January 4, 2022 • 20 mins
In this episode we welcome Davide Cirillo. He represents the Barcelona Supercomputing Center, a partner in iPC which, by the way, stands for Indivualized Peadiatric Cure. We talk about how explainable AI and machine learning is the key to tailor treatments for kids while minimizing the risks.
The iPC project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 826121.
See omnystudio.com/listener for privacy information.
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Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Intro (00:01):
This is a Technikon podcast.
Peter Balint (00:06):
There is a digital transformation all around us. We know
this from news reports, but even more so we know
this because we are a part of it. How many
of your daily activities are done on your computer or
mobile telephone? So what about the big picture, like treating
cancer in children? How has this changed for the better
in our digital world? I'm Peter Balint from Technikon, and
(00:29):
today we explore this very topic with Davide Cirillo from the
iPC project. He represents the Barcelona Supercomputing Centre, a partner
in iPC, which, by the way, stands for individualized paediatric cure. Doctors, clinicians, oncologists,
biomedical engineers and computer scientists are working together in iPC
(00:51):
to use data, human samples and artificial intelligence to tailor
treatments for kids while minimizing the risks. Let's have a listen. Thanks, Davide,
for coming on today.
Davide Cirillo (01:06):
Thank you. Thank you. It's a pleasure to be here.
Thank you for having me.
Peter Balint (01:10):
First, let's look at what iPC is trying to do,
but frame your answer around this quote and this is
from the iPC webpage
is in personalization."
Davide Cirillo (01:24):
Yes. So in this in this sentence, there are two
words that really stand out and and really - like - summarize
very well the goals of the iPC project, and those
words are personalization and future. So in the health domain,
by personalization, we generally refer to personalized medicine and we
(01:48):
choose our medical paradigm centered around the patient. So in
order to deliver better therapeutic strategies and the preventive solutions.
So have you ever noticed that if you have two
patients that have been diagnosed with the same condition and
you give exactly the same treatment to them, the outcome
(02:11):
of this treatment will be different. So maybe like in one
of two people that the drug is used is working
very well. But the other is maybe not working very much.
So the reason behind this is that the variability in
the human population is is huge. So we are very
(02:31):
different one from each other, and the personalized medicine approach
is actually looking for those differences in order to provide
better therapies, better diagnostic solutions. And so looking for individual
information like, for instance, demographic characteristics like age, sex, ethnicity,
(02:52):
the clinical history, maybe previous diseases, genetic factors like mutations
and also the socioeconomic context like, for instance, the I
don't know, the environmental conditions in which the person lives
or the psychosocial factors. And so this is about personalization.
And then we have the future of pediatric cancer and
(03:14):
and again, in the health domain, when we talk about
the future of biomedical research, we refer to the applications
of artificial intelligence and we are witnessing a digital transformation
of the health care. But I would say like a
digital transformation of our lives in general. So we have
(03:34):
artificial intelligence algorithms embedded in all the devices that we
are using from our smartwatches to the mobile phones. We
have algorithms that are able to recommend us products based
on our tastes and preferences, for instance in Amazon or
(03:54):
videos on on YouTube and stuff like that. So in
biomedicine and in particular in pediatric cancer research, we are
trying to work with those artificial intelligence systems in order
to realize this personalized medicine approach and to use those
algorithms and create those models in order to provide a better health service.
Peter Balint (04:22):
I see, so we could almost say that this is
using our digital world to sort of connect with the
world of cancer research or maybe even medicine in general
for better outcomes, so that in this case, children don't
have to suffer with treatments that don't necessarily work.
Davide Cirillo (04:38):
Exactly.
Peter Balint (04:40):
OK, so then we have to ask the question what's
the role of Barcelona Supercomputing Centre in iPC?
Davide Cirillo (04:49):
Yes. So the Barcelona Supercomputing Centre is the National Supercomputing
Centre in Spain, and we are experts in HPC, which
stands for high performance computing and which is basically the
use of computational resources with a high level of parallelism
and scalability . And one of those resources that the
(05:13):
bigger one is called MareNostrum 4, that is a supercomputer that
we host. Just to give you an idea of this,
the peak performance of this supercomputer is almost 14 petaflops,
which corresponds to more than the 13000 trillion operations per second.
So this is like a big machine that is really
(05:34):
able to run fast programs. Yeah. And so this is
MareNostrum 4 . But the best known supercomputing center will soon
host the MareNostrum 5 , which is one of the three
so-called pre-exascale supercomputers selected in 2019 by an initiative that
(05:56):
is called the Euro HPC. And so do you remember
the peak performance of almost 14 petaflops of MareNostrum 4 , well,
with MareNostrum 5, these will become 200 petaflops. So these machines are
growing . Yes.
Peter Balint (06:09):
Oh, wow.
Davide Cirillo (06:10):
So yes, this is this is what we do. We
we use those computational resources in order to provide these
HPC resources to different communities and to generate knowledge in
in different areas such as, for instance, engineering, climatology and,
of course, life sciences. So our role in, the role
(06:33):
of BSC in iPC is to develop research in artificial
intelligence using these HPC resources and provide, of course, the
computational infrastructure required for such a big project.
Peter Balint (06:46):
Mm-Hmm. You're using artificial intelligence then in iPC, and I
think many people might not understand that, and they might immediately
jump to the fact that they think that there are
ethical issues associated with this. How is that dealt with
in iPC? Or is that even the case?
Davide Cirillo (07:05):
It definitely is the case for many reasons, and not
only for the aspects related to artificial intelligence, but also those.
So in general ethics is fundamental to the project iPC
and as I said, like in general to the applications
of artificial intelligence to health, so the issue is that -
(07:29):
thinking of a form of medicine that goes hand-in-hand with
artificial intelligence is fueling a very strong debate nowadays. So
there are some opinions free stance on the possible dystopic
future for healthcare where, you know, machines takeover doctors jobs
and things like that. But actually like the most likely
(07:52):
scenario for the next decades is actually an increase in
flourishing human machine interaction, where a human doctor will be
accountable for any decision about the patient without a direct..
and in particular without an unauthorized intervention of any machine.
(08:13):
So yes, the ethical aspects are a crucial tool to
this type of projects, and BSC has dedicated resources to
do ethics in the project, and we also participated in
the many workshops devoted to these themes organized by the consortium.
Then there are an important aspect that is more related
(08:34):
to the actual development of artificial intelligence is that all
the data that we can use to train artificial intelligence
systems can also be extremely biased towards certain groups. So
we really have to be careful that we are not
excluding -that the data that we are using is not like
excluding under-served groups and minorities and the situations of this kind.
(09:01):
Biases can be hidden in all the steps of the
lifecycle of the artificial intelligence development. For instance, we can
have historical biases in the documents and data that can
retain cultural aspects, beliefs of stereotypes. We can have the
representation biases if we like select a specific group of patients,
(09:23):
excluding others. We can have biases the in the way
the values and the parameters of the data that we
are collecting are measured and we can have different biases
in the way those systems are evaluated. And finally deployed
in the real world, because you can release a model,
but depending where you are deploying the system, who is
(09:47):
going to use it? You can create a bias also there.
So it's really like a complex landscape of biases and
issues that we really have to be very careful to
address when we start an artificial intelligence based project like iPC,
for instance.
Peter Balint (10:08):
Yeah. And it seems like ethics is a really necessary
part of the framework in a project like this.
Davide Cirillo (10:13):
Yes.
Peter Balint (10:14):
So in many existing personalized treatment plans for cancer, large
datasets are used to help inform clinicians about treatment plans.
Why is this not the case when examining the paediatric
patient population? And what does iPC propose to do about this?
Davide Cirillo (10:34):
Yes. So the the the main conundrum here is that
many pediatric tumours are rare and so a rare disease by definition,
affects a small number of individuals compared to the general population.
And so as a consequence, the data sets that that we,
(10:55):
that we have are characterized by being of small sizes
and and so like we have like few data points,
and these limit our ability to be statistically confident about
any finding that we might identify in our in our research.
So one solution to overcome this limitation is to augment
(11:19):
the data by generating synthetic instances. And so indeed, BSC
is very much involved in this aspect. This is a
very advanced application of artificial intelligence for synthetic data generation
and and this is an emerging dominant A.I. solutions for
personalized medicine since it enables to address those types of challenges. As,
(11:45):
for instance, creating the data volumes that are needed to
deliver accurate results and and also like correcting for possible biases,
as we were discussing before and also complying with increasingly
restrictive privacy regulations. So and this is, of course, very
much relevant for pediatric cancer research. I'm talking about privacy
(12:08):
here because you have to imagine that when you are
creating a synthetic version of a patient, you are kind
of detaching from the real person. So you can then
work on the on these digital twin and you can,
for instance, test different perturbations. You can see like if
(12:29):
a drug is actually working or not working, but without
doing this on the real patient, but only like in
a virtual environment inside a computer. So this is this
is like the main idea, and this is like the
main advantage of the synthetic data generation. And regarding to this,
(12:49):
there is an entire field of research that is working
on on this particular area. And this is because synthetic
data generation uses mainly deep learning nowadays. And the problem
with deep learning is that this is something called like a
black box. We we hear more and more about this
(13:12):
this term, and a black box is basically a system
in which, like all the complexities and the nonlinearities that
that are used to model the data are not intelligible
to humans. And and so there is a lot of
research focusing on the explainability of those systems, the explainability
(13:33):
of artificial intelligence, which basically means how to convert a
black box into a white box so something that we
can actually understand. Like, we like the mechanism behind the
learning process and behind the algorithm. We can really see
what is going on and why the machine reached a
certain solution, a certain outcome.
Peter Balint (13:55):
I see. So it's not just about the machine making
a decision. You also have to understand why it made
a decision.
Davide Cirillo (14:03):
Exactly.
Peter Balint (14:03):
And when we talk about data and the lack of data,
I'm guessing that besides data just not being there because
there aren't so many cases, because pediatric cancer is rare.
In the cases where there is data, it may be
protected and difficult to access, perhaps due to the laws
that protect minors to a greater extent than adults.
Davide Cirillo (14:28):
Yes, exactly. We also have to consider this. So we
are talking about children. And of course, in this case,
the regulation is much stricter. And I mean, this is
this is understandable. And of course, it's completely right. One of
the main issues most of the time, and this is
not just related to pediatric cancer research, but in general
(14:50):
is is that those regulations are very strict on data
access and the management of the sensible data, also for
the people that are working with this data. So it's
generally very difficult to, you know, to to share for instance the
data among different hospitals or among different research institutes. And
(15:13):
so in my opinion, personally speaking, I think that creating
a safe environment for data sharing is crucial to to
advance research in this field. So all the regulation is ok
we all agree, that's that they are right and they
must be there, but we also have to guarantee that
the researchers can actually work. So there are some solutions
(15:35):
to this, in particular in the artificial intelligence area. One
of them is called federated learning. So basically, imagine that
you have many different hospitals in different countries and and
each hospital has its own constraints to data sharing. So
instead of bringing the data to a centralized place, what
(15:57):
Federated Learning proposes is to bring the models in situ
so drained the artificial intelligence system inside the hospital and
share only the parameters that have been learned during these
local training. So in this way, basically, you can like train,
a general model that is accounting for all the data
(16:21):
stays like in the in the periphery, stay in those
places and doesn't move, but still you can learn from
from from it.
Peter Balint (16:30):
So previously you mentioned in iPC, the virtual patient or
the digital twin, and this is used to test treatments
while preventing any harm to any humans. Tell us more
about this.
Davide Cirillo (16:43):
Yes, a digital twin, is a synthetic version of some characteristics
of a patient. So I think it's important to to
say that when we say digital twins, we do not
mean like a digital physical version of a person. You
know what this is what the probably most of people
think when you say digital twin. And what we are,
(17:06):
what we are simulating are specific characteristics. So the scale
is important because we can simulate, you know, the macroscopic
or the microscopic. And so and there are different modeling
approaches depending on the on the scale that is more
adequate to do the things that you are doing, what
we want to study so we can produce multicellular systems like,
(17:28):
for instance, pieces of tissue using coarse grain simulations with
a lower resolution. Or we can go much more finer
and we can free sensory produce the expression levels of
the genes that are sitting on the DNA of a
patient with a very high resolution. So no matter the
scale all the time, what we can do is basically
(17:52):
to test what if scenarios. So again, if we if
we have like if we can reproduce, you can simulate
something we can then test perturbate and see what is the
effect inside a computer and not inside the real patient.
Peter Balint (18:07):
So if you look ahead a bit, what is the
best outcome for iPC? And I mean not only from
the domain of science and medicine, but also from the
side of pediatric patients and their families?
Davide Cirillo (18:21):
Yes. So the the best outcomes for iPC would be,
first of all, to generate knowledge about the pediatric tumors
that are under study, especially because most of them present
many unanswered questions. And the aspects that need to be
further studied. So this is an important aspect of this
(18:43):
of this project that is fostering the research in specific
pediatric tumors, then, is to provide an infrastructure where all
the data and the tools that have been created can
be accessed in a secure and privacy preserving environment and
also reused for the other pediatric tumors and also other
(19:06):
rare diseases as well. And finally, to to find the
theoretical but also practical solutions to technological limitations that are
related to the typical scenarios of personalized medicine applications or artificial intelligence,
and we talked about that before like, for instance, the
small sized datasets or the explainability of the black box models.
(19:30):
So all those are definitely would be the best outcomes
that we can have from a project like iPC.
Peter Balint (19:38):
OK, well, it sounds like iPC is a really important
project for making inroads into research and treatment options when
it comes to pediatric cancer. And I want to say
thank you for taking some time with us today and
sharing your knowledge in the project and how it works.
Davide Cirillo (19:55):
Thank you so much. It has been a pleasure.
Peter Balint (19:57):
For more information about iPC, go to ipc-project.eu . The
iPC project has received funding from the European Union's Horizon
2020 Research and Innovation Programme under grant agreement Number 826121 .
This is a Technikon podcast.
Peter Balint (00:06):
There is a digital transformation all around us. We know
this from news reports, but even more so we know
this because we are a part of it. How many
of your daily activities are done on your computer or
mobile telephone? So what about the big picture, like treating
cancer in children? How has this changed for the better
in our digital world? I'm Peter Balint from Technikon, and
(00:29):
today we explore this very topic with Davide Cirillo from the
iPC project. He represents the Barcelona Supercomputing Centre, a partner
in iPC, which, by the way, stands for individualized paediatric cure. Doctors, clinicians, oncologists,
biomedical engineers and computer scientists are working together in iPC
(00:51):
to use data, human samples and artificial intelligence to tailor
treatments for kids while minimizing the risks. Let's have a listen. Thanks, Davide,
for coming on today.
Davide Cirillo (01:06):
Thank you. Thank you. It's a pleasure to be here.
Thank you for having me.
Peter Balint (01:10):
First, let's look at what iPC is trying to do,
but frame your answer around this quote and this is
from the iPC webpage
is in personalization."
Davide Cirillo (01:24):
Yes. So in this in this sentence, there are two
words that really stand out and and really - like - summarize
very well the goals of the iPC project, and those
words are personalization and future. So in the health domain,
by personalization, we generally refer to personalized medicine and we
(01:48):
choose our medical paradigm centered around the patient. So in
order to deliver better therapeutic strategies and the preventive solutions.
So have you ever noticed that if you have two
patients that have been diagnosed with the same condition and
you give exactly the same treatment to them, the outcome
(02:11):
of this treatment will be different. So maybe like in one
of two people that the drug is used is working
very well. But the other is maybe not working very much.
So the reason behind this is that the variability in
the human population is is huge. So we are very
(02:31):
different one from each other, and the personalized medicine approach
is actually looking for those differences in order to provide
better therapies, better diagnostic solutions. And so looking for individual
information like, for instance, demographic characteristics like age, sex, ethnicity,
(02:52):
the clinical history, maybe previous diseases, genetic factors like mutations
and also the socioeconomic context like, for instance, the I
don't know, the environmental conditions in which the person lives
or the psychosocial factors. And so this is about personalization.
And then we have the future of pediatric cancer and
(03:14):
and again, in the health domain, when we talk about
the future of biomedical research, we refer to the applications
of artificial intelligence and we are witnessing a digital transformation
of the health care. But I would say like a
digital transformation of our lives in general. So we have
(03:34):
artificial intelligence algorithms embedded in all the devices that we
are using from our smartwatches to the mobile phones. We
have algorithms that are able to recommend us products based
on our tastes and preferences, for instance in Amazon or
(03:54):
videos on on YouTube and stuff like that. So in
biomedicine and in particular in pediatric cancer research, we are
trying to work with those artificial intelligence systems in order
to realize this personalized medicine approach and to use those
algorithms and create those models in order to provide a better health service.
Peter Balint (04:22):
I see, so we could almost say that this is
using our digital world to sort of connect with the
world of cancer research or maybe even medicine in general
for better outcomes, so that in this case, children don't
have to suffer with treatments that don't necessarily work.
Davide Cirillo (04:38):
Exactly.
Peter Balint (04:40):
OK, so then we have to ask the question what's
the role of Barcelona Supercomputing Centre in iPC?
Davide Cirillo (04:49):
Yes. So the Barcelona Supercomputing Centre is the National Supercomputing
Centre in Spain, and we are experts in HPC, which
stands for high performance computing and which is basically the
use of computational resources with a high level of parallelism
and scalability . And one of those resources that the
(05:13):
bigger one is called MareNostrum 4, that is a supercomputer that
we host. Just to give you an idea of this,
the peak performance of this supercomputer is almost 14 petaflops,
which corresponds to more than the 13000 trillion operations per second.
So this is like a big machine that is really
(05:34):
able to run fast programs. Yeah. And so this is
MareNostrum 4 . But the best known supercomputing center will soon
host the MareNostrum 5 , which is one of the three
so-called pre-exascale supercomputers selected in 2019 by an initiative that
(05:56):
is called the Euro HPC. And so do you remember
the peak performance of almost 14 petaflops of MareNostrum 4 , well,
with MareNostrum 5, these will become 200 petaflops. So these machines are
growing . Yes.
Peter Balint (06:09):
Oh, wow.
Davide Cirillo (06:10):
So yes, this is this is what we do. We
we use those computational resources in order to provide these
HPC resources to different communities and to generate knowledge in
in different areas such as, for instance, engineering, climatology and,
of course, life sciences. So our role in, the role
(06:33):
of BSC in iPC is to develop research in artificial
intelligence using these HPC resources and provide, of course, the
computational infrastructure required for such a big project.
Peter Balint (06:46):
Mm-Hmm. You're using artificial intelligence then in iPC, and I
think many people might not understand that, and they might immediately
jump to the fact that they think that there are
ethical issues associated with this. How is that dealt with
in iPC? Or is that even the case?
Davide Cirillo (07:05):
It definitely is the case for many reasons, and not
only for the aspects related to artificial intelligence, but also those.
So in general ethics is fundamental to the project iPC
and as I said, like in general to the applications
of artificial intelligence to health, so the issue is that -
(07:29):
thinking of a form of medicine that goes hand-in-hand with
artificial intelligence is fueling a very strong debate nowadays. So
there are some opinions free stance on the possible dystopic
future for healthcare where, you know, machines takeover doctors jobs
and things like that. But actually like the most likely
(07:52):
scenario for the next decades is actually an increase in
flourishing human machine interaction, where a human doctor will be
accountable for any decision about the patient without a direct..
and in particular without an unauthorized intervention of any machine.
(08:13):
So yes, the ethical aspects are a crucial tool to
this type of projects, and BSC has dedicated resources to
do ethics in the project, and we also participated in
the many workshops devoted to these themes organized by the consortium.
Then there are an important aspect that is more related
(08:34):
to the actual development of artificial intelligence is that all
the data that we can use to train artificial intelligence
systems can also be extremely biased towards certain groups. So
we really have to be careful that we are not
excluding -that the data that we are using is not like
excluding under-served groups and minorities and the situations of this kind.
(09:01):
Biases can be hidden in all the steps of the
lifecycle of the artificial intelligence development. For instance, we can
have historical biases in the documents and data that can
retain cultural aspects, beliefs of stereotypes. We can have the
representation biases if we like select a specific group of patients,
(09:23):
excluding others. We can have biases the in the way
the values and the parameters of the data that we
are collecting are measured and we can have different biases
in the way those systems are evaluated. And finally deployed
in the real world, because you can release a model,
but depending where you are deploying the system, who is
(09:47):
going to use it? You can create a bias also there.
So it's really like a complex landscape of biases and
issues that we really have to be very careful to
address when we start an artificial intelligence based project like iPC,
for instance.
Peter Balint (10:08):
Yeah. And it seems like ethics is a really necessary
part of the framework in a project like this.
Davide Cirillo (10:13):
Yes.
Peter Balint (10:14):
So in many existing personalized treatment plans for cancer, large
datasets are used to help inform clinicians about treatment plans.
Why is this not the case when examining the paediatric
patient population? And what does iPC propose to do about this?
Davide Cirillo (10:34):
Yes. So the the the main conundrum here is that
many pediatric tumours are rare and so a rare disease by definition,
affects a small number of individuals compared to the general population.
And so as a consequence, the data sets that that we,
(10:55):
that we have are characterized by being of small sizes
and and so like we have like few data points,
and these limit our ability to be statistically confident about
any finding that we might identify in our in our research.
So one solution to overcome this limitation is to augment
(11:19):
the data by generating synthetic instances. And so indeed, BSC
is very much involved in this aspect. This is a
very advanced application of artificial intelligence for synthetic data generation
and and this is an emerging dominant A.I. solutions for
personalized medicine since it enables to address those types of challenges. As,
(11:45):
for instance, creating the data volumes that are needed to
deliver accurate results and and also like correcting for possible biases,
as we were discussing before and also complying with increasingly
restrictive privacy regulations. So and this is, of course, very
much relevant for pediatric cancer research. I'm talking about privacy
(12:08):
here because you have to imagine that when you are
creating a synthetic version of a patient, you are kind
of detaching from the real person. So you can then
work on the on these digital twin and you can,
for instance, test different perturbations. You can see like if
(12:29):
a drug is actually working or not working, but without
doing this on the real patient, but only like in
a virtual environment inside a computer. So this is this
is like the main idea, and this is like the
main advantage of the synthetic data generation. And regarding to this,
(12:49):
there is an entire field of research that is working
on on this particular area. And this is because synthetic
data generation uses mainly deep learning nowadays. And the problem
with deep learning is that this is something called like a
black box. We we hear more and more about this
(13:12):
this term, and a black box is basically a system
in which, like all the complexities and the nonlinearities that
that are used to model the data are not intelligible
to humans. And and so there is a lot of
research focusing on the explainability of those systems, the explainability
(13:33):
of artificial intelligence, which basically means how to convert a
black box into a white box so something that we
can actually understand. Like, we like the mechanism behind the
learning process and behind the algorithm. We can really see
what is going on and why the machine reached a
certain solution, a certain outcome.
Peter Balint (13:55):
I see. So it's not just about the machine making
a decision. You also have to understand why it made
a decision.
Davide Cirillo (14:03):
Exactly.
Peter Balint (14:03):
And when we talk about data and the lack of data,
I'm guessing that besides data just not being there because
there aren't so many cases, because pediatric cancer is rare.
In the cases where there is data, it may be
protected and difficult to access, perhaps due to the laws
that protect minors to a greater extent than adults.
Davide Cirillo (14:28):
Yes, exactly. We also have to consider this. So we
are talking about children. And of course, in this case,
the regulation is much stricter. And I mean, this is
this is understandable. And of course, it's completely right. One of
the main issues most of the time, and this is
not just related to pediatric cancer research, but in general
(14:50):
is is that those regulations are very strict on data
access and the management of the sensible data, also for
the people that are working with this data. So it's
generally very difficult to, you know, to to share for instance the
data among different hospitals or among different research institutes. And
(15:13):
so in my opinion, personally speaking, I think that creating
a safe environment for data sharing is crucial to to
advance research in this field. So all the regulation is ok
we all agree, that's that they are right and they
must be there, but we also have to guarantee that
the researchers can actually work. So there are some solutions
(15:35):
to this, in particular in the artificial intelligence area. One
of them is called federated learning. So basically, imagine that
you have many different hospitals in different countries and and
each hospital has its own constraints to data sharing. So
instead of bringing the data to a centralized place, what
(15:57):
Federated Learning proposes is to bring the models in situ
so drained the artificial intelligence system inside the hospital and
share only the parameters that have been learned during these
local training. So in this way, basically, you can like train,
a general model that is accounting for all the data
(16:21):
stays like in the in the periphery, stay in those
places and doesn't move, but still you can learn from
from from it.
Peter Balint (16:30):
So previously you mentioned in iPC, the virtual patient or
the digital twin, and this is used to test treatments
while preventing any harm to any humans. Tell us more
about this.
Davide Cirillo (16:43):
Yes, a digital twin, is a synthetic version of some characteristics
of a patient. So I think it's important to to
say that when we say digital twins, we do not
mean like a digital physical version of a person. You
know what this is what the probably most of people
think when you say digital twin. And what we are,
(17:06):
what we are simulating are specific characteristics. So the scale
is important because we can simulate, you know, the macroscopic
or the microscopic. And so and there are different modeling
approaches depending on the on the scale that is more
adequate to do the things that you are doing, what
we want to study so we can produce multicellular systems like,
(17:28):
for instance, pieces of tissue using coarse grain simulations with
a lower resolution. Or we can go much more finer
and we can free sensory produce the expression levels of
the genes that are sitting on the DNA of a
patient with a very high resolution. So no matter the
scale all the time, what we can do is basically
(17:52):
to test what if scenarios. So again, if we if
we have like if we can reproduce, you can simulate
something we can then test perturbate and see what is the
effect inside a computer and not inside the real patient.
Peter Balint (18:07):
So if you look ahead a bit, what is the
best outcome for iPC? And I mean not only from
the domain of science and medicine, but also from the
side of pediatric patients and their families?
Davide Cirillo (18:21):
Yes. So the the best outcomes for iPC would be,
first of all, to generate knowledge about the pediatric tumors
that are under study, especially because most of them present
many unanswered questions. And the aspects that need to be
further studied. So this is an important aspect of this
(18:43):
of this project that is fostering the research in specific
pediatric tumors, then, is to provide an infrastructure where all
the data and the tools that have been created can
be accessed in a secure and privacy preserving environment and
also reused for the other pediatric tumors and also other
(19:06):
rare diseases as well. And finally, to to find the
theoretical but also practical solutions to technological limitations that are
related to the typical scenarios of personalized medicine applications or artificial intelligence,
and we talked about that before like, for instance, the
small sized datasets or the explainability of the black box models.
(19:30):
So all those are definitely would be the best outcomes
that we can have from a project like iPC.
Peter Balint (19:38):
OK, well, it sounds like iPC is a really important
project for making inroads into research and treatment options when
it comes to pediatric cancer. And I want to say
thank you for taking some time with us today and
sharing your knowledge in the project and how it works.
Davide Cirillo (19:55):
Thank you so much. It has been a pleasure.
Peter Balint (19:57):
For more information about iPC, go to ipc-project.eu . The
iPC project has received funding from the European Union's Horizon
2020 Research and Innovation Programme under grant agreement Number 826121 .
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