Scientific Data,
Journal Year:
2024,
Volume and Issue:
11(1)
Published: Nov. 27, 2024
Oral
diseases
affect
nearly
3.5
billion
people,
and
medical
resources
are
limited,
which
makes
access
to
oral
health
services
nontrivial.
Imaging-based
machine
learning
technology
is
one
of
the
most
promising
technologies
improve
reduce
patient
costs.
The
development
requires
publicly
accessible
datasets.
However,
previous
public
dental
datasets
have
several
limitations:
a
small
volume
computed
tomography
(CT)
images,
lack
multimodal
data,
complexity
diversity
data.
These
issues
detrimental
field
dentistry.
Thus,
solve
these
problems,
this
paper
introduces
new
dataset
that
contains
169
patients,
three
commonly
used
image
modalities,
images
various
conditions
cavity.
proposed
has
good
potential
facilitate
research
on
services,
such
as
reconstructing
3D
structure
assisting
clinicians
in
diagnosis
treatment,
translation,
segmentation.
Healthcare,
Journal Year:
2023,
Volume and Issue:
11(5), P. 683 - 683
Published: Feb. 25, 2023
This
scoping
review
examines
the
contemporary
applications
of
advanced
artificial
intelligence
(AI)
software
in
orthodontics,
focusing
on
its
potential
to
improve
daily
working
protocols,
but
also
highlighting
limitations.
The
aim
was
evaluate
accuracy
and
efficiency
current
AI-based
systems
compared
conventional
methods
diagnosing,
assessing
progress
patients’
treatment
follow-up
stability.
researchers
used
various
online
databases
identified
diagnostic
dental
monitoring
as
most
studied
orthodontics.
former
can
accurately
identify
anatomical
landmarks
for
cephalometric
analysis,
while
latter
enables
orthodontists
thoroughly
monitor
each
patient,
determine
specific
desired
outcomes,
track
progress,
warn
changes
pre-existing
pathology.
However,
there
is
limited
evidence
assess
stability
outcomes
relapse
detection.
study
concludes
that
AI
an
effective
tool
managing
orthodontic
from
diagnosis
retention,
benefiting
both
patients
clinicians.
Patients
find
easy
use
feel
better
cared
for,
clinicians
make
diagnoses
more
easily
compliance
damage
braces
or
aligners
quickly
frequently.
Sensors,
Journal Year:
2021,
Volume and Issue:
21(19), P. 6628 - 6628
Published: Oct. 5, 2021
(1)
Background:
The
rapid
pace
of
digital
development
in
everyday
life
is
also
reflected
dentistry,
including
the
emergence
first
systems
based
on
artificial
intelligence
(AI).
This
systematic
review
focused
recent
scientific
literature
and
provides
an
overview
application
AI
dental
discipline
prosthodontics.
(2)
Method:
According
to
a
modified
PICO-strategy,
electronic
(MEDLINE,
EMBASE,
CENTRAL)
manual
search
up
30
June
2021
was
carried
out
for
published
last
five
years
reporting
use
field
(3)
Results:
560
titles
were
screened,
which
abstracts
16
full
texts
selected
further
review.
Seven
studies
met
inclusion
criteria
analyzed.
Most
identified
reported
training
system
(n
=
6)
or
explored
function
intrinsic
CAD
software
1).
(4)
Conclusions:
While
number
included
relatively
low,
summary
obtained
findings
by
represents
latest
developments
prosthodontics
demonstrating
its
automated
diagnostics,
as
predictive
measure,
classification
identification
tool.
In
future,
technologies
will
likely
be
used
collecting,
processing,
organizing
patient-related
datasets
provide
patient-centered,
individualized
treatment.
Frontiers in Oral Health,
Journal Year:
2022,
Volume and Issue:
2
Published: Jan. 11, 2022
Oral
squamous
cell
carcinoma
(OSCC)
is
one
of
the
most
prevalent
cancers
worldwide
and
its
incidence
on
rise
in
many
populations.
The
high
rate,
late
diagnosis,
improper
treatment
planning
still
form
a
significant
concern.
Diagnosis
at
an
early-stage
important
for
better
prognosis,
treatment,
survival.
Despite
recent
improvement
understanding
molecular
mechanisms,
diagnosis
approach
toward
precision
medicine
OSCC
patients
remain
challenge.
To
enhance
medicine,
deep
machine
learning
technique
has
been
touted
to
early
detection,
consequently
reduce
cancer-specific
mortality
morbidity.
This
reported
have
made
progress
data
extraction
analysis
vital
information
medical
imaging
years.
Therefore,
it
potential
assist
detection
oral
carcinoma.
Furthermore,
automated
image
can
pathologists
clinicians
make
informed
decision
regarding
cancer
patients.
article
discusses
technical
knowledge
algorithms
OSCC.
It
examines
application
technology
classification,
segmentation
synthesis,
planning.
Finally,
we
discuss
how
this
future
perspective
F1000Research,
Journal Year:
2023,
Volume and Issue:
12, P. 1179 - 1179
Published: Sept. 20, 2023
Artificial
Intelligence
(AI)
technologies
play
a
significant
role
and
significantly
impact
various
sectors,
including
healthcare,
engineering,
sciences,
smart
cities.
AI
has
the
potential
to
improve
quality
of
patient
care
treatment
outcomes
while
minimizing
risk
human
error.
Artificial
is
transforming
dental
industry,
just
like
it
revolutionizing
other
sectors.
It
used
in
dentistry
diagnose
diseases
provide
recommendations.
Dental
professionals
are
increasingly
relying
on
technology
assist
diagnosis,
clinical
decision-making,
planning,
prognosis
prediction
across
ten
specialties.
One
most
advantages
its
ability
analyze
vast
amounts
data
quickly
accurately,
providing
with
valuable
insights
enhance
their
decision-making
processes.
The
purpose
this
paper
identify
advancement
artificial
intelligence
algorithms
that
have
been
frequently
assess
how
well
they
perform
terms
treatment,
specialties;
public
health,
endodontics,
oral
maxillofacial
surgery,
medicine
pathology,
&
radiology,
orthodontics
dentofacial
orthopedics,
pediatric
dentistry,
periodontics,
prosthodontics,
digital
general.
We
will
also
show
pros
cons
using
all
specialties
different
ways.
Finally,
we
present
limitations
which
made
incapable
replacing
personnel,
dentists,
who
should
consider
complimentary
benefit
not
threat.
Periodontology 2000,
Journal Year:
2023,
Volume and Issue:
96(1), P. 250 - 280
Published: Dec. 10, 2023
The
oral
squamous
cell
carcinoma
(OSCC)
5
year
survival
rate
of
41%
has
marginally
improved
in
the
last
few
years,
with
less
than
a
1%
improvement
per
from
2005
to
2017,
higher
rates
when
detected
at
early
stages.
Based
on
histopathological
grading
dysplasia,
it
is
estimated
that
severe
dysplasia
malignant
transformation
7%-50%.
Despite
these
numbers,
does
not
reliably
predict
its
clinical
behavior.
Thus,
more
accurate
markers
predicting
progression
cancer
would
enable
better
targeting
lesions
for
closer
follow-up,
especially
stages
disease.
In
this
context,
molecular
biomarkers
derived
genetics,
proteins,
and
metabolites
play
key
roles
oncology.
These
signatures
can
help
likelihood
OSCC
development
and/or
have
potential
detect
disease
an
stage
and,
support
treatment
decision-making
responsiveness.
Also,
identifying
reliable
detection
be
obtained
non-invasively
enhance
management
OSCC.
This
review
will
discuss
emerged
different
biological
areas,
including
genomics,
transcriptomics,
proteomics,
metabolomics,
immunomics,
microbiomics.
AI,
Journal Year:
2024,
Volume and Issue:
5(1), P. 158 - 176
Published: Jan. 5, 2024
In
the
age
of
artificial
intelligence
(AI),
technological
progress
is
changing
established
workflows
and
enabling
some
basic
routines
to
be
updated.
dentistry,
patient’s
face
a
crucial
part
treatment
planning,
although
it
has
always
been
difficult
grasp
in
an
analytical
way.
This
review
highlights
current
digital
advances
that,
thanks
AI
tools,
allow
us
implement
facial
features
beyond
symmetry
proportionality
incorporate
analysis
into
diagnosis
planning
orthodontics.
A
Scopus
literature
search
was
conducted
identify
topics
with
greatest
research
potential
within
orthodontics
over
last
five
years.
The
most
researched
cited
topic
its
applications
Apart
from
automated
2D
or
3D
cephalometric
analysis,
finds
application
decision-making
algorithms
as
well
evaluation
retention.
Together
AI,
other
are
shaping
today’s
Without
any
doubts,
era
“old”
at
end,
modern,
face-driven
on
way
becoming
reality
modern
orthodontic
practices.
Journal of Dental Research,
Journal Year:
2022,
Volume and Issue:
101(11), P. 1321 - 1327
Published: April 21, 2022
Oral
squamous
cell
carcinoma
(OSCC)
is
prevalent
around
the
world
and
associated
with
poor
prognosis.
OSCC
typically
diagnosed
from
tissue
biopsy
sections
by
pathologists
who
rely
on
their
empirical
experience.
Deep
learning
models
may
improve
accuracy
speed
of
image
classification,
thus
reducing
human
error
workload.
Here
we
developed
a
custom-made
deep
model
to
assist
in
detecting
histopathology
images.
We
collected
analyzed
total
2,025
images,
among
which
1,925
images
were
included
training
set
100
testing
set.
Our
was
able
automatically
evaluate
these
arrive
at
diagnosis
sensitivity
0.98,
specificity
0.92,
positive
predictive
value
0.924,
negative
0.978,
F1
score
0.951.
Using
subset
examined
whether
our
could
diagnostic
performance
junior
senior
pathologists.
found
that
delineate
6.26
min
faster
when
assisted
than
working
alone.
When
clinicians
model,
average
improved
0.9221
0.9566
case
0.9361
0.9463
findings
indicate
can
