[MUSIC PLAYING]
ERICA: Great.
So next up, we have our final project,
the 10th of the 10 projects that we're going
to be sharing with you today.
And so this is the CoronAI project.
They are based in the US at UC Berkeley,
and so we're really excited to see what they built here.
It's a really cool project.
So I'll let you take a look.
And Laurence will be joining us again
to ask the team a couple of questions.
So let's take a look at the demo.
SPEAKER 2: CoronAI-- creating technology for change,
because it is time to create technology
that helps our doctors.
What we did was create a band filled with electrodes that
once put on the patient's chest creates
images that can then connect to a computer
screen for easy access.
This computer screen can easily be
connected to any dashboard that is accessible by a doctor,
without the doctor having to be in the same room.
Our solution provides doctors with a mechanism
that allows them to detect which patient
is in a more severe condition and how to allocate
their resources efficiently.
Our machine gives instantaneous results, is portable,
and is much, much cheaper compared to the existing
methodologies today.
This concept is based on electrical impedance
tomography.
What this does is it creates a total graphic image because
of the intersection points of the different electrodes.
As you can see, each red circle in this image
is one electrode that emits a tiny, small amount of current.
Because of which, there is an ecopotential change
that all the other electrodes face.
And at each intersection, it comes together
to form a topographic image.
Coupled with our machine learning algorithms,
this image is then turned into a 3D illustration
of the human lung.
With this information, we are able to extract
what the lungs look like when the patient is breathing,
and therefore, are able to detect what parts of the lung
are affected and any progress in the patient's condition,
based on the scan from the previous stay
and the scan that's been done today.
Doctors no longer need to bring patients out of their wards
and take them to do a CT scan.
And now they, too, can, practice social distancing.
It's time to use technology to help the doctors and nurses who
are working so hard to help us.
It's time for CoronAI.
ERICA: Hi.
Hi, Team CoronAI.
NEIL: Hello.
ERICA: Hi, good to see you.
SPEAKER 4: Hello.
SPEAKER 2: Hello, great to see you.
ERICA: How are you doing?
SPEAKER 2: Very well.
SPEAKER 4: Doing great.
NEIL: Great.
ERICA: Great.
That's great to hear.
And we also have Laurence--
LAURENCE: Hey.
ERICA: --who's stuck around with us
and wants to ask you a couple of questions about your project.
I'll pass it over to him.
LAURENCE: Yeah, so when Erica asked
us to tweet about this thing, and I
tweeted that one of the projects blew my mind,
and it was this project.
So I just want to say congratulations, CoronAI.
NEIL: Thank you.
LAURENCE: I am so happy to be here.
And it's a real privilege to actually
be able to ask you questions about it.
So I'm just going to get right down to it.
I loved how you explained the tomography.
And you built these devices.
And how many of them did you need
and how did you build them?
SPEAKER 2: So first of all, thank you for saying that.
That was great for us to hear.
So unfortunately, or should I say fortunately,
our solution was developed while we were in lockdown.
And so because of that, we built our first prototype
using the nerdy engineering tools that the three of us
had together.
So that was Raspberry Pis, numerous random sensors,
one Velcro strip, and some basic electrodes.
So in total, using this equipment,
if I can call it that, we were able to develop
five initial devices, which we then used for testing and demo
purposes.
The cloud technology was the most [INAUDIBLE] of that.
And [INAUDIBLE] had nothing on the cloud services,
using the Google Cloud product.
And [INAUDIBLE] for the library with Keras/ we trained
and tested our models at home, on the Google Cloud Collab.
This provides us with a lot of resources-- also, the GPUs.
And next, with demoed it on one of our team
members, which is Neil.
NEIL: Yeah, aside from being the guinea pig who was tested on,
I was also in charge of fabricating these materials
and assembling the hardware, which [INAUDIBLE] guided.
And actually, by using those tools,
we were able to make it small, compact, and easy to use.
LAURENCE: And Neil, your acting skills in the video
were fabulous, I have to say.
[LAUGHTER]
Oscar-worthy stuff.
NEIL: Thank you.
LAURENCE: By the way, I got a ton
of old Raspberry Pis and equipment lying in the closet
behind me here.
Can you guys come around and build
me a medical scanner out of it?
[LAUGHTER]
SPEAKER 2: Yeah, yeah, we can totally do it there.
LAURENCE: And then one other question
that I had when I was looking at the video
and looking at the project was really about the training data
that you use.
Because you were able to use training data,
and you're able to build these sensors and then
turn that into visualizations.
So how were you able to convert the reading from the sensors
into those visualizations?
And how did you train a model around that?
SPEAKER 2: Yeah, for sure.
So with my background in electrical impedance
tomography, I was already aware, actually,
that the technology is able to produce these black and white
two-dimensional images, and that in the human body,
biological compositions are arranged such
that any phenomena appears either clustered
or varying over space.
So this is what we did with eight electrodes.
We used each one and had it send small frequency pulses.
And then we measured the ecopotential differences
experienced by each alternate electrode.
And then we created a bunch of back projection algorithms
which we applied to this to generate a 3D bio
image of the scanned human body parts inner structure.
So in Neil's case, it was his lungs.
This technology is non-invasive, radiation-free,
and most importantly, real time, which of the three of us,
Neil was the most thankful for.
NEIL: Yeah.
SPEAKER 4: Also adding to that, the images
were reconstructed using the open Yeti library in Python.
And we used around eight electrodes
at the frequency of around 50 cycles per second,
so basically 50 times--
it's calculated every second 50 times.
Using the techniques of photogrammetry,
we were able to construct the 3D model,
with the high-impedance value in the lungs representing
the red and the low-impedance value
representing the blue color, thus creating a heat map out
of that.
Once we had the final representation ready,
we did classification using the convolution neural network.
NEIL: Yeah, as [INAUDIBLE] said, that we
used 16 electrodes and 50 cycles per second,
so that says that our technology was around 94%
accurate with the results.
As the data sets that we were using were the MRI scan images
and the heat map images of lungs from the hospitals and datas
that we got.
So around 550 images were used to create the data sets.
And we also had some consulting and mentors
in biotechnological field who gave us feedback.
And he was surely from Harvard Medical School
who actually helped us out with the whole thing.
LAURENCE: Nice.
Nice.
So just fabulous, fabulous work.
Please keep pushing it forward and productize this.
I think the world needs more and more things like this,
so I'm so impressed.
Congratulations.
SPEAKER 2: Thank you so much.
NEIL: Thank you so much.
ERICA: Congratulations.
Yeah, but we're going to pull in a live chat.
Let's see what everyone watching has to ask.
So one question is, how is it different
from other technologies in hospitals?
SPEAKER 2: That's an amazing question.
And we had that question as well.
What can we three students do that would
be better than something that's in hospitals?
And we realized that sometimes, everything great is usually
expensive, and that might not be the best thing.
So the machines in hospitals, like MRI scans
and CT scans-- they're very bulky, they're not portable,
and they're limited.
Because of that, there is often a very long waiting time
for a patient to actually be able to get a scan.
And we wanted to change that.
We wanted to add in an intermediary
where patients can get medical screening done
so that their conditions don't deteriorate
while they're waiting for getting a scan done.
NEIL: All right.
ERICA: That's great.
NEIL: Just adding a point for that--
as we also note, in [INAUDIBLE] times,
it's also very difficult for doctors
to actually interact with the patients.
And just to make it more easy to use, we came up with this band
that they could use on their chest
and that can be used by the patients themselves
to just monitor them--
monitor their own health conditions.
And that was one of the reasons why we actually integrated it
with a band.
ERICA: That's great.
Thank you so much for sharing your project with us.
We-- yeah, we just loved checking this traffic out,
watching your demo, see what you build.
We really want to see what's next.
And yeah, really appreciate you being with us here.
And thank you so much, Laurence, for joining us, as well.
LAURENCE: Oh the pleasure was all mine.
This was amazing.
Thank you.
NEIL: Thank you so much.
SPEAKER 2: Thank you so much.
ERICA: Thanks so much.
SPEAKER 4: Thank you.
Peace.
SPEAKER 2: Bye.
Bye, everyone.
Peace.
[MUSIC PLAYING]