Can Elective Single Embryo Transfer (eSET) with AI Integration Become the Future of IVF?
Zeev Shoham
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Journal of IVF-Worldwide,
Journal Year:
2025,
Volume and Issue:
3(1)
Published: Feb. 25, 2025
This
manuscript
examines
whether
elective
single
embryo
transfer
(eSET)
should
be
mandated
in
all
IVF
cycles,
assessing
its
clinical
benefits,
challenges,
and
global
implementation.
Evidence
shows
that
eSET
significantly
reduces
multiple
pregnancies
associated
complications
while
maintaining
cumulative
live
birth
rates.
However,
ethical,
regulatory,
practical
considerations
complicate
universal
enforcement.
While
a
standardized
policy
offers
advantages,
potential
drawbacks
include
restricted
patient
autonomy
the
need
for
additional
cycles
some
instances.
To
navigate
these
complexities,
advocates
balanced
approach:
promoting
as
preferred
standard
allowing
individualized
decisions
based
on
patient-specific
factors.
Alternative
models—such
partial
mandates
or
incentive-based
strategies—are
explored
to
enhance
outcomes
without
imposing
rigid
requirements.
nuanced
perspective
balances
safety,
treatment
efficacy,
shared
decision-making
fertility
care.
Language: Английский
Reporting Quality of AI Intervention in Randomized Controlled Trials in Primary Care: Systematic Review and Meta-Epidemiological Study
Journal of Medical Internet Research,
Journal Year:
2025,
Volume and Issue:
27, P. e56774 - e56774
Published: Feb. 25, 2025
Background
The
surge
in
artificial
intelligence
(AI)
interventions
primary
care
trials
lacks
a
study
on
reporting
quality.
Objective
This
aimed
to
systematically
evaluate
the
quality
of
both
published
randomized
controlled
(RCTs)
and
protocols
for
RCTs
that
investigated
AI
care.
Methods
PubMed,
Embase,
Cochrane
Library,
MEDLINE,
Web
Science,
CINAHL
databases
were
searched
until
November
2024.
Eligible
studies
or
full
exploring
was
assessed
using
CONSORT-AI
(Consolidated
Standards
Reporting
Trials–Artificial
Intelligence)
SPIRIT-AI
(Standard
Protocol
Items:
Recommendations
Interventional
checklists,
focusing
intervention–related
items.
Results
A
total
11,711
records
identified.
In
total,
19
21
RCT
35
included.
overall
proportion
adequately
reported
items
65%
(172/266;
95%
CI
59%-70%)
68%
(214/315;
62%-73%)
protocols,
respectively.
percentage
specific
item
ranged
from
11%
(2/19)
100%
(19/19)
10%
(2/21)
(21/21),
exhibited
similar
characteristics
trends.
They
lack
transparency
completeness,
which
can
be
summarized
three
aspects:
without
providing
adequate
information
regarding
input
data,
mentioning
methods
identifying
analyzing
performance
errors,
stating
whether
how
intervention
its
code
accessed.
Conclusions
could
improved
protocols.
helps
promote
transparent
complete
with
Language: Английский
Neural networks pipeline for quality management in IVF laboratory
Sergei Sergeev,
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Iuliia Diakova,
No information about this author
Lasha Nadirashvili
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et al.
Journal of IVF-Worldwide,
Journal Year:
2024,
Volume and Issue:
2(4)
Published: Oct. 23, 2024
This
study
introduces
a
novel
neural
network-based
pipeline
for
predicting
clinical
pregnancy
rates
in
IVF
treatments,
integrating
both
and
laboratory
data.
We
developed
metamodel
combining
deep
networks
Kolmogorov-Arnold
networks,
leveraging
their
complementary
strengths
to
enhance
predictive
accuracy
interpretability.
The
achieved
robust
performance
metrics
after
training
fitting
on
11500
cases:
=
0.72,
AUC
0.75,
F1
score
0.60,
Matthews
Correlation
Coefficient
of
0.42.
According
morpho-kinetical
embryo
evaluation,
our
model’s
PRC
0.66
significantly
improves
over
existing
time-lapse
systems
prediction,
demonstrating
better
handling
imbalanced
metamodel’s
calibration
(Brier
0.20,
expected
error
0.06,
maximum
0.12,
Hosmer-Lemeshow
test
p-value
0.06)
indicate
reliability
outcomes.
validated
the
reproducibility
using
an
independent
dataset
665
treatment
cycles,
showing
close
alignment
between
predicted
actual
(58.9%
vs.
59.1%).
With
Bayesian
method,
we
proposed
framework
historical
data
with
real-time
predictions
from
enabling
transition
retrospective
prospective
analysis.
Our
approach
extends
beyond
conventional
selection,
incorporating
post-analytical
phase
evaluation
laboratory.
comprehensive
enables
detailed
analysis
across
different
patient
subpopulations
time
periods,
facilitating
identification
systemic
issues
protocol
optimization.
ability
track
probabilities
staff
members
allows
outcome
prediction
assessment
efficacy,
providing
data-driven
strategy
continuous
improvement
assisted
reproductive
technology.
Language: Английский