Towards Transforming Neurorehabilitation: The Impact of Artificial Intelligence on Diagnosis and Treatment of Neurological Disorders
Biomedicines,
Journal Year:
2024,
Volume and Issue:
12(10), P. 2415 - 2415
Published: Oct. 21, 2024
Background
and
Objectives:
Neurological
disorders
like
stroke,
spinal
cord
injury
(SCI),
Parkinson’s
disease
(PD)
significantly
affect
global
health,
requiring
accurate
diagnosis
long-term
neurorehabilitation.
Artificial
intelligence
(AI),
such
as
machine
learning
(ML),
may
enhance
early
diagnosis,
personalize
treatment,
optimize
rehabilitation
through
predictive
analytics,
robotic
systems,
brain-computer
interfaces,
improving
outcomes
for
patients.
This
systematic
review
examines
how
AI
ML
systems
influence
treatment
in
neurorehabilitation
among
neurological
disorders.
Materials
Methods:
Studies
were
identified
from
an
online
search
of
PubMed,
Web
Science,
Scopus
databases
with
a
time
range
2014
to
2024.
has
been
registered
on
Open
OSF
(n)
EH9PT.
Results:
Recent
advancements
are
revolutionizing
motor
conditions
SCI,
PD,
offering
new
opportunities
personalized
care
improved
outcomes.
These
technologies
clinical
assessments,
therapy
personalization,
remote
monitoring,
providing
more
precise
interventions
better
management.
Conclusions:
is
neurorehabilitation,
personalized,
data-driven
treatments
that
recovery
Future
efforts
should
focus
large-scale
validation,
ethical
considerations,
expanding
access
advanced,
home-based
care.
Language: Английский
Brain-computer interfaces in neurorehabilitation for central nervous system diseases: Applications in stroke, multiple sclerosis and Parkinson's disease
SANAMED,
Journal Year:
2025,
Volume and Issue:
00, P. 75 - 75
Published: Jan. 1, 2025
Brain-computer
interfaces
(BCIs)
represent
an
innovative
approach
to
neurorehabilitation
for
neurological
conditions,
particularly
stroke,
multiple
sclerosis,
and
Parkinson's
disease.
This
paper
provides
a
comprehensive
analysis
of
current
BCI
applications,
technological
developments,
clinical
outcomes
in
these
conditions.
Recent
advances
electroencephalography-based
BCIs
have
demonstrated
promising
results,
with
classification
accuracies
exceeding
90%
stroke
rehabilitation
comparable
performance
sclerosis
Meta-analyses
trials
(n=235)
indicate
significant
motor
function
improvements
,
standardized
mean
differences
0.79
upper
limb
assessment
scores
compared
conventional
therapy.
Disease-specific
challenges
necessitate
tailored
approaches,
while
hybrid
systems
combining
signal
types
integration
virtual
reality
or
robotic
assistance
enhance
therapeutic
potential.
The
development
portable,
home-based
offers
increased
therapy
intensity
but
raises
concerns
about
remote
monitoring
safety
protocols.
review
synthesizes
evidence
supporting
applications
highlights
critical
areas
future
research,
including
cognitive
optimization
the
standardization
outcome
measures
cross-condition
comparison.
Language: Английский
Developments, Hardships, and Prospective Directions in the Field of Neuroscience and Neurological Machine Learning for Behavioral Evaluation
Advances in psychology, mental health, and behavioral studies (APMHBS) book series,
Journal Year:
2025,
Volume and Issue:
unknown, P. 459 - 474
Published: Jan. 3, 2025
This
study
examines
the
current
state
of
neuroscience,
emphasizing
use
machine
learning
to
improve
behavioral
assessment
and
its
therapeutic
applications.
Progress
in
neuroscience
has
facilitated
comprehension
cognitive
aging,
neurological
illnesses,
essential
function
cognition
detecting
decline.
The
increasing
potential
likely
overcomes
aforementioned
deficiencies—consistent
monitoring
early
identification
mental
health—through
automation.
research
will
ultimately
address
AI-related
concerns
regarding
longitudinal
individuals
with
illnesses.
Neurocognitive
testing
may
leverage
significantly
advance
evaluation
management
health,
hence
facilitating
future
enhance
broaden
capabilities
neurolearning
neuropsychology.
Language: Английский
TraxVBF: A hybrid transformer-xLSTM framework for EMG signal processing and assistive technology development in rehabilitation
Sensing and Bio-Sensing Research,
Journal Year:
2025,
Volume and Issue:
unknown, P. 100749 - 100749
Published: Jan. 1, 2025
Language: Английский
Telerehabilitation and Its Impact Following Stroke: An Umbrella Review of Systematic Reviews
Bayan Alwadai,
No information about this author
Hatem Lazem,
No information about this author
Hajar Almoajil
No information about this author
et al.
Journal of Clinical Medicine,
Journal Year:
2024,
Volume and Issue:
14(1), P. 50 - 50
Published: Dec. 26, 2024
Objectives:
To
summarize
the
impact
of
various
telerehabilitation
interventions
on
motor
function,
balance,
gait,
activities
daily
living
(ADLs),
and
quality
life
(QoL)
among
patients
with
stroke
to
determine
existing
for
delivering
physiotherapy
sessions
in
clinical
practice.
Methods:
Six
electronic
databases
were
searched
identify
relevant
quantitative
systematic
reviews
(SRs).
Due
substantial
heterogeneity,
data
analysed
narratively.
Results:
A
total
28
(n
=
245
primary
studies)
included
that
examined
after
stroke.
Motor
function
was
most
studied
outcome
domain
across
(20
SRs),
followed
by
ADL
(18
balance
(14
SRs)
domains.
For
outcomes,
our
findings
highlight
moderate-
high-quality
evidence
showing
either
a
significant
effect
or
no
difference
between
other
interventions.
There
insufficient
draw
conclusion
regarding
feasibility
including
participant
satisfaction,
adherence
treatment,
cost.
Most
under
this
umbrella
subacute
chronic
phase
(12
SRs).
Simple
complex
such
as
telephone
calls,
videoconferencing,
smartphone-
tablet-based
mobile
health
applications,
messaging,
virtual
reality,
robot-assisted
devices,
3D
animation
videos,
alone
combination
interventions,
reviews.
Conclusions:
Various
have
shown
compared
improving
upper
lower
limb
ADLs,
QoL,
regardless
whether
simple
approaches
used.
Further
research
is
needed
support
delivery
rehabilitation
services
through
intervention
following
Language: Английский