Recent advances and applications of artificial intelligence in 3D bioprinting
Biophysics Reviews,
Год журнала:
2024,
Номер
5(3)
Опубликована: Июль 19, 2024
3D
bioprinting
techniques
enable
the
precise
deposition
of
living
cells,
biomaterials,
and
biomolecules,
emerging
as
a
promising
approach
for
engineering
functional
tissues
organs.
Meanwhile,
recent
advances
in
researchers
to
build
vitro
models
with
finely
controlled
complex
micro-architecture
drug
screening
disease
modeling.
Recently,
artificial
intelligence
(AI)
has
been
applied
different
stages
bioprinting,
including
medical
image
reconstruction,
bioink
selection,
printing
process,
both
classical
AI
machine
learning
approaches.
The
ability
handle
datasets,
make
computations,
learn
from
past
experiences,
optimize
processes
dynamically
makes
it
an
invaluable
tool
advancing
bioprinting.
review
highlights
current
integration
discusses
future
approaches
harness
synergistic
capabilities
developing
personalized
Язык: Английский
Advances and Challenges in 3D Bioprinted Cancer Models: Opportunities for Personalized Medicine and Tissue Engineering
Polymers,
Год журнала:
2025,
Номер
17(7), С. 948 - 948
Опубликована: Март 31, 2025
Cancer
is
the
second
leading
cause
of
death
worldwide,
after
cardiovascular
disease,
claiming
not
only
a
staggering
number
lives
but
also
causing
considerable
health
and
economic
devastation,
particularly
in
less-developed
countries.
Therapeutic
interventions
are
impeded
by
differences
patient-to-patient
responses
to
anti-cancer
drugs.
A
personalized
medicine
approach
crucial
for
treating
specific
patient
groups
includes
using
molecular
genetic
screens
find
appropriate
stratifications
patients
who
will
respond
(and
those
not)
treatment
regimens.
However,
information
on
which
risk
stratification
method
can
be
used
hone
cancer
types
likely
responders
agent
remains
elusive
most
cancers.
Novel
developments
3D
bioprinting
technology
have
been
widely
applied
recreate
relevant
bioengineered
tumor
organotypic
structures
capable
mimicking
human
tissue
microenvironment
or
adequate
drug
high-throughput
screening
settings.
Parts
autogenously
printed
form
tissues
computer-aided
design
concept
where
multiple
layers
include
different
cell
compatible
biomaterials
build
configurations.
Patient-derived
stromal
cells,
together
with
material,
extracellular
matrix
proteins,
growth
factors,
create
bioprinted
models
that
provide
possible
platform
new
therapies
advance.
Both
natural
synthetic
biopolymers
encourage
cells
biological
materials
models/implants.
These
may
facilitate
physiologically
cell-cell
cell-matrix
interactions
heterogeneity
resembling
real
tumors.
Язык: Английский
Automated Craniofacial Biometry with 3D T2w Fetal MRI
medRxiv (Cold Spring Harbor Laboratory),
Год журнала:
2024,
Номер
unknown
Опубликована: Авг. 14, 2024
ABSTRACT
Objectives
Evaluating
craniofacial
phenotype-genotype
correlations
prenatally
is
increasingly
important;
however,
it
subjective
and
challenging
with
3D
ultrasound.
We
developed
an
automated
landmark
propagation
pipeline
using
motion-corrected,
slice-to-volume
reconstructed
(SVR)
fetal
MRI
for
measurements.
Methods
A
literature
review
expert
consensus
identified
31
biometrics
MRI.
An
atlas
defined
anatomical
landmarks
served
as
a
template
subject
registration,
auto-labelling,
biometric
calculation.
assessed
108
healthy
controls
24
fetuses
Down
syndrome
(T21)
in
the
third
trimester
(29-36
weeks
gestational
age,
GA)
to
identify
meaningful
T21.
Reliability
reproducibility
were
evaluated
10
random
datasets
by
four
observers.
Results
Automated
labels
produced
all
132
subjects
0.03%
placement
error
rate.
Seven
measurements,
including
anterior
base
of
skull
length
maxillary
length,
showed
significant
differences
large
effect
sizes
between
T21
control
groups
(ANOVA,
p<0.001).
Manual
measurements
took
25-35
minutes
per
case,
while
extraction
approximately
5
minutes.
Bland-Altman
plots
agreement
within
manual
observer
ranges
except
mandibular
width,
which
had
higher
variability.
Extended
GA
growth
charts
(19-39
weeks),
based
on
280
fetuses,
future
research.
Conclusion
This
first
atlas-based
protocol
SVR
biometrics,
accurately
revealing
morphological
cohort.
Future
work
should
focus
improving
measurement
reliability,
larger
clinical
cohorts,
technical
advancements,
enhance
prenatal
care
phenotypic
characterisation.
Язык: Английский
Automated craniofacial biometry with 3D T2w fetal MRI
PLOS Digital Health,
Год журнала:
2024,
Номер
3(12), С. e0000663 - e0000663
Опубликована: Дек. 30, 2024
Evaluating
craniofacial
phenotype-genotype
correlations
prenatally
is
increasingly
important;
however,
it
subjective
and
challenging
with
3D
ultrasound.
We
developed
an
automated
label
propagation
pipeline
using
motion-
corrected,
slice-to-volume
reconstructed
(SVR)
fetal
MRI
for
measurements.
A
literature
review
expert
consensus
identified
31
biometrics
MRI.
An
atlas
defined
anatomical
landmarks
served
as
a
template
subject
registration,
auto-labelling,
biometric
calculation.
assessed
108
healthy
controls
24
fetuses
Down
syndrome
(T21)
in
the
third
trimester
(29-36
weeks
gestational
age,
GA)
to
identify
meaningful
T21.
Reliability
reproducibility
were
evaluated
10
random
datasets
by
four
observers.
Automated
labels
produced
all
132
subjects
0.3%
placement
error
rate.
Seven
measurements,
including
anterior
base
of
skull
length
maxillary
length,
showed
significant
differences
large
effect
sizes
between
T21
control
groups
(ANOVA,
p<0.001).
Manual
measurements
took
25-35
minutes
per
case,
while
extraction
approximately
5
minutes.
Bland-Altman
plots
agreement
within
manual
observer
ranges
except
mandibular
width,
which
had
higher
variability.
Extended
GA
growth
charts
(19-39
weeks),
based
on
280
fetuses,
future
research.
This
first
atlas-based
protocol
SVR
biometrics,
accurately
revealing
morphological
cohort.
Future
work
should
focus
improving
measurement
reliability,
larger
clinical
cohorts,
technical
advancements,
enhance
prenatal
care
phenotypic
characterisation.
Язык: Английский