big mechanical stress, and to overload the joints,

and also down the road, this kind of big force and big mechanical stress can impact the barrage

to initiate the tissue modeling and damage the tissues. So those kind of things, actually,

so we through so-called multi-skill biomechanical modeling to combine the morphology, mechanical

function. and the biology together. So you see this kind of multidisciplinary approach really

benefit actually these kind of studies. And so here actually, so the point here is machine

learning is very powerful. Without this kind of machine learning, these kind of powerful

tools, we won't be able to systematically to identify these kind of structure risk factor.

But the machine learning itself also has some kind of limitations there. because they are

really not going to help you to build, to tell you why this is a risk factor, and what's the

mechanical reasons and the biological reasons. So by integrating with our multi-scale mechanical

models, so use the output from the machine learning. And so we put it into the mechanics model.

Now we understand how it's going to impact the mechanical functions and how it will change.

the barge in the joint and eventually can lead to the disease pathways. Would it be accurate

to say that the machine learning helps you see where to focus and then the other models that

you have allow you to then investigate that focus where you may take years to figure out

what the focus should be without the support of the machine learning processing these sheer

quantities of data that it can process. Yeah, you just did the wonderful summary here. And

so, exactly. So without the machine learning, we wouldn't be able to search quickly to systematically

to identify the risk factor. Right, because the machine learning can't tell you where is

this is the problem. This is the problem. It needs to be focused, yeah. It can say, here's

some things based on your criteria that we think you should look at. This is what's rising to

the top, so to speak. Exactly. So that actually really gives you the idea, what do you need

to focus? And they're very powerful. They're not gonna miss any things. Right. So if we're

based on the traditional approach, for example, based on this kind of hypothesis-driven and

approach, conventional approach, we could miss some kind of things. But machine learning is

very systematic. They can identify all the risk factors in one time and overall. So then we

have the focused areas. So in other words, in the engineering part, we have the region of

the interest. What do we need to be focused? The region of interest, yeah. So then we can

study that and to understand the mechanism. Biomechanical mechanisms and mechanical biological

mechanisms. So then we can answer the question, what exactly the cause and for this kind of

disease or the disorders. Right. So then we can identify the target, how to restore the

TMJ functions using the therapeutic strategies, either through the medication or through the

rehabilitation or the surgical treatment because we have the target now. Right, which is such

a incredible thing for the patient because now we're making, we're able to make a more informed

decision about what the treatment strategy should be because these targets now have been identified.

versus maybe going down a list of what we think might work and kind of hoping that something

would work. This is where we get to the more, the better possible outcomes by using machine

learning and AI in patient care. Yes, and also the machine learning can take care of these

kind of, you know, the complicated situation because each patient is different. Right. And

as Su-Tung already mentioned, so when we look at this kind of structure, you know, risk factor,

there is all kind of possibility. For example, some patients only have one risk factor. Some

people have two, some people have three, all could be the combination, all the different

variations there. Right. So machinery is so powerful. Yeah. So they learn the things from

the collective data set. Right. But then they do the predictions, they can be the patient

specific. Right. So they can identify. for the specific patient, what's the combination, what's

the problem. So it's very targeted. Right. So we've talked about the AI and the machine learning

as a part of that. AI is the broad umbrella. Machine learning and also deep learning fall

under that. But let's talk about the data that goes into that funnel. So when we talk about

data that the machine is working with in order to develop these regions of interest where

it says, hey. Here's a flag in the ground. You should maybe take a look at this. What kind

of data is going into that system? So for our machine learning model, we use a dental scan,

the dental CT model. So that is our input. But that is not all. We need to process the CT

in order for the model to use it, because AI is basically mathematical. So you can only

put in things that could be represented by the mathematical entity. So what we actually did

is from the CT image, we can get the geometry of the skull, of the bone. And then we try

to discretize them into matrices. So now we have a matrix. The matrix could represent geometry.

So if it is one in the matrix, means there's a bone over there. If it's 0, there's no bone

over there. Now we have a 3D, we have three dimensional matrix, which represents the geometry.

That is our input of the data. So you're taking an image, a picture, and you have to make it

mathematical in order to enter it into the system to be used. Correct. So actually anything that

goes into a machine learning model needs to be mathematical. So even for other. disciplines,

maybe like computer vision, that's also something mathematical. It doesn't see a picture, it

sees a matrix, basically a bunch of numbers. That's what machine learning could see, could

understand. Wow, that's fantastic. So you have the image, the mathematical image that you're

putting it in. But you also are working with multi-dimensional data as well, right? So what

does that mean? What kind of data is that as well? Well, there are other things that could

be used as input. So a very good example is the motion, because it's a joint. So it has

movable part to be a joint, right? It moves when it's a joint, yeah. Exactly. So that's

why we put the motion data. And we basically use that as another source of input. And people

are all very interested in the muscles, because muscle is the actuator for the system. And

one of the good indicators for the muscle activity would be the electromyograph, which is the

weak electricity generated by the muscle when it generates force. So that is another example.

And also clinical records and diagnosis result can also go into that, but maybe not always

go into the input, but also go to the output as well. Basically, the machine learning model

tries to build a connection between input and output. So we can put that information in the

input. Or we can say, well, it has this structure. And then we know from the clinical notes this

is a female subject with TMD. So it could be encoded in the input encoded in the output.

So that is basically an example of other factors we are considering. We are trying to integrate

into this model. And you're laying all of these data on top of each other. And rather than

a team of people, two, three, five, however many people, sifting through all of this data,

we now have the power of the computer to be able to plug it all in there. and really come

quickly to some ideas about where we should be exploring and gaining new research discovery

around this. Correct. One of the problems with humans looking at the data, which has been

there for quite some time, the problem is that sometimes we're not quite good at looking at

something. For example, complex structures. We're not so good at looking at three structures

and try to summarize what is going on over there. And it became even harder when we do it on

the computer screen, because things were kind of distorted and scaled over there. But machine

learning is different. It looks at mathematical entity. It looks at things. It could look at

a big picture at the same time. And it could look at multiple data at the same time. Think

about someone. Maybe I can look at two or three at the same time, but 100? No, never. But machine

learning could do. Could look at 100. very complex 3D geometry at the same time, and try to find

out the pattern and the connection with the diagnosis results. And especially when talking

about the multi-dimensional data set, we mentioned that you have the CT imaging, and you have

the motion data, and you also have this electromyograph data. And we put together how to look at it.

And a human being probably doesn't have this kind of capacity, you know, at the same time

try to look at all those kind of different domains, these kind of data, but the machinery, yes.

So they can look at this kind of the data set from the different domains and these kind of

different dimensions. And also we look at also the longitudinal data, not like one time spot.

So we have different time spots. So they can dig into this kind of data set and try to find

the unique patterns. So this is probably, the human being, probably, it's impossible to do

that. So that's the kind of beauty to use in the machine learning. So specifically in this

project. And also this kind of approach can apply to the other kind of organ system and

other kind of disease studies. Right, right. So Dr. Yao, this project has been a tremendous

collaboration across, it's a multidisciplinary collaboration. It's, we have the, we have dental

medicine, we have a more traditional college of medicine, we also have physical therapy

and some of the health professions. Can you talk a little bit about what it has been like

to have this collaboration and what the strength of that collaboration has meant for the outcomes

that you're seeing in your lab? Yeah, so as we discussed, actually, this is the truly multidisciplinary

kind of approach studies. And so how make these kind of things happen, actually? So here, actually,

I would like to emphasize the one thing. So we have this kind of platform and be able to

group people with the different expertise. So in this case, we have the dentist. We have

the medicine people. And so we have the biologists to understand the joints, the barge. And of

course, we have the bioengineering to be able to assess the joint functions. How are we going

to put a group of these people and work together and to address these kind of very complex problems?

Yeah, we do have this kind of system to ensure the group people can work together. So here,

we have a very unique. program. It's called Clemson University and Medical University Joint

Bioengineering Program. So this program actually established in 2003. And so the mission for

this program is to try to encourage multidisciplinary translational research and education. So we

are focused on the research and also we are focused on training our next generation of

investigators. So within this group... the program, so we have a group of people, physician, biologist,

and engineering, we are working together. And so in this case, engineers serve as a kind

of liaison. And so they try to interact with the physicians. So when we started TMJ, we

start with the physician, and we try to get the first input to define what's the clinical

problems. And in the meantime, we reach out to the biologist and to see these kind of mechanical

functions and joint environment having to relate to the different barges down the road. And

as a bioengineering, we can put all those kind of output from the physicians, from the biologists,

and put it into our computational model and to simulate the whole joint systems. identify

the risk factor and also to elucidate the mechanisms. Based on that understanding and with this kind

of knowledge, so now we go back to work with the physician again, so try to design new strategy

for the treatment or for the prevention. So that's the kind of unique platform through

the Clemson MUIC joint bioengineering program to ensure. We have the expertise, multidisciplinary

expertise, and also the workforce and investigator, as well as the students in the program to work

on this multidisciplinary project. So for example, we do have expertise for the machine learning

from engineering part, but we also have the clinical input to help us to identify all the,

to classify the data set with the diagnostic outcomes. And also we work with the investigator

from the College of Medicine on a standard barrage. And also we work with the health professionals

and look at the different strategies for the rehabilitation through the physical therapies.

So this is such a unique environment. We are the only probably the programs with the engineering

by engineering on the medical campus and work with the team like that. So it's a very unique

program. Yeah. And I think your point about that it really is research that moves towards

translational is a great one because what you're doing in terms of learning about the jaw and

the risk factors, a lot of that is through the process of the data, you're discovering things

that we don't know yet about these joint systems. But it's towards an end of moving towards...

moving it to patient care and using that information to improve how we care for folks who are experiencing

disorders in the jaw. And that's so important that we can make that connection and show that

this is what research does, is it discovers the things that we don't know so they can move

towards improving care and prevention in the future. Right, so as an engineer, we always

try to translate all the discoveries, all the findings from the lab. and to the best side.

So our ultimate goal is basically to treat the patients. All could develop a strategy to prevent

this kind of disease. So we believe as an engineer, because as Su-Tung mentioned, so the engineer

is, the essence is to try to focus on the problem-solving. But problem-solving, we also need to understand

the mechanisms. So these kind of problems. resolving strategies based on well understand the disease

mechanism. So that's why all the strategy we develop is very targeted and available with

very sound science as a backup. When we think about AI in this space or just AI generally

really, I think you have some really great examples to talk about this as a tool. It is not a tool

that takes precedent over anything else, but it is a tool that we can use to expand our

learning and discovery, which of course is something that we really love to focus on here on Science

Never Sleeps. So Dr. Yao, can you talk a little bit about AI as a tool? Yeah, sure. And so

AI actually, you know, just like, you know. other tools, you know, so human being actually

invented over the years and tried to understand better about this world and tried to create

the tools to develop the things that really benefit the society. So look at the artificial

intelligence. So compared to the human intelligence, they do have a lot of advantages. For example,

So the AI can really learn from very complex patterns, and they never get tired. If there

are people to look at, they get tired and start to make mistakes. Yeah. And also, if you look

at actually the other things over the years, actually the human beings always try to create

these kind of tools to... to benefit actually and make ourselves more powerful to observe

the world. So, to give an example, our human being, their vision capacity is limited, so

they cannot see very, very far away the things. So that's why they tried to create some kind

of the tools. Eventually, they invented the telescope, so they can see the stars and see

the details. So just recently, you know, so we put a very fancy microscope on the orbit

so we can observe the far away the galaxies, right? That's just the truth, but that tremendously

increased actually the capacity for the human being to observe things far away and the same

thing actually, so we also try to look at the things in the very detailed, very small scale,

but So our eyes won't be able to do that very well. So that's why we. come out the microscope.

So it's very exciting things. Now we can see the little microorganism, like a microbial,

and those are very clear details. So that kind of image helps in the biomedical research tremendously.

The same thing here, and now we face new challenges. So we have such kind of fruitful data set and

very complex. patterns to identify. It could come from this CTMR imaging, could come from

the motion, and come from the electromyograph. Or it could be a combination. And how are we

going to deal with that? And human beings probably, with their bare eyes, probably is hard. We

need new tools. So in this case, yes, the artificial intelligence specifically could be the machine

learning tools. as a grid tool, just like a telescope or the microscope, help us to better

to process those kind of data set to get meaningful results. And so there's no surprise, I think,

down the road, so the human being will create more tools to help us to better understand

the world we're in at this moment. I love that. I think it's a little bit like the... AI is

the microscope for the data scientist. Yes, of course. So we can understand, you know,

consider actually AI like that. Mm-hmm. Yeah, it's a powerful tool. And also, yeah, we can

ensure this powerful tool can be used properly. Mm-hmm. So that's why we have all those kinds

of different strategies to guarantee those kind of tools in the people's hands with a good

cause. So it feels a little silly to ask what is the future of AI, because it feels like

the future is AI, but certainly there is a future to utilizing this tool. So what is the future

of this in biomedicine? So first, actually, for sure, AI is a very powerful tool, and also

can make very significant change the way actually where... doing the biomedical research and

also the health care practice. So no doubt. And we envision actually these kind of applications

become more and more with these kind of exponential growths. So one thing actually I would emphasize

on that is we need to notice actually first how powerful these tools are, and also need

to understand. any limitations in these two. So that could help us to fully utilize and

explore those kind of the potential for these powerful tools. So one thing actually I'm thinking

about in the biomedical research, so the AI must be combined with the traditional approach

we are using at this moment. For example. how to combine with the way of doing things, you

know, daily based with the, you know, using the, you know, batch top, you know, cell models,

and also with the animal model studies and the clinical trials. So in that case, we can ensure

this kind of, you know, outcome from the AIs and it's meaningful, and with the great magnetistic

understandings. So this kind of output, when we go back to use for the patients, so we have

fully understand the mechanism. So it's basically, so we're ensure this kind of outcome so we

use this properly. So the second part, actually, so we're looking at this and how to increase

this kind of application for the AI. As Sucef mentioned, so the AI is based on the data.

So how are we going to create this kind of mechanism to better generate this data and share this

data? Yes, we're in the digital era, right? So we have a lot of the platform being able

to generate the data and share the data. And also especially try to create the multi-dimensional

data set. And the one thing I would emphasize and also be cautious is when we try to collect

this data, and store this data and use this data, we have to make sure we're doing this

in a very safe way. Because the tool is powerful, but we need to make sure the tool is in the

good people's hand and used properly. So that is what I'm thinking about. But overall, I

envision the AI. And it will probably lead to significant change in biomedical research in

the near future. Yep, so totally with what Dr. J.L. mentioned. So I think in the future, the

thing that we really want to see is really large quantity of data and more diversity data. So

we see all kinds of data could be used as input. As the biomedical engineering develops, we

can gather more data. We can have more information about the patient. Those information can all

go into the machine-naming model and contribute to the health care. Thank you so much for joining

us on Science Never Sleeps. It's great to have you here. Thank you for having us. We've been

talking to Dr. Hai Yao and Shunshun Sun about TMJ and the use of AI to improve treatment

and prevention. Have an idea for a future episode of Science Never Sleeps? Click on the link

in the show notes to share with us. Science Never Sleeps is produced by the Office of the

Vice President for Research at the Medical University of South Carolina. Special thanks to the Office

of Instructional Technology for support on this episode.