Neuroscience
explores
the
anatomy,
function
and
development
of
central
peripheral
nervous
system.
Neuroscientists
lately
study
functional
brain
networks
to
understand
mental
disorders
like
depression.
Analysis
these
can
aid
in
diagnosing
Q-analysis,
a
higher-order
interaction
approach,
may
be
more
effective
identifying
regions
relevant
depression,
compared
standard
paired
approach.
This
examined
networks,
by
using
approach
with
Q-analysis
method,
depressed
patients
healthy
subjects
fMRI
data.
Results
indicated
fewer
weaker
interactions
controls.
Modularity
clustering
were
also
reduce
These
findings
highlight
importance
studying
for
understanding
The
purpose
of
this
paper
is
to
comparatively
analyse
the
efficiency
using
artificial
neural
networks
with
different
convolutional
and
recurrent
architectures
in
task
depression
diagnosis
based
on
electroencephalogram
(EEG)
data.
Open
datasets
were
chosen
as
objects
study
own
EEG
data
real
patients
collected.
Methods.
To
solve
problem
identifying
biomarkers
depressive
disorder
from
data,
we
used
two-dimensional
or
one-dimensional
convolution
operation,
well
hybrid
models
networks.
test
developed
networks,
selected
open
sets,
performed
an
experiment
collect
our
depressed
patients,
merged
prepared
sets.
result
work
analysis
comparison
performance
classifiers
network
models.
Conclusion.
We
show
that
average
accuracy
classification
a
sample
cross-validation
was
0.68.
results
are
consistent
known
literature
for
small
patient-disaggregated
datasets.
Although
obtained
insufficient
practical
application
model,
it
can
be
argued
further
research
improve
model
promising,
need
create
sufficiently
large
representative
dataset
which
important
scientific
construction
biophysical
disorders.
Izvestiya of Saratov University Physics,
Journal Year:
2024,
Volume and Issue:
24(3), P. 239 - 249
Published: Aug. 22, 2024
Аннотация.Цель
настоящей
работы
-проанализировать
требования
к
методике
сбора
биофизических
данных
на
основе
открытых
наборов
определения
психоэмоционального
состояния,
аппаратному
и
программному
обеспечению
для
их
первичной
обработки.Сформулировать
методику
формирования
мультимодальных
данных,
пригодную
исследования
психических
состояний
изменений,
в
том
числе
с
использованием
алгоритмов
машинного
обучения.Описать
возможный
метод
реализации
этих
требований
аппаратно-программных
комплексах.Методы.Для
анализа
основных
особенностей
характеризующих
психические
были
выбраны
открытые
наборы
пациентов
депрессивными
расстройствами.Основные
сформулированы
изучения
публикаций
об
особенностях
применения
диагностики
депрессивных
расстройств.Результатом
являются
набор
мультимодальным
данным
биопотенциалов
психоэмоциональных
состояний,
методика
функциональная
концепция
аппаратно-программного
комплекса
регистрации,
синхронизации
записи
аннотированном
виде.Заключение.На
примере
депрессивного
расстройства
показана
целесообразность
возможность
регистрации
мультимодальных,
синхронизированных
между
собой
аннотированных
о
психоэмоциональном
состоянии
испытуемого
исследовательских,
диагностических
целей
качестве
обучающей
выборки
алгоритмах
обучения.Предложенная
программно-аппаратного
позволяют
Proceedings of the Southwest State University Series Economics Sociology and Management,
Journal Year:
2024,
Volume and Issue:
14(5), P. 240 - 255
Published: Dec. 7, 2024
The
relevance.
rate
of
spread
artificial
systems
with
intelligence
is
increasing
every
year.
This
evidenced
by
a
large
number
scientific
publications,
patents,
research
in
this
area
and
support
for
government
programs.
However,
the
ambiguity
its
use
science
practice
leaves
AI
subject
heated
discussions
both
humanitarian
community
among
scientists
who
technologies
their
professional
activities.
purpose
article
to
analyze
role
professional,
specifically,
medical
practice.
Objectives:
study
digital
elements
used
high-tech
practices,
potential
risks;
identify
social
roles
that
can
be
assigned
programs
as
assistants
scientists;
show
possibilities
risks
introducing
into
Methodology
.
As
main
approach
solving
tasks
set,
uses
an
interdisciplinary
synthesis
philosophical
reflections,
statistics
results
practical
application
healthcare,
which
allows
highlighting
anthropological
problems
rapid
introduction
new
sphere.
Results.
examines
healthcare
environment
all
system
processes,
from
diagnosis
management
complexes.
possible
AIS
assistant
manager,
analyst,
consultant
qualified
colleague
modern
technology-oriented
are
shown.
Conclusions.
When
using
work
it
pragmatic,
psychological
ethical
aspects.
Problems
were
found
not
only
disclosure
confidential
information
about
patients,
but
also
more
serious
shortcomings
related
effectiveness
organization
loss
important
competencies
practitioners.
Open Access Government,
Journal Year:
2023,
Volume and Issue:
39(1), P. 226 - 227
Published: July 10, 2023
Improving
AI/ML
services
for
ophthalmology
and
medicine
Eric
Buckland,
PhD
of
Translational
Imaging
Innovations,
delves
into
how
we
can
achieve
better
transparency,
traceability,
reproducibility
in
medicine.
Biomarkers
are
indispensable
to
finding
cures
developing
new
therapies
degenerative
eye
diseases.
objectively
measurable
characteristics
or
indicators
normal
biological
processes,
disease
responses
therapeutic
interventions.
essential
decision-making
throughout
drug
development
patient
care
management.
Dr.
Buckland
seeks
explain
biomarkers
clinical
endpoints
transform
ophthalmology,
AI
ML
healthcare
applications,
biomarker
relies
on
traceability
&
reproducibility.
Overall,
Innovations
has
committed
“delivering
the
most
convenient
productive
solutions
ophthalmic
discovery
endpoint
validation”.
TII
aims
enable
translational
researchers
develop
diagnostics
with
more
predictable
benefits
–
faster,
cheaper,
less
frustrating.
International Physical Medicine & Rehabilitation Journal,
Journal Year:
2023,
Volume and Issue:
8(2), P. 135 - 140
Published: June 29, 2023
The
development
of
artificially
intelligent
technological
machine
systems
that
can
integrate
large
volumes
data,
and
also
‘learn’
to
recognize
notable
patterns,
are
currently
being
widely
discussed
employed
in
various
health
other
realms.
In
this
regard,
what
promise
do
these
hold
for
ameliorating
the
late
life
chronic
disease
burden
increasing
numbers
adults
globally
may
stem
from
one
or
multiple
longstanding
conditions.
To
explore
issue,
a
broad
exploration
rehabilitation
associated
artificial
intelligence
implications
was
conducted
using
leading
data
bases.
Results
show
there
some
active
advances
both
learning
realms,
but
not
context
desirable
robust
observations
all
cases.
Much
future
work
is
indicated
though
strongly
recommended.
In
this
work,
we
analyzed
the
functional
connectivity
between
different
groups
of
subjects.
The
included
patients
with
major
depressive
disorder,
and
bipolar
depression
that
were
obtained
during
experimental
research.
data
had
been
subjected
to
preprocessing
procedures
employed
in
order
identify
brain
regions
displayed
significant
variations.
