The Hearing Test App for Android Devices: Distinctive Features of Pure-Tone Audiometry Performed on Mobile Devices
Medical Devices Evidence and Research,
Journal Year:
2024,
Volume and Issue:
Volume 17, P. 151 - 163
Published: April 1, 2024
Abstract:
The
popularity
of
mobile
devices,
combined
with
advances
in
electronic
design
and
internet
technology,
has
enabled
home-based
hearing
tests
recent
years.
purpose
this
article
is
to
highlight
the
distinctive
aspects
pure-tone
audiometry
performed
on
a
device
by
means
Hearing
Test
app
for
Android
devices.
first
version
was
released
decade
ago,
since
then
been
systematically
improved,
which
required
addressing
many
issues
common
majority
apps
testing.
discusses
techniques
calibration,
outlines
testing
procedure
how
it
differs
from
traditional
audiometry,
explores
potential
bone
conduction
testing,
provides
considerations
interpreting
including
test
duration
background
noise.
concludes
detailing
clinically
relevant
requiring
special
attention
during
interpretation
results
are
substantial
value
hundreds
thousands
active
users
worldwide,
as
well
other
apps.
Keywords:
self-testing,
threshold,
Language: Английский
Development and Comparison of Machine Learning and Deep Learning Models for Speech Audiometry Prediction
Applied Sciences,
Journal Year:
2025,
Volume and Issue:
15(6), P. 3071 - 3071
Published: March 12, 2025
Hearing
loss
significantly
impacts
daily
communication,
making
accurate
speech
audiometry
(SA)
assessment
essential
for
diagnosis
and
treatment.
However,
SA
testing
is
time-consuming
resource-intensive,
limiting
its
accessibility
in
clinical
practice.
This
study
aimed
to
develop
a
multi-class
classification
model
that
predicts
results
using
pure-tone
(PTA)
data,
enabling
more
efficient
automated
assessment.
To
achieve
this,
we
implemented
compared
MLP,
RNN,
gradient
boosting,
XGBoost
models,
evaluating
their
performance
accuracy,
F1
score,
log
loss,
confusion
matrix
analysis.
Experimental
showed
boosting
achieved
the
highest
86.22%,
while
demonstrated
balanced
performance.
The
MLP
85.77%
RNN
85.41%,
exhibiting
relatively
low
with
showing
limitations
due
temporal
dependency
of
PTA
data.
Additionally,
all
models
faced
challenges
predicting
class
2
(borderline
hearing
levels)
overlapping
data
distributions.
These
findings
suggest
machine
learning
particularly
XGBoost,
outperform
deep
prediction.
Future
research
should
focus
on
feature
engineering,
hyperparameter
optimization,
ensemble
approaches
enhance
validate
real-world
applicability.
proposed
could
contribute
automating
prediction
improving
efficiency
patient
care.
Language: Английский
The Hearing Test App for Android Devices: Distinctive Features of Pure-Tone Audiometry Performed on Mobile Devices [Letter]
Medical Devices Evidence and Research,
Journal Year:
2024,
Volume and Issue:
Volume 17, P. 213 - 214
Published: May 1, 2024
The
Hearing
Test
App
for
Android
Devices:
Distinctive
Features
of
Pure-Tone
Audiometry
Performed
on
Mobile
Devices"
by
M.
Masalski,
published
in
Medical
Evidence
and
Research.
1
article
reveals
the
innovative
use
mobile
devices
home
hearing
tests,
which
represents
a
remarkable
advance
given
growing
prevalence
loss
worldwide.The
app
devices,
developed
offers
practical
solution
early
identification
loss,
particularly
resource-constrained
environments.The
continuous
development
since
its
release
2013
impressive
number
downloads
over
two
million
demonstrates
importance
to
users
around
world.However,
this
paper
also
recognizes
some
limitations
weaknesses
inherent
audiometry.One
primary
concerns
is
reliability
calibration
coefficients,
are
crucial
generation
accurate
test
results.The
notes
that
although
pre-determined
coefficients
most
reliable,
there
degree
variability
can
affect
precision
test.In
addition,
testing
environment,
such
as
background
noise
level,
significantly
results,
potentially
leading
an
overestimated
threshold.Based
these
observations,
it
recommended
future
research
area
should
explore
more
advanced
techniques
adapt
diverse
headphones
used
consumers.It
would
be
beneficial
develop
algorithms
compensate
environmental
effectively,
improve
accuracy
audiometry
tests.
2,3Finally,
user
education
proper
interpretation
tests
improved.This
will
help
reduce
risk
misdiagnosis
ensure
serves
reliable
tool
professional
audiologists.
Language: Английский
The multilingual digits-in-noise (DIN) test: development and evaluation
International Journal of Audiology,
Journal Year:
2024,
Volume and Issue:
unknown, P. 1 - 11
Published: Aug. 29, 2024
To
develop
a
methodologically
uniform
digits-in-noise
(DIN)
test
in
17
different
languages.
Language: Английский
The Hearing Test App for Android Devices: Distinctive Features of Pure-Tone Audiometry Performed on Mobile Devices [Response to Letter]
Medical Devices Evidence and Research,
Journal Year:
2024,
Volume and Issue:
Volume 17, P. 215 - 216
Published: June 1, 2024
The
objective
of
this
response
is
to
Language: Английский
ChatGPT as a decision-support tool for better self-monitoring of hearing
medRxiv (Cold Spring Harbor Laboratory),
Journal Year:
2024,
Volume and Issue:
unknown
Published: Dec. 16, 2024
Abstract
Background
The
rapid
development
of
large
language
model
chatbots,
such
as
ChatGPT,
has
created
new
possibilities
for
healthcare
support.
This
study
investigates
the
feasibility
integrating
self-monitoring
hearing
(via
a
mobile
app)
with
ChatGPT’s
decision-making
capabilities
to
assess
whether
specialist
consultation
is
required.
In
particular,
evaluated
how
accuracy
make
recommendation
changed
over
periods
up
12
months.
Methods
ChatGPT-4.0
was
tested
on
dataset
1,000
simulated
cases,
each
containing
monthly
threshold
measurements
Its
recommendations
were
compared
opinions
5
experts
using
percent
agreement
and
Cohen’s
Kappa.
A
multiple-response
strategy,
selecting
most
frequent
from
trials,
also
analyzed.
Results
ChatGPT
aligned
strongly
experts’
judgments,
scores
ranging
0.80
0.84.
Accuracy
improved
0.87
when
multiple-query
strategy
employed.
those
cases
where
all
unanimously
agreed,
achieved
near-perfect
score
0.99.
It
adapted
its
criteria
extended
observation
periods,
seemingly
accounting
potential
random
fluctuations
in
thresholds.
Conclusions
significant
decision-support
tool
monitoring
hearing,
able
match
expert
adapting
effectively
time-series
data.
Existing
self-testing
apps
lack
tracking
evaluating
changes
time;
could
fill
this
gap.
While
not
without
limitations,
offers
promising
complement
self-monitoring.
can
enhance
processes
potentially
encourage
patients
seek
clinical
expertise
needed.
Graphical
abstract
Language: Английский