Scientific Data,
Journal Year:
2025,
Volume and Issue:
12(1)
Published: March 29, 2025
Abstract
This
dataset
(named
CeTI-Age-Kinematics
)
fills
the
gap
in
existing
motion
capture
(MoCap)
data
by
recording
kinematics
of
full-body
movements
during
daily
tasks
an
age-comparative
sample
with
32
participants
two
groups:
older
adults
(66–75
years)
and
younger
(19–28
years).
The
were
recorded
using
sensor
suits
gloves
inertial
measurement
units
(IMUs).
features
30
common
elemental
that
are
grouped
into
nine
categories,
including
simulated
interactions
imaginary
objects.
Kinematic
under
well-controlled
conditions,
repetitions
well-documented
task
procedures
variations.
It
also
entails
anthropometric
body
measurements
spatial
experimental
setups
to
enhance
interpretation
IMU
MoCap
relation
characteristics
situational
surroundings.
can
contribute
advancing
machine
learning,
virtual
reality,
medical
applications
enabling
detailed
analyses
modeling
naturalistic
motions
their
variability
across
a
wide
age
range.
Such
technologies
essential
for
developing
adaptive
systems
tele-diagnostics,
rehabilitation,
robotic
planning
aim
serve
broad
populations.
Journal of Personalized Medicine,
Journal Year:
2024,
Volume and Issue:
14(11), P. 1101 - 1101
Published: Nov. 11, 2024
This
review
examines
the
significant
influence
of
Digital
Twins
(DTs)
and
their
variant,
Human
(DHTs),
on
healthcare
field.
DTs
represent
virtual
replicas
that
encapsulate
both
medical
physiological
characteristics-such
as
tissues,
organs,
biokinetic
data-of
patients.
These
models
facilitate
a
deeper
understanding
disease
progression
enhance
customization
optimization
treatment
plans
by
modeling
complex
interactions
between
genetic
factors
environmental
influences.
By
establishing
dynamic,
bidirectional
connections
physical
objects
digital
counterparts,
these
technologies
enable
real-time
data
exchange,
thereby
transforming
electronic
health
records.
Leveraging
increasing
availability
extensive
historical
datasets
from
clinical
trials
real-world
sources,
AI
can
now
generate
comprehensive
predictions
future
outcomes
for
specific
patients
in
form
AI-generated
DTs.
Such
also
offer
insights
into
potential
diagnoses,
progression,
responses.
remarkable
paves
way
precision
medicine
personalized
health,
allowing
high-level
individualized
interventions
therapies.
However,
integration
faces
several
challenges,
including
security,
accessibility,
bias,
quality.
Addressing
obstacles
is
crucial
to
realizing
full
DHTs,
heralding
new
era
personalized,
precise,
accurate
medicine.
IEEE Access,
Journal Year:
2024,
Volume and Issue:
12, P. 69652 - 69676
Published: Jan. 1, 2024
In
the
dynamic
landscape
of
healthcare,
Digital
Twin
(DT)
technology
has
emerged
as
a
transformative
force,
holding
promise
revolutionizing
patient
care
and
industry
practices.
This
article
surveys
literature
over
period
2020
to
2023
on
comprehensive
exploration
DT
in
elucidating
its
roles,
benefits,
implications
for
smart
personalized
healthcare.
The
study
addresses
fundamental
questions
concerning
potential
DT,
investigating
varied
roles
benefits
revolutionary
impact
industry,
essential
requirements
crafting
system
tailored
demands
research
further
unveils
key
layers
necessary
implementing
healthcare
system,
examining
applications
that
extend
from
diagnostics
treatment
strategies.
Methodologically,
paper
navigates
through
different
model
discussions,
providing
structured
approach
understanding
implementation
Despite
potential,
delves
into
limitations
challenges
faced
by
technology,
offering
balanced
perspective
current
state.
conclusion,
synthesizes
findings,
outlines
methodologies,
discusses
challenges,
sets
stage
future
research,
presenting
holistic
overview
pitfalls,
pathways
integrating
industry.
Electronics,
Journal Year:
2024,
Volume and Issue:
13(4), P. 746 - 746
Published: Feb. 13, 2024
Recently,
artificial
intelligence
(AI)-based
algorithms
have
revolutionized
the
medical
image
segmentation
processes.
Thus,
precise
of
organs
and
their
lesions
may
contribute
to
an
efficient
diagnostics
process
a
more
effective
selection
targeted
therapies,
as
well
increasing
effectiveness
training
process.
In
this
context,
AI
automatization
scan
increase
quality
resulting
3D
objects,
which
lead
generation
realistic
virtual
objects.
paper,
we
focus
on
AI-based
solutions
applied
in
intelligent
visual
content
generation,
i.e.,
computer-generated
three-dimensional
(3D)
images
context
extended
reality
(XR).
We
consider
different
types
neural
networks
used
with
special
emphasis
learning
rules
applied,
taking
into
account
algorithm
accuracy
performance,
open
data
availability.
This
paper
attempts
summarize
current
development
methods
imaging
that
are
XR.
It
concludes
possible
developments
challenges
applications
reality-based
solutions.
Finally,
future
lines
research
directions
applications,
both
solutions,
discussed.
Journal of Personalized Medicine,
Journal Year:
2024,
Volume and Issue:
14(5), P. 475 - 475
Published: April 29, 2024
The
massive
amount
of
human
biological,
imaging,
and
clinical
data
produced
by
multiple
diverse
sources
necessitates
integrative
modeling
approaches
able
to
summarize
all
this
information
into
answers
specific
questions.
In
paper,
we
present
a
hypermodeling
scheme
combine
models
cancer
aspects
regardless
their
underlying
method
or
scale.
Describing
tissue-scale
cell
proliferation,
biomechanical
tumor
growth,
nutrient
transport,
genomic-scale
aberrant
metabolism,
cell-signaling
pathways
that
regulate
the
cellular
response
therapy,
hypermodel
integrates
mutation,
miRNA
expression,
data.
constituting
hypomodels,
as
well
orchestration
links,
are
described.
Two
types,
Wilms
(nephroblastoma)
non-small
lung
cancer,
addressed
proof-of-concept
study
cases.
Personalized
simulations
actual
anatomy
patient
have
been
conducted.
has
also
applied
predict
control
after
radiotherapy
relationship
between
proliferative
activity
neoadjuvant
chemotherapy.
Our
innovative
holds
promise
digital
twin-based
decision
support
system
core
future
in
silico
trial
platforms,
although
additional
retrospective
adaptation
validation
necessary.
Aging,
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 16, 2025
With
the
global
population
aging
at
an
unprecedented
rate,
there
is
a
need
to
extend
healthy
productive
life
span.
This
review
examines
how
Deep
Learning
(DL)
and
Generative
Artificial
Intelligence
(GenAI)
are
used
in
biomarker
discovery,
deep
clock
development,
geroprotector
identification
generation
of
dual-purpose
therapeutics
targeting
disease.
The
paper
explores
emergence
multimodal,
multitasking
research
systems
highlighting
promising
future
directions
for
GenAI
human
animal
research,
as
well
clinical
application
longevity
medicine.
