>> Natalia: Hi, I'm a professor of biomedical engineering
and I have a lab called Computational Cardiology Lab,
and we work on this digital twin technology.
I want to first start with sort of touching base
of what is actually a digital twin?
Digital twin is a replica of a physical entity,
and it provides not only representation
of the different elements,
but the connection between the elements,
dynamic between the components,
how the entity functions as being composed
of these interrelated parts.
So this is a concept that has been used a lot in industry
because digital twins are used to optimize operation
and maintenance of physical assets.
For instance, one can think of let's say an MRI machine.
They're very expensive,
and one wants to anticipate the potential of a part failure.
So a part can be ordered in advance
and replaced before the machine needs to be shut down.
And there is a wait period while the new part is brought.
So this is a concept that is used now,
what's called in the fourth industrial revolution.
We believe, and this is not just our opinion,
but also widely accepted among people
who are looking into the future of healthcare,
that the digital twin will be a technology
that is most likely to disrupt healthcare,
because it will enable this continuously
adjustable personal repositories of information,
it's basically a representation of the patient
or an organ of the patient
where all the information continuously comes
into this digital entity,
and it presents all kinds of information about the patient,
and that digital twin can be used to decide
what would be the treatment,
what is diagnosis and so forth,
and it keeps being updated with more information.
So I wanna talk to you about the heart digital twin.
Back in 2018,
this is a screenshot from web-based blog from Phillips.
You can see here their logo on the screen
where article was written
how a virtual heart would save you real one.
In other words,
the virtual heart can be used to anticipate
the problems with the real one,
and it can provide new diagnostic approaches,
and also what would be the appropriate therapy
for this patient?
And this is entirely the concept
of personalized medicine embedded in this.
So in my lab,
we do these sort of personalized medicine
in regard to cardiology.
And we use this digital twin concept
in our translational research.
and I can divide our clinical translational research
into two parts; diagnostics and treatment.
And in diagnostics we mostly have predicted risks
of sudden cardiac death.
We are working on predicting of stroke due to arrhythmias,
also recurrence of disease after treatment.
in the treatment part,
we are mostly focused on treatment
for heart rhythm disorders,
and those are ablation guidance
for atrial and ventricular arrhythmias.
And I wanna share one of the studies.
We have numerous.
So I wanna share just one of those.
This was a approach that was recently published in the fall.
And this was, as I said,
a treatment for heart rhythm disorder
that was entirely driven by computation.
Basically we constructed a digital twin
of the patient's atria to treat atrial fibrillation,
which is the most common human arrhythmia.
One to 2% of the human population has that.
And it's one of the biggest healthcare expenditures.
So we call this approach OPTIMA,
Optimal Target Identification
via Modeling of Arrhythgenesis.
So let me tell you what the concept is.
So this was a prospective study,
entirely driven by simulation.
So the patients are treated by threading
a catheter in their heart and burning a location,
which is considered the perpetrator of the arrhythmia.
Now typically that perpetrator is very hard to determine.
And what we're doing is we're using
a scan of the patient's heart,
constructing a digital twin,
incorporating information about
the functioning of the cells in disease in this patient.
And then we are predicting the personalized treatment
for each patient.
And so it was a prospective trial.
So the physicians directly navigated the catheter
and burned pieces of the heart based
on our computational prediction.
So let me describe what the process incorporates.
So first there is a contrast enhanced MRI
of the patient before the procedure.
That we get this image,
and then we segment the images as you can see here.
From the segmented image we reconstruct,
those are the two atria of the patients,
the upper chambers of the heart.
They incorporate areas green, these are fibrosis.
These are typically patients
who have persistent atrial fibrillation,
fibrosis grows in the heart.
Also age leads to that as well.
That's why more advanced age is associated
with a higher probability of fibrosis.
And then we calculate the personalized arrhythmias
by poking the heart, these many, many little locations.
And then we elicit these recirculating waves,
which are like little hurricanes on the heart.
Those are the arrhythmias.
And what we are trying to do is to determine
which is the main perpetrator?
And you can see here a little dot
that's going around and around.
And this is an organizing center.
The others are really chaotic,
but we are finding one organized center right here.
And that's what we are going to target with our approach.
My slide is not advancing for some reason,
but we can solve that this way.
So this was the concept.
What we want to do though is not just to predict
what are these arrhythmias?
And then here we are predicting
what are the ablation targets?
But what we do is a mock-up of the clinical procedure
in the model.
We perform the ablation.
Then we predict whether the ablation was done right.
Whether sometimes even if you do it right,
there are new origins of new perpetrators.
And so we perform virtual ablations.
Then we identify whether there are new targets,
new arrhythmias will arise,
which is a very common reason
why these ablations fail in the patients.
And then we predict the new targets.
And we repeat this until we have
a complete inability to induce arrhythmia
in this patient heart.
And then we export these targets.
They are exported in the procedure room
and the catheter is navigated to these targets
and the treatment is executed.
And that's that.
So what is the biggest benefit of what we do?
I said that when I described the digital twin,
the biggest benefit of the digital twin is
that it is tailored not only to the patient,
but in anticipation of the patient's response,
because we are predicting how the patient's heart
is going to respond to this ablation,
to the burning of the piece,
and where the new problems will arise.
So this is one of the biggest advantages of this technology.
So let me show you one of our patients.
Again everything is prospective.
Everything is driven by simulations.
This is the atria of this patient.
The green is the fibrotic remodeling.
This guy had previous ablation unsuccessful.
So we took him and we were able to predict,
here is one arrhythmia on one side on one of the atria,
it's a recirculating wave.
There is another one on the other side.
So we had these two targets shown with circles.
And then the physicians basically when they're unoblated.
and here is next patient.
That's another patient, a little bit less fibrosis.
Here is the ablation plan.
I'm not showing the arrhythmia here,
but what appears on the screen during the procedure.
And you can see here, these are the targets.
Some connecting lines.
So the targets are not disjoint.
Everything is calculated.
And we predict that this patient will do very well.
And here is the outcome of
this prospective study of 10 patients.
You can see of all the 10 patients,
only one came back for repeat procedure.
And it was a very simple arrhythmia.
And the particular reason was that
the physicians didn't reach to one of the predicted places.
Now I wanna tell you, in reality,
when this is executed in the cleaning,
the success of this procedure is between 20 to 40%,
20 to 40%.
We had out of these 10 patients,
only one came back and only for very simple touch up.
So it was so much better than
what is done routinely in the clinic.
So what we did is these are the advantages.
It can guide the ablation.
Also we have offline prediction of targets.
We are hoping to eliminate these redo procedures,
which are huge healthcare expense.
We have received an FDA approval.
We went to the FDA,
got an approval for a randomized clinical trial
of 160 patients.
We registered it on ClinicalTrials.gov.
It is the only clinical trial that FDA
this far has approved
that's entirely driven by computer simulations.
The actual treatment of the patient
is driven by computer simulations.
We had two patients about to enroll and then COVID happened.
So this is the OPTIMA approach.
We have similar digital twin approaches also
for other types of arrhythmias like ventricular arrhythmias.
This was the earlier paper.
And the approach is very similar where you use the image,
then you reconstruct a model,
then you predict where to oblate,
and then it is done in the clinic.
So that works also for ventricular arrhythmias.
This is the part of our research that deals with treatment.
As I said,
we also do diagnostics,
which predict risk of sudden cardiac death
or recurrence of disease.
And we had this approach that we also had developed
for predicting risk of sudden cardiac death.
Those were patients that had infarction,
and we predicted which of them
will have actually arrhythmia post-infarction.
This was published.
So this year we expanded that
to another very complex disease, many diseases.
But I just wanted to show one very complex disease
called cardiac sarcoidosis.
And this is an inflammatory heart disease.
And here in our approach,
we combined different types of imaging modalities,
not only the contrast enhanced MRI,
but also PET scans that show inflammation.
And then we constructed the mechanistic model.
And then we combined the mechanistic model
with machine learning.
We incorporated other imaging features
and also clinical data of the patient.
And we had a very good predictive capability of this model.
We called that CHAI.
So in conclusion,
mechanistic modeling, artificial intelligence
are major tools in precision cardiology.
They will engender accurate clinical decision making
and lead to reduction in healthcare costs.
And we believe that
the heart digital twin technology really rules.
We also received a funding.
We were the first federaly funded project
at Johns Hopkins on COVID.
It got funded about a month and a half by NSF.
It is to use machine learning
to predict which patients that have COVID hurt injury
will develop major cardiac events.
And it took me 12 days between submitting
the grant and its funding.
And you can see the press release here by Hopkins
and also it's in the club.
So if you have any questions about that,
I also can answer.
And I'm gonna finish with this last slide.
We have what we call an Alliance
for Cardiovascular Diagnostic and Treatment Innovation,
which is a center that is targeting precision cardiology.
And if you guys want to spend some time looking at our page,
you're welcome to do so.
Thank you very much.
>> Man: Great, thank you Natalia.
So we have time for one question.
Peter Weiss asks,
if we go back to the slide where you were showing
sort of the waves,
the movies for the various waves,
which aspects of the wave of arrhythmia sweeping
over the heart shows pathology or getting rid of it?
Is it the light part, the dark part, something else?
Which one now?
>> Natalia: So if we look at this movie,
the pathology is the green is fibrotic remodeling.
However, the waves that generate,
so normal cardiac beat,
we have a propagation of a wave through the heart,
through the atria.
But when you have these regions
that are obstacles to propagation,
the wave breaks and starts to recirculate.
So by looking at this image,
we cannot tell what the problem is.
If we could,
imaging would be sufficient,
you would not need the simulations.
But you can see it's very distributed
and you cannot tell how the wave will behave.
Only when we incorporate this image
in a geometrical heart model.
And then we put all the cell behavior,
the cells, the way they interact with each other,
some are disease, some are not.
When we put all that together,
this has a cellular resolution.
And when we put that together,
we can see only then a wave that gets attached
to a particular place.
Why there?
why not in the other place?
We cannot tell.
That's why we need the simulation.
I hope that answers the question.
>> Man: Exactly. Thank you.