Santosh University Journal of Health Sciences,
Journal Year:
2024,
Volume and Issue:
10(2), P. 269 - 278
Published: July 1, 2024
ABSTRACT
Artificial
intelligence
(AI)
is
a
computer
technology
that
becoming
increasingly
popular
worldwide
as
high-impact,
game-changing
innovation,
where
machines
can
imitate
human
actions.
AI
in
the
healthcare
system
evolving
dentistry.
The
primary
uses
of
dentistry
include:
diagnosis
and
treatment,
patient
management,
prognosis
prediction
using
key
feature
mathematical
model
building
administrative
activities.
life-saving
for
oral
professionals,
particularly
fields
dental
implants
periodontology.
Therefore,
we
have
positive
view
on
development
machine
learning
reduction
medical
errors,
better
care,
optimization
clinical
decision
making
implantology.
This
review
summarizes
characteristics
model,
its
use
periodontology
implant
therapy,
drawbacks
ethical
concerns,
future
perspectives.
Biosensors,
Journal Year:
2024,
Volume and Issue:
14(7), P. 356 - 356
Published: July 22, 2024
The
steady
progress
in
consumer
electronics,
together
with
improvement
microflow
techniques,
nanotechnology,
and
data
processing,
has
led
to
implementation
of
cost-effective,
user-friendly
portable
devices,
which
play
the
role
not
only
gadgets
but
also
diagnostic
tools.
Moreover,
numerous
smart
devices
monitor
patients'
health,
some
them
are
applied
point-of-care
(PoC)
tests
as
a
reliable
source
evaluation
patient's
condition.
Current
practices
still
based
on
laboratory
tests,
preceded
by
collection
biological
samples,
then
tested
clinical
conditions
trained
personnel
specialistic
equipment.
In
practice,
collecting
passive/active
physiological
behavioral
from
patients
real
time
feeding
artificial
intelligence
(AI)
models
can
significantly
improve
decision
process
regarding
diagnosis
treatment
procedures
via
omission
conventional
sampling
while
excluding
pathologists.
A
combination
novel
methods
digital
traditional
biomarker
detection
portable,
autonomous,
miniaturized
revolutionize
medical
diagnostics
coming
years.
This
article
focuses
comparison
modern
techniques
AI
machine
learning
(ML).
presented
technologies
will
bypass
laboratories
start
being
commercialized,
should
lead
or
substitution
current
Their
application
PoC
settings
technology
accessible
every
patient
appears
be
possibility.
Research
this
field
is
expected
intensify
Technological
advancements
sensors
biosensors
anticipated
enable
continuous
real-time
analysis
various
omics
fields,
fostering
early
disease
intervention
strategies.
integration
health
platforms
would
predictive
personalized
healthcare,
emphasizing
importance
interdisciplinary
collaboration
related
scientific
fields.
Periodontology 2000,
Journal Year:
2024,
Volume and Issue:
95(1), P. 220 - 231
Published: June 1, 2024
Periodontal
diseases
pose
a
significant
global
health
burden,
requiring
early
detection
and
personalized
treatment
approaches.
Traditional
diagnostic
approaches
in
periodontology
often
rely
on
"one
size
fits
all"
approach,
which
may
overlook
the
unique
variations
disease
progression
response
to
among
individuals.
This
narrative
review
explores
role
of
artificial
intelligence
(AI)
diagnostics
periodontology,
emphasizing
potential
for
tailored
strategies
enhance
precision
medicine
periodontal
care.
The
begins
by
elucidating
limitations
conventional
techniques.
Subsequently,
it
delves
into
application
AI
models
analyzing
diverse
data
sets,
such
as
clinical
records,
imaging,
molecular
information,
its
training.
Furthermore,
also
discusses
research
community
policymakers
integrating
Challenges
ethical
considerations
associated
with
adopting
AI-based
tools
are
explored,
need
transparent
algorithms,
safety
privacy,
ongoing
multidisciplinary
collaboration,
patient
involvement.
In
conclusion,
this
underscores
transformative
advancing
toward
paradigm,
their
integration
practice
holds
promise
ushering
new
era
BMC Oral Health,
Journal Year:
2024,
Volume and Issue:
24(1)
Published: March 2, 2024
Abstract
Background
The
IL-23/IL-17
axis
plays
an
important
role
in
the
immunopathogenesis
of
periodontal
disease.
A
systematic
review
was
conducted
to
synthesize
all
research
reporting
on
levels
gingival
crevicular
fluid
(GCF)
from
subjects
with
gingivits,
and
periodontitis,
compared
healthy
controls.
Methods
protocol
followed
PRISMA,
Cochrane
guidelines,
registered
Open
Science
Framework
(OSF):
https://doi.org/10.17605/OSF.IO/7495V
.
search
electronic
databases
PubMed/MEDLINE,
Scopus,
Google
Schoolar,
November
15th,
2005,
May
10th,
2023.
quality
studies
assessed
using
JBI
tool
for
cross-sectional
studies.
Results
strategy
provided
a
total
2,098
articles,
which
12
investigations
met
inclusion
criteria.
number
patients
studied
537,
337
represented
case
group
(subjects
gingivitis,
chronic
periodontitis),
200
control
(periodontally
subjects).
ages
ranged
20
50
years,
mean
(SD)
36,6
±
4,2,
47%
were
men,
53%
women.
75%
collected
GCF
samples
absorbent
paper
strips,
analyzed
cytokine
IL-17
individually.
In
addition,
qualitative
analysis
revealed
that
there
are
differences
between
gingivitis
Conclusions
Thus,
could
be
used
future
as
diagnostic
distinguish
diseases.
Current Oncology,
Journal Year:
2024,
Volume and Issue:
31(9), P. 5255 - 5290
Published: Sept. 6, 2024
Artificial
intelligence
(AI)
is
revolutionizing
head
and
neck
cancer
(HNC)
care
by
providing
innovative
tools
that
enhance
diagnostic
accuracy
personalize
treatment
strategies.
This
review
highlights
the
advancements
in
AI
technologies,
including
deep
learning
natural
language
processing,
their
applications
HNC.
The
integration
of
with
imaging
techniques,
genomics,
electronic
health
records
explored,
emphasizing
its
role
early
detection,
biomarker
discovery,
planning.
Despite
noticeable
progress,
challenges
such
as
data
quality,
algorithmic
bias,
need
for
interdisciplinary
collaboration
remain.
Emerging
innovations
like
explainable
AI,
AI-powered
robotics,
real-time
monitoring
systems
are
poised
to
further
advance
field.
Addressing
these
fostering
among
experts,
clinicians,
researchers
crucial
developing
equitable
effective
applications.
future
HNC
holds
significant
promise,
offering
potential
breakthroughs
diagnostics,
personalized
therapies,
improved
patient
outcomes.
Life,
Journal Year:
2024,
Volume and Issue:
14(6), P. 727 - 727
Published: June 5, 2024
Salivary
glands
tumors
are
uncommon
neoplasms
with
variable
incidence,
heterogenous
histologies
and
unpredictable
biological
behaviour.
Most
located
in
the
parotid
gland.
Benign
salivary
represent
54–79%
of
cases
pleomorphic
adenoma
is
frequently
diagnosed
this
group.
malignant
that
more
commonly
adenoid
cystic
carcinomas
mucoepidermoid
carcinomas.
Because
their
diversity
overlapping
features,
these
require
complex
methods
evaluation.
Diagnostic
procedures
include
imaging
techniques
combined
clinical
examination,
fine
needle
aspiration
histopathological
investigation
excised
specimens.
This
narrative
review
describes
advances
diagnosis
unusual
tumors—from
histomorphology
to
artificial
intelligence
algorithms.
Photodiagnosis and Photodynamic Therapy,
Journal Year:
2024,
Volume and Issue:
46, P. 104106 - 104106
Published: April 1, 2024
FT-IR
is
an
important
and
emerging
tool,
providing
information
related
to
the
biochemical
composition
of
biofluids.
It
demonstrate
that
there
efficacy
in
separating
healthy
diseased
groups,
helping
establish
uses
as
fast
screening
tool.
BMC Oral Health,
Journal Year:
2025,
Volume and Issue:
25(1)
Published: Jan. 17, 2025
Aurora
kinase
A
(AurkA)
plays
a
vital
role
in
mitosis
and
is
therefore
critical
tumors
development
progression.
There
are
few
studies
on
AurkA
expression
salivary
gland
tumors.
The
aim
of
the
present
study
was
to
evaluate
pattern
most
common
benign
malignant
by
immunohistochemistry.
In
this
retrospective
cross-sectional
study,
68
cases
including
25
pleomorphic
adenomas
(PAs),
21
adenoid
cystic
carcinomas
(ADCa),
15
mucoepidermoid
(MEC),
7
normal
glands
(NSG)
were
enrolled
from
archive
Department
Pathology
Shiraz
School
Dentistry,
Iran.
tissue
samples
assessed
immunohistochemical
method
analyzed
using
statistical
analysis
(p
<
0.05).
Of
total
analyzed,
majority
found
involve
minor
compared
major
0.001).
addition,
all
lesions
studied
expressed
AurkA.
More
than
half
tumor
tissues
showed
staining
percentages
between
26
50%
76-100%
NSG
=
0.08).
44.1%
cases,
cells
had
weak
score,
27.9%
moderate
score
rest
(27.9%)
strong
0.64).
Although
observed
be
tissues,
further
needed
clearly
understand
possibility
it
as
diagnostic,
prognostic
therapeutic
factor.
Research Square (Research Square),
Journal Year:
2025,
Volume and Issue:
unknown
Published: Jan. 23, 2025
Abstract
Background
Despite
surgery
is
the
recommended
treatment
for
oral
cancer
patients,
little
known
about
intraoperative
blood
loss
in
this
population.
This
study
sought
to
identify
risk
factors
haemorrhage
resection
and
free
flap
reconstruction
surgery,
develop
a
machine
learning-based
predictive
model.
Methods
retrospective
cohort
included
patients
with
who
underwent
fibular
at
tertiary
hospital.
Demographic
clinical
parameters
were
selected
using
Recursive
Feature
Elimination
algorithm.
The
final
model
further
analysis
was
after
considering
precision,
accuracy,
area
under
curve.
Results
A
total
of
452
individuals
had
met
criteria,
179
(39.6%)
experiencing
hemorrhage,
which
results
higher
inpatient
expenses
longer
durations
stay.
Subsequently,
11
47
variables
picked
learning
building.
In
comparison,
Random
Forest
highest
curve
(AUC)
(0.835,
95%
CI
0.773–0.898),
accuracy.
Further
feature
importance
evaluation
Shapley
additive
explanation
revealed
that
hemoglobin,
surgical
duration,
bilirubin,
leucocyte
count,
tumor
size,
albumin,
Charlson
comorbidity
index
score
significant
bleeding.
nomogram
algorithm
utilizing
listed
above
used
interpret
predict
possibility
operative
hemorrhage
Individualized
undergoing
reconstructive
surgery.
Conclusions
Hemoglobin,
proved
be
predictors
can
applied
bleed
helped
provide
more
adequate
preoperative
evaluation,
preparation
optimal
resource
utilization.
Oral,
Journal Year:
2025,
Volume and Issue:
5(2), P. 26 - 26
Published: April 9, 2025
Introduction:
Salivary
biomarkers
have
been
extensively
studied
in
relation
to
oral
disease,
such
as
periodontal
cancer,
and
dental
caries,
well
systemic
conditions
including
diabetes,
cardiovascular
diseases,
neurological
disorders.
Literature
Review:
A
systematic
literature
review
was
conducted,
analyzing
recent
advancements
salivary
biomarker
research.
Databases
PubMed,
Scopus,
Web
of
Science
were
searched
for
relevant
studies
published
the
last
decade.
The
selection
criteria
included
focusing
on
identification,
validation,
clinical
application
diagnosing
diseases.
Various
detection
techniques,
enzyme-linked
immunosorbent
assay
(ELISA),
polymerase
chain
reaction
(PCR),
mass
spectrometry,
biosensor
technologies,
reviewed
assess
their
effectiveness
analysis.
Specific
biomarkers,
inflammatory
cytokines,
oxidative
stress
markers,
microRNAs,
identified
reliable
indicators
disease
progression.
Current
Trends
Future
Perspectives:
Advances
proteomics,
genomics,
metabolomics
significantly
enhanced
ability
analyze
with
high
sensitivity
specificity.
Despite
promising
findings,
challenges
remain
standardizing
sample
collection,
processing,
analysis
ensure
reproducibility
applicability.
Conclusions:
research
should
focus
developing
point-of-care
diagnostic
tools
integrating
artificial
intelligence
improve
predictive
accuracy
biomarkers.
