International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering,
Journal Year:
2022,
Volume and Issue:
13(1), P. 920 - 920
Published: Oct. 25, 2022
<span
lang="EN-US">The
detection
of
keratoconus
has
been
a
difficult
and
arduous
process
over
the
years
for
ophthalmologists
who
have
devised
traditional
approaches
diagnosis
including
slit-lamp
examination
observation
thinning
corneal.
The
main
contribution
this
paper
is
using
deep
learning
models
namely
Resnet50
EfficientNet
to
not
just
detect
whether
an
eye
infected
with
or
but
also
accurately
stages
infection
mild,
moderate,
advanced.
dataset
used
consists
corneal
topographic
maps
pentacam
images.
Individually
achieved
97%
94%
accuracy
on
dataset.
We
employed
class
activated
(CAM)
observe
help
visualize
which
areas
images
are
utilized
when
making
classifications
different
keratoconus.
Using
predict
severity
can
drastically
speed
up
provide
accurate
results
at
same
time.</span>
Cureus,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Oct. 22, 2024
Keratoconus
(KC)
is
a
prevalent
corneal
condition
in
Saudi
Arabia,
with
studies
suggesting
variable
prevalence
rates
across
regions,
highlighting
considerable
public
health
issue.
Despite
its
prevalence,
awareness
of
the
remains
low.
This
study
aims
to
evaluate
level
keratoconus
among
population
Taif
City,
Arabia.
Current Opinion in Ophthalmology,
Journal Year:
2024,
Volume and Issue:
unknown
Published: Nov. 28, 2024
Purpose
of
review
To
role
artificial
intelligence
in
medicine.
Recent
findings
Artificial
is
continuing
to
revolutionize
access,
diagnosis,
personalization
medicine,
and
treatment
healthcare.
As
a
matter
fact,
contributed
the
research
that
resulted
2024
Nobel
Prizes
physics,
chemistry,
economics.
We
are
only
at
tip
iceberg
utilizing
abilities
medicine
improve
accuracy
diagnoses
enhance
patient
outcomes.
has
allowed
better
image
analysis,
prediction
progression
disease,
personalized
plans,
incorporations
genomics,
improved
efficiency
care
follow-up
home
monitoring.
In
ocular
health
diagnosis
diabetic
retinopathy,
macular
degeneration,
glaucoma,
corneal
infections,
ectasia
few
examples
how
power
been
harnessed.
Even
though
there
still
challenges
need
more
work
areas
privacy,
Health
Insurance
Portability
Accountability
Act
(HIPAA)
compliance,
reliability,
development
regulatory
frameworks,
revolutionized
will
continue
Summary
enhancing
medical
treatment,
as
well
access
prevention.
Ocular
imaging,
visual
outcome,
optics,
intraocular
pressure,
data
points
see
growth
it
field
intelligence.
Research Square (Research Square),
Journal Year:
2024,
Volume and Issue:
unknown
Published: April 30, 2024
Abstract
Background
Keratoconus
in
patients
can
progress
at
different
ages
and
rates.
This
creates
difficulty
determining
optimal
timing
for
follow-up
interventions
such
as
corneal
cross-linking.
Previous
studies
have
shown
that
artificial
intelligence
(AI)
accurately
diagnose
keratoconus.
Less
is
known
on
AI
use
predicting
progression
of
Methods
A
systematic
review
peer-reviewed
articles
was
performed
February
2023
using
medical
databases
(Medline,
PubMed,
EMBASE,
Cochrane)
engineering
(IEEE
Xplore,
ACM
Digital
Library).
Studies
were
included
if
they
published
journals,
reported
least
one
accuracy
measure,
investigated
keratoconus
rather
than
diagnosis
or
treatment
efficacy.
The
outcome
measures
progression,
type
method,
input
details,
number
parameters
algorithm.
Results
455
records
identified.
Following
duplicate
removal,
abstract
full-text
screening,
six
(total
eyes
n
=
3
151;
5
083;
mean
proportion
males
62.8%±13.4%;
age
36.9
±
18.7
years)
included.
methods
used
convolutional
neural
networks,
machine
learning,
random
forests.
Input
modalities
Optical
Coherence
Tomography
(OCTs),
Anterior-segment
OCTs
Pentacam.
Overall,
the
good
utility
[Areas
under
Curve
(AUC,
0.814–0.93),
(71.5–97.5%),
sensitivity
(70.9–95.5%)
specificity
(41.9–82%)]
progression.
Conclusion
Emerging
evidence
indicates
may
a
role
Further
high-quality
are
needed
to
establish
clinical
practice.
International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering,
Journal Year:
2022,
Volume and Issue:
13(1), P. 920 - 920
Published: Oct. 25, 2022
<span
lang="EN-US">The
detection
of
keratoconus
has
been
a
difficult
and
arduous
process
over
the
years
for
ophthalmologists
who
have
devised
traditional
approaches
diagnosis
including
slit-lamp
examination
observation
thinning
corneal.
The
main
contribution
this
paper
is
using
deep
learning
models
namely
Resnet50
EfficientNet
to
not
just
detect
whether
an
eye
infected
with
or
but
also
accurately
stages
infection
mild,
moderate,
advanced.
dataset
used
consists
corneal
topographic
maps
pentacam
images.
Individually
achieved
97%
94%
accuracy
on
dataset.
We
employed
class
activated
(CAM)
observe
help
visualize
which
areas
images
are
utilized
when
making
classifications
different
keratoconus.
Using
predict
severity
can
drastically
speed
up
provide
accurate
results
at
same
time.</span>
