A predictive surrogate model of blood haemodynamics for patient-specific carotid artery stenosis
Journal of The Royal Society Interface,
Год журнала:
2025,
Номер
22(224)
Опубликована: Март 1, 2025
In
this
study,
the
haemodynamic
factors
inside
patient-specific
carotid
artery
with
stenosis
are
evaluated
via
a
predictive
surrogate
model.
The
technique
of
proper
orthogonal
decomposition
(POD)
is
used
for
reducing
order
main
model
and
consequently,
long
short-term
memory
employed
prediction
blood
flow
parameters,
i.e.
velocity
pressure
along
stenosis.
efficiency
proposed
machine
learning
has
been
in
arteries
with/without
Besides,
reconstruction
error
analysis
performed
different
POD
mode
numbers.
Our
results
demonstrate
that
value
at
stages
cardiac
cycle
great
impact
on
method
estimation
haemodynamics.
presence
intensifies
complexity
flow,
magnitude
errors
increased
when
exists
artery.
Язык: Английский
A predictive surrogate model based on linear and nonlinear solution manifold reduction in cardiovascular FSI: A comparative study
Computers in Biology and Medicine,
Год журнала:
2025,
Номер
189, С. 109959 - 109959
Опубликована: Март 5, 2025
This
study
investigates
the
fluid-structure
interaction
(FSI)
simulation
of
abdominal
aorta,
with
a
particular
focus
on
hemodynamic
alterations
induced
by
aneurysmal
deformations.
The
behavior
within
aorta
is
highly
dependent
geometric
characteristics
aneurysm,
necessitating
use
patient-specific
models
to
ensure
accurate
predictions.
primary
objective
this
research
enhance
predictive
capability
flow
and
structural
indices
in
complex
FSI
biomechanical
setting
under
varying
physiological
conditions,
namely
rest
exercise
states.
paper
presents
comparative
analysis
between
two
distinct
yet
promising
surrogate
models:
Proper
Orthogonal
Decomposition
coupled
Long
Short-Term
Memory
(POD
+
LSTM)
Convolutional
Neural
Network
combined
(CNN
LSTM).
methodology,
model
selection,
performance
are
discussed
detail,
providing
insights
into
efficacy
limitations
each
approach
context
personalized
cardiovascular
simulations.
Язык: Английский
Linear Surrogate Modelling for Predicting Hemodynamic in Carotid Artery Stenosis During Exercise Conditions
Chinese Journal of Physics,
Год журнала:
2025,
Номер
unknown
Опубликована: Янв. 1, 2025
Язык: Английский
Estimation of the fuel mixing of annular extruded fuel multi-jets in cavity flame holder at the supersonic combustion chamber via predictive surrogate model
Engineering Analysis with Boundary Elements,
Год журнала:
2024,
Номер
163, С. 369 - 377
Опубликована: Март 27, 2024
Язык: Английский
Computational Modeling Approach to Profile Hemodynamical Behavior in a Healthy Aorta
Bioengineering,
Год журнала:
2024,
Номер
11(9), С. 914 - 914
Опубликована: Сен. 12, 2024
Cardiovascular
diseases
(CVD)
remain
the
leading
cause
of
mortality
among
older
adults.
Early
detection
is
critical
as
prognosis
for
advanced-stage
CVD
often
poor.
Consequently,
non-invasive
diagnostic
tools
that
can
assess
hemodynamic
function,
particularly
aorta,
are
essential.
Computational
fluid
dynamics
(CFD)
has
emerged
a
promising
method
simulating
cardiovascular
efficiently
and
cost-effectively,
using
increasingly
accessible
computational
resources.
This
study
developed
CFD
model
to
aorta
geometry
tetrahedral
polyhedral
meshes.
A
healthy
was
modeled
with
mesh
sizes
ranging
from
0.2
1
mm.
Key
parameters,
including
blood
pressure
waveform,
difference,
wall
shear
stress
(WSS),
associated
parameters
like
relative
residence
time
(RRT),
oscillatory
index
(OSI),
endothelial
cell
activation
potential
(ECAP)
were
evaluated.
The
performance
simulations,
focusing
on
accuracy
processing
time,
assessed
determine
clinical
viability.
demonstrated
clinically
acceptable
results,
achieving
over
95%
while
reducing
simulation
by
up
54%.
entire
process,
image
construction
post-processing
completed
in
under
120
min.
Both
types
(tetrahedral
polyhedral)
provided
reliable
outputs
analysis.
provides
novel
demonstration
impact
type
obtaining
accurate
data,
quickly
efficiently,
simulations
aortic
assessments.
beneficial
routine
check-ups,
offering
improved
diagnostics
populations
limited
healthcare
access
or
higher
disease
risk.
Язык: Английский
Efficiency of a predictive surrogate model for hemodynamic predictions of blood flow in an idealized carotid artery stenosis
Physics of Fluids,
Год журнала:
2024,
Номер
36(12)
Опубликована: Дек. 1, 2024
This
study
presents
a
predictive
surrogate
model
(PSM)
for
predicting
hemodynamic
variables
in
idealized
carotid
artery
stenosis.
The
PSM
integrates
long
short-term
memory
(LSTM)
and
proper
orthogonal
decomposition
(POD)
techniques.
model's
accuracy
is
evaluated
two
different
stenosis
conditions
the
For
simulation
of
full-order
stenosis,
fluid–structure
interaction
(FSI)
solver
employed
to
between
blood
vessel
wall.
Casson
used
estimate
viscosity
non-Newtonian
flow.
These
are
selected
accurately
capture
hemodynamics
across
various
conditions.
examines
pressure,
wall
shear
stress
(WSS),
velocity
components,
oscillatory
index
(OSI)
variables.
reconstruction
error
reduced
order
calculated
based
on
chosen
number
POD
modes.
It
noteworthy
that
OSI
significantly
higher
than
other
components
derivatives
(i.e.,
WSS)
both
LSTM
under
conditions,
showing
promising
results
despite
inherent
complexities
physiological
situations.
While
effectively
predicts
WSS
indices
with
reliable
scales,
exhibits
slightly
larger
errors.
Язык: Английский