AI in neurosurgical education: Can machines learn to see like surgeons?
Journal of Clinical Neuroscience,
Год журнала:
2025,
Номер
unknown, С. 111153 - 111153
Опубликована: Март 1, 2025
Язык: Английский
Alzheimer’s disease diagnosis using deep learning techniques: datasets, challenges, research gaps and future directions
International Journal of Systems Assurance Engineering and Management,
Год журнала:
2024,
Номер
unknown
Опубликована: Июль 30, 2024
Язык: Английский
An Open Data Collection of 3D Tool and Equipment Models for Neonatology
Results in Engineering,
Год журнала:
2025,
Номер
unknown, С. 104236 - 104236
Опубликована: Фев. 1, 2025
Язык: Английский
AI-Driven Advances in Low-Dose Imaging and Enhancement—A Review
Diagnostics,
Год журнала:
2025,
Номер
15(6), С. 689 - 689
Опубликована: Март 11, 2025
The
widespread
use
of
medical
imaging
techniques
such
as
X-rays
and
computed
tomography
(CT)
has
raised
significant
concerns
regarding
ionizing
radiation
exposure,
particularly
among
vulnerable
populations
requiring
frequent
imaging.
Achieving
a
balance
between
high-quality
diagnostic
minimizing
exposure
remains
fundamental
challenge
in
radiology.
Artificial
intelligence
(AI)
emerged
transformative
solution,
enabling
low-dose
protocols
that
enhance
image
quality
while
significantly
reducing
doses.
This
review
explores
the
role
AI-assisted
imaging,
CT,
X-ray,
magnetic
resonance
(MRI),
highlighting
advancements
deep
learning
models,
convolutional
neural
networks
(CNNs),
other
AI-based
approaches.
These
technologies
have
demonstrated
substantial
improvements
noise
reduction,
artifact
removal,
real-time
optimization
parameters,
thereby
enhancing
accuracy
mitigating
risks.
Additionally,
AI
contributed
to
improved
radiology
workflow
efficiency
cost
reduction
by
need
for
repeat
scans.
also
discusses
emerging
directions
AI-driven
including
hybrid
systems
integrate
post-processing
with
data
acquisition,
personalized
tailored
patient
characteristics,
expansion
applications
fluoroscopy
positron
emission
(PET).
However,
challenges
model
generalizability,
regulatory
constraints,
ethical
considerations,
computational
requirements
must
be
addressed
facilitate
broader
clinical
adoption.
potential
revolutionize
safety,
optimizing
quality,
improving
healthcare
efficiency,
paving
way
more
advanced
sustainable
future
Язык: Английский
HTRecNet: a deep learning study for efficient and accurate diagnosis of hepatocellular carcinoma and cholangiocarcinoma
Frontiers in Cell and Developmental Biology,
Год журнала:
2025,
Номер
13
Опубликована: Март 24, 2025
Background
Hepatocellular
carcinoma
(HCC)
and
cholangiocarcinoma
(CCA)
represent
the
primary
liver
cancer
types.
Traditional
diagnostic
techniques,
reliant
on
radiologist
interpretation,
are
both
time-intensive
often
inadequate
for
detecting
less
prevalent
CCA.
There
is
an
emergent
need
to
explore
automated
methods
using
deep
learning
address
these
challenges.
Methods
This
study
introduces
HTRecNet,
a
novel
framework
enhanced
precision
efficiency.
The
model
incorporates
sophisticated
data
augmentation
strategies
optimize
feature
extraction,
ensuring
robust
performance
even
with
constrained
sample
sizes.
A
comprehensive
dataset
of
5,432
histopathological
images
was
divided
into
5,096
training
validation,
336
external
testing.
Evaluation
conducted
five-fold
cross-validation
applying
metrics
such
as
accuracy,
area
under
receiver
operating
characteristic
curve
(AUC),
Matthews
correlation
coefficient
(MCC)
against
established
clinical
benchmarks.
Results
validation
cohorts
comprised
1,536
normal
tissue,
3,380
HCC,
180
HTRecNet
showed
exceptional
efficacy,
consistently
achieving
AUC
values
over
0.99
across
all
categories.
In
testing,
reached
accuracy
0.97
MCC
0.95,
affirming
its
reliability
in
distinguishing
between
normal,
CCA
tissues.
Conclusion
markedly
enhances
capability
early
accurate
differentiation
HCC
from
Its
high
efficiency
position
it
invaluable
tool
settings,
potentially
transforming
protocols.
system
offers
substantial
support
refining
workflows
healthcare
environments
focused
malignancies.
Язык: Английский
From Text to Hologram: Creation of High-Quality Holographic Stereograms Using Artificial Intelligence
Photonics,
Год журнала:
2024,
Номер
11(9), С. 787 - 787
Опубликована: Авг. 23, 2024
This
study
simplified
the
creation
of
holographic
stereograms
using
AI-generated
prompts,
overcoming
conventional
need
for
complex
equipment
and
professional
software.
AI
enabled
generation
detailed
perspective
images
suitable
various
content
styles.
The
generated
were
interpolated,
upscaled,
printed
a
CHIMERA
holoprinter
to
obtain
high-quality
holograms.
method
significantly
reduces
required
time
expertise,
thereby
making
accessible.
approach
demonstrated
that
can
effectively
streamline
production
high-fidelity
holograms,
suggesting
exciting
future
advancements
in
technology.
Язык: Английский