Artificial Intelligence Review,
Journal Year:
2024,
Volume and Issue:
58(2)
Published: Dec. 20, 2024
Functional
near-infrared
spectroscopy
(fNIRS)
imaging
offers
a
promising
avenue
for
measuring
brain
function
in
both
healthy
and
diseased
cohorts.
However,
signal
quality
fNIRS
data
frequently
encounters
challenges,
such
as
low
signal-to-noise
ratio
or
substantial
motion
artifacts
one
multiple
measurement
channels,
impeding
the
comprehensive
exploitation
of
data.
Developing
valid
method
to
improve
damaged
signals
is
crucial,
particularly
given
extensive
use
wearable
devices
natural
settings
where
noise
issues
are
even
more
unavoidable.
Here,
we
proposed
generative
deep
learning
approach
recover
channels.
The
model
captured
spatial
temporal
variations
time
series
by
integrating
multiscale
convolutional
layers,
gated
recurrent
units
(GRUs),
linear
regression
analyses.
We
trained
on
resting-state
dataset
from
elderly
individuals
evaluated
its
performance
terms
reconstruction
accuracy
functional
connectivity
matrix
similarity.
Collectively,
exhbited
an
excellent
series.
In
individual
channel-level,
can
accurately
reconstruct
(mean
correlation
=
0.80
±
0.14)
while
preserving
intervariable
relationships
(correlation
0.93).
maintained
robust
consistency
connectivity.
Our
findings
underscore
potential
techniques
reconstructing
signals,
providing
novel
perspective
efficient
utilization
clinical
diagnosis
research.
Diagnostics,
Journal Year:
2024,
Volume and Issue:
14(11), P. 1100 - 1100
Published: May 25, 2024
The
integration
of
artificial
intelligence
(AI)
into
point-of-care
(POC)
biosensing
has
the
potential
to
revolutionize
diagnostic
methodologies
by
offering
rapid,
accurate,
and
accessible
health
assessment
directly
at
patient
level.
This
review
paper
explores
transformative
impact
AI
technologies
on
POC
biosensing,
emphasizing
recent
computational
advancements,
ongoing
challenges,
future
prospects
in
field.
We
provide
an
overview
core
their
use
POC,
highlighting
issues
challenges
that
may
be
solved
with
AI.
follow
can
applied
including
machine
learning
algorithms,
neural
networks,
data
processing
frameworks
facilitate
real-time
analytical
decision-making.
explore
applications
each
stage
biosensor
development
process,
diverse
opportunities
beyond
simple
analysis
procedures.
include
a
thorough
outstanding
field
AI-assisted
focusing
technical
ethical
regarding
widespread
adoption
these
technologies,
such
as
security,
algorithmic
bias,
regulatory
compliance.
Through
this
review,
we
aim
emphasize
role
advancing
inform
researchers,
clinicians,
policymakers
about
reshaping
global
healthcare
landscapes.
IGI Global eBooks,
Journal Year:
2025,
Volume and Issue:
unknown, P. 221 - 250
Published: March 14, 2025
Health
care
sensor
data
analytics
is
one
of
the
most
disruptive
innovations
in
personal
health
since
it
uses
information
from
sensors
to
improve
patient
care.
In
this
chapter,
a
new
algorithm
for
Multi-Modal
Sensor
Data
Fusion
and
Anomaly
Detection
presented;
some
important
issues,
including
authenticity,
confidentiality
integrity,
are
discussed.
The
proposed
method
builds
on
machine
learning
artificial
intelligence
deal
with
large
sets
comprehend
order
enhance
diagnosis
solutions
oriented
towards
then
validated
through
series
tests
outperforms
different
approaches
used
same
field
diagnose
problems
by
analyzing
data.
findings
strongly
argue
robust
infrastructure
that
covers
acquisition,
distribution,
archival,
analysis,
presentation.
Thus,
paper
underlines
deficiencies
current
underscores
applicability
analytical
techniques
radical
transformation
healthcare
processes
alongside
adherence
ethical
considerations.
Frontiers in Aging Neuroscience,
Journal Year:
2025,
Volume and Issue:
17
Published: March 3, 2025
Introduction
Inadequate
primary
care
infrastructure
and
training
in
China
misconceptions
about
aging
lead
to
high
mis−/under-diagnoses
serious
time
delays
for
dementia
patients,
imposing
significant
burdens
on
family
members
medical
carers.
Main
body
A
flowchart
integrating
rural
urban
areas
of
pathway
is
proposed,
especially
spotting
the
obstacles
mis/under-diagnoses
that
can
be
alleviated
by
data-driven
computational
strategies.
Artificial
intelligence
(AI)
machine
learning
models
built
data
are
succinctly
reviewed
terms
roadmap
from
home,
community
hospital
settings.
Challenges
corresponding
recommendations
clinical
transformation
then
reported
viewpoint
diverse
integrity
accessibility,
as
well
models’
interpretability,
reliability,
transparency.
Discussion
Dementia
cohort
study
along
with
developing
a
center-crossed
platform
should
strongly
encouraged,
also
publicly
accessible
where
appropriate.
Only
doing
so
challenges
overcome
AI-enabled
research
enhanced,
leading
an
optimized
China.
Future
policy-guided
cooperation
between
researchers
multi-stakeholders
urgently
called
4E
(early-screening,
early-assessment,
early-diagnosis,
early-intervention).
