Combining simulation models and machine learning in healthcare management: strategies and applications
Progress in Biomedical Engineering,
Journal Year:
2024,
Volume and Issue:
6(2), P. 022001 - 022001
Published: Jan. 24, 2024
Abstract
Simulation
models
and
artificial
intelligence
(AI)
are
largely
used
to
address
healthcare
biomedical
engineering
problems.
Both
approaches
showed
promising
results
in
the
analysis
optimization
of
processes.
Therefore,
combination
simulation
AI
could
provide
a
strategy
further
boost
quality
health
services.
In
this
work,
systematic
review
studies
applying
hybrid
approach
management
challenges
was
carried
out.
Scopus,
Web
Science,
PubMed
databases
were
screened
by
independent
reviewers.
The
main
strategies
combine
as
well
major
application
scenarios
identified
discussed.
Moreover,
tools
algorithms
implement
proposed
described.
Results
that
machine
learning
appears
be
most
employed
with
models,
which
mainly
rely
on
agent-based
discrete-event
systems.
scarcity
heterogeneity
included
suggested
standardized
framework
learning-simulation
is
yet
defined.
Future
efforts
should
aim
use
these
design
novel
intelligent
in-silico
processes
effective
translation
clinics.
Language: Английский
A Statistical Approach to Assess the Robustness of Radiomics Features in the Discrimination of Mammographic Lesions
Journal of Personalized Medicine,
Journal Year:
2023,
Volume and Issue:
13(7), P. 1104 - 1104
Published: July 7, 2023
Despite
mammography
(MG)
being
among
the
most
widespread
techniques
in
breast
cancer
screening,
tumour
detection
and
classification
remain
challenging
tasks
due
to
high
morphological
variability
of
lesions.
The
extraction
radiomics
features
has
proved
be
a
promising
approach
MG.
However,
can
suffer
from
dependency
on
factors
such
as
acquisition
protocol,
segmentation
accuracy,
feature
engineering
methods,
which
prevent
implementation
robust
clinically
reliable
workflow
In
this
study,
robustness
is
investigated
function
lesion
MG
images
public
database.
A
statistical
analysis
carried
out
assess
score
introduced
based
significance
tests
performed.
obtained
results
indicate
that
observable
not
only
abnormality
type
(calcification
masses),
but
also
categories
(first-order
second-order),
image
view
(craniocaudal
medial
lateral
oblique),
lesions
(benign
malignant).
Furthermore,
through
proposed
approach,
it
possible
identify
those
characteristics
with
higher
discriminative
power
between
benign
malignant
lower
segmentation,
thus
suggesting
appropriate
choice
used
inputs
automated
algorithms.
Language: Английский
Comparison of Automatic and Semiautomatic Approach for the Posterior Nipple Line Calculation
IFMBE proceedings,
Journal Year:
2024,
Volume and Issue:
unknown, P. 217 - 226
Published: Jan. 1, 2024
Language: Английский
3D Dental Reconstruction with Photogrammetry Technology
2022 IEEE International Conference on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE),
Journal Year:
2023,
Volume and Issue:
unknown, P. 490 - 495
Published: Oct. 25, 2023
In
the
dental
field,
use
of
digital
technologies
for
scanning
hard
and
soft
tissues
mouth
is
becoming
more
widespread.
The
availability
3D
models
arches
allows
to
plan
treatments
show
results
in
advance,
increasing
patient
confidence.
However,
currently
clinical
practice,
accuracy
models,
although
very
satisfactory,
does
not
reach
that
traditional
impressions.
It
also
requires
simplify
hardware
structure,
making
intraoral
acquisition
device
manageable
comfortable.
purpose
this
study
evaluate
how
photogrammetry
technology,
commonly
widely
used
effective
other
sectors,
can
be
adapted
starting
from
reconstruction
a
plaster
cast.
By
comparing
model
obtained
with
proposed
technology
using
leading
top
player
scanners
on
market,
comparable
were
terms
performance.
Both
comparison
spatial
alignment
shape,
certain
overlap
equality
between
two
emerge.
These
suggest
could
represent
valid
solution
overcoming
limitation
market
field.
Language: Английский