Okay, I'm Miguel, so I'm presenting how open-source software is shaping the future of healthcare.
I'm a senior research software engineer at the Advanced Research Computer Center at UCL.
So this is mainly the overview.
We're going to look at some challenges that we faced from bench to bedside, some use cases,
and a community we are trying to build around open software in healthcare.
First I start with my, I guess, kind of journey.
I'm Mexican, based in the UK.
So one thing I want to point out is that, like, nearly 20 years ago,
as I go as far as using Ubuntu and GitHub,
because I know a lot to use MATLA,
I mean, I buy the private CD using MATLA, in fact, in Mexico.
But then I realize, how can I be more kind of ethic,
and more, I guess,
because, yeah, like, not breaking the rules of the licensing.
So I start to look into a junior update, all the kind of libraries in the BR and KDA,
you know, then I find out, GitHub,
and then I start working mainly on kind of engineering war.
I try to dance with robots,
and then I realize I want to do more of this kind of war,
like, how can I combine computers with software,
and I start with my PhD at Birmingham University,
doing human robot interactions,
still working on kind of this war of open software, open science.
I publish my thesis on the open science thesis,
and then I start doing a postdoc in King's College,
where I was trying to do kind of synthetic babies,
using mainly using, you know,
kind of techniques of diffusion models,
doing predicts around AI enabled ecocardiography,
and all the predicts around ultrasound needle tracking.
Just two years ago, I joined UCL,
where I basically working on predicts around software,
or clinical translation,
we do praise around clinical engineering.
So you can navigation using Python libraries,
doing war with multimodal imaging,
some real-time AI pipelines,
and now we are kind of thinking
how we can make use of all these experience,
we have been, I guess, collecting through different years,
and we are colleagues.
So there's a big challenge when you go from bench
to bedside.
This is kind of the balance when you are working
with the latest generation of carbon software,
with a state of AI models,
so you want to work maybe with a kind of lay system,
but then you need to also look at the other side
when you are kind of thinking,
how can I regulate my software that can be maybe
in your patient?
So that's where we need to think carefully,
how we made use of these latest technologies,
and latest models,
because maybe, yeah, definitely it's improving
kind of the state of the accuracy of the model,
but that may be not good for the patient,
so we need to go into all these regulations
for medical, a software device,
so AI systems or medical devices,
and we need people from pure mass aspects,
so it's like a huge, I guess,
and therefore, it's not only one person that goes from,
you know, like doing a fun project to go
into helping and improving the health of persons,
so this is like a common pipeline
when you start developing your medical AI device,
you validate your software,
a generalized interoperability,
longevity and liability,
and then you finally go into the application
of your software, right?
But then you start going deep in
why I need to build my software as a medical device,
so it's true how to be a challenging
you confront like a, you know,
say not clinical or a Q-s aspect background,
so you need to navigate all the regulations,
all the standards, and it's not a straightforward,
and all this, I guess, on the payment,
you need to actually buy the licenses
to get access to these kind of, you know,
frameworks, but luckily you, so Google the standards,
you can find interesting papers on how people
are kind of using standards to produce,
software as medical devices.
There are also some guidelines on how we can produce,
like follow good software practices, you see FDA,
so what, what, for example, happens is
when you have a new data set, you need to validate,
sorry, you need to validate,
you need to train the model, you need to engage with your clinical colleague,
and then kind of validate all this pipeline,
so request lots of interaction as well.
So, I mean, this is kind of the general pipeline
when you are trying to implement new models
into or producing a new device,
and this is kind of,
so landscape when you are trying to incorporate
the standards, so it's very challenging,
that's something I don't understand at this stage,
but I just noticed that all the lights
just point to testing, so that means
the unit testing is very important for your development,
because you are creating a new model,
then you need to test that model with maybe a small data set,
and then with those, that is more data set than you,
you are sure, or make sure you mitigate any risk
of your kind of pipeline.
So, it's like a huge, I guess, fear to navigate,
but just to give you an overview of what you say,
but you are building software as medical devices.
Okay, so now let's go a bit of into the use cases,
so I would like to talk about this feature
about some image-in synthesis.
I mean, just for context,
usually, I guess, plain language,
go to escans, and they want to understand
the healthiness of the baby.
So, if we want to also,
I would like to take these okay conditions
to isolate this kind of procedure,
then we need to get access to these data sets
to train our models.
I was starting looking into data sets,
because there are different challenges
on the, I guess, biometrics,
but one main challenge is the data sets.
So, there are few public data sets
that we can, I guess, use to train your models.
And that's where we are thinking,
because, definitely, there's a few data sets
because of the data privacy,
we want to protect patients.
We want also anonymize or sell the anonymous
the data information of patients.
That's understandable,
but if you want to push the boundaries
or how we can maybe improve the model
to detect any kind of biometric disease,
then one way to do on one way to think about
this is maybe using synthetic data sets.
So, for example, in the case of this model data sets,
which is an open data set,
you can analyze the different,
different image brain plays for features.
You can then do a bit of anonymization,
ammentation of the model,
10-year pilot,
and finally, engage with your clinical
collaborative to validate the quality
of that kind of image synthesis.
So, that's where we are trying to do here.
I mean, I don't want to go into the test,
but we've been using some genetic adversarial networks,
some deformers based guns as well.
The important thing here is how we engage with
clinical clinicians to validate our models.
So, for example, this is one data set of real images,
some using guns, one methodology,
and other transform based guns.
So, I guess, I guess you need to have that
by a clinician to judge it is a reliable
and clinically acceptable images.
So, engineers, like I said,
will say, oh, that looks good to me,
but then you go for one expert,
they kind of validate the quality of that image.
So, that's where we start kind of putting the clinical
in the loop to validate the quality of that image.
So, yeah, and also evaluating the metrics
and the quality of the image as well.
And then we have some kind of future work
and how we can maybe go from one plane to the other plane
or also using data sets from, for example,
there's African data set ultrasound.
So, there are different parts of Africa
that we can change kind of the characteristics
of the ultrasound physics as well.
And we can also help to produce,
help to produce this kind of more reliable data sets
that can help other communities.
So, with that, we then have some papers
and middle.
We also developed Python-based library
where we are trying to create like a community
as well.
So, we are discussing how we can use this new data set.
So, we can maybe produce more kind of open data sets
that others can make use of that to improve
and train the models.
So, that's one example.
The other example is about real-time AI applications.
We'll be collaborating with Nvidia
creating these kind of pilots
on how we train models, how we build the applications,
how we validate very, very, very daily.
And the application and how we deploy that into the medical device.
That's, I mean, Nvidia is really good at producing open source
of where SDKs.
And I think that's also one of the selling points of the hardware
because they produce a nice documentation there.
I guess, I mean, you need to actually build that relationship
but the software is working.
And I guess the downside is that you need to have funding
to buy these expensive GPUs.
The other thing is that you need to also buy hardware
that is that can be used in the clinic.
So, you have like a medical computer devices.
So, that's very expensive.
I mean, does the other downside of using this,
I guess, expensive hardware?
They develop this holoscan SDK basically
with this holoscan SDK.
You can stream video.
Then do some formatting.
Then do multi-eye.
Some segmentation and visualize the open source.
They have also nice kind of documentation
on how you create your Python library,
your kind of configuration files.
And then, without we start working in a project
on doing endoscopic tutorial.
So, very basically, the camera that goes into your nose
and start looking into the kind of tissue
to find tumors, I guess, in the brain.
Maybe I'm not going to play the video, but basically,
but basically, it's basically helping the clinicians
to identify tumors or train
also clinicians to go into more accurate,
I guess, identification of these areas
that may lead to some diseases.
So, we develop open source library.
And we have also nice documentation,
how we do, how we onboard new students,
new clinicians, how they can produce
in a very basic example,
to bring your own model.
And yeah, how they can debug the prototype as well.
So, we work on a model doing multi-eye,
meaning that you do some segmentation
and some landmark clean as well.
I mean, it's not visible, but that means you do this
multiple multi-head models.
We also do some phase model as well.
So, depends on where the clinician is in the operation.
The model can do that.
And then, we develop this kind of multi-eye models as well.
All these is open source.
I mean, you understand, you can dive into that.
And then, we also help, I guess,
researchers to think about how to show good practices
on how to contribute, how to create pull requests.
You know, review it, magic when everybody is happy.
And then, yeah, as I was saying, onboard in new users
at the new models, creating new PRs,
how to release the software libraries as well
doing a standardized documentation and what we are doing.
So, we have a very nice virtual control.
All the other nice projects about what are called ocular,
which is an open source care using a state of the AI
for real-time monitoring and diagnosis.
What I mean by that is basically using images from,
from topology.
So, there are different modalities here.
But I was going to focus on one particular example
that we are working now with our clinical lecturer in UCL.
He's interested in understanding the NISTASMA,
which is an eye movement disorder.
I mean, he wants to make use of this real-time AI application
in the emergency department,
because what he says is that every time anyone with this disorder
goes into the emergency, they need to find,
I guess, an expert, there's nobody there.
So, one way to do that is maybe using a mobile application
that he can use to kind of out to detect any kind of diseases.
So, we start working on this similar pipeline,
developing a trained optimized models,
developing the application by finding the model
and deploying into the mobile device.
We have this library, it's called ready,
it's real-time AI for NISTASMA's.
I mean, it's currently on developing
if anyone interested, please let me know.
I can add you to the repo, because it's still a private,
but all the worries is open.
But, particularly for this one, because we are working on our paper,
I guess, what we publish or prepping,
we will put also the library open.
But, similar to earlier other projects,
we have documentation,
we're going to onboard people and nice forums
to discuss about data sets, new papers,
and what we can do to improve that.
This is the quick demo, you see in UNET,
and more of your data set.
UNET, I just go to GitHub find a very basic model.
Implemented it.
Also, Google, how can I get data sets
from AI segmentation?
Like Splera, I find data called Mobius.
And I train UNET, and then deploy UNET
to do this kind of real-time AI inference of this segmentation.
So, why you can see here is, I guess,
the segmentation of the AI is that crazy kind of tracking
and that's useful for the Ocaski or my clinical collaborator
to understand how we can make use of this application
in the image settings.
We have plans to create like a guidance
to help clinicians to kind of the better position
in illumination and clarity of the image
when you are using this application
to implement new and some more modern segmentation models
like UNET, BIT or Visual Transformers.
We are working on that as well.
This is open source.
I mean, you have interest, you can dive it there as well.
And finally, just close.
We are building a communicable open source
of where in healthcare.
Last year, we organized this workshop open source of workforce.
So, you can technologies, we invite people
from industry and academia.
We discuss challenges of how open source
of where it is used to create clinical impact
with academics, industry partners
and how we can create community.
We have some posters as well last year
and we plan into organized a new workshop this year.
So, if anyone is interested, please let me know.
I can share more details.
So, the workshop is going to call
healing through collaboration.
We think we already have open data sex, open access,
open healthcare resources.
We are planning to do open review or papers.
I found some open regulatory templates.
Maybe we are missing something else.
Maybe you want to join us.
Let me know.
Yeah, and that's basically it.
So, the takeaways are,
there are definitely various challenges
on translating research to bench to bedside.
We show some use cases on synthetic data
on real-time AI-driven diagnosis.
And we can, I feel we can do the,
we can share the future of healthcare using open source software.
By contributing to,
to increase the high quality educational resources.
And, yeah, release your open source
and your models on high quality standards.
Yeah.
Thank you.