The Potential of Artificial Intelligence in Improving IVF Success Rates

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The Potential of Artificial Intelligence in
Improving IVF Success Rates
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What is IVF?
What is AI?
In vitro fertilization (IVF) is A type of
assisted reproductive technology that involves
combining eggs and sperm in a laboratory dish and
then transferring the resulting embryos to the
uterus.
AI, or Artificial Intelligence, is a field of
computer science that focuses on developing
intelligent machines or programs capable of
performing tasks that would typically require
human intelligence.
Addition Of Sperm in Lab Dish To Make
Fertilization
Retrieval Of Eggs From Ovaries
Transformation To Mother Uterus
Formation of Embryo in a Lab
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Key Takeaways
In this presentation we will be looking into the
methods of applying AI for the selection of
embryo for IVF.

• Discover the need for additional tools in
  embryonic screening.
• Explore operator bias in Manual Embryonic
  Morphological Assessment.
• Know about the AI-driven method, STORK-A for
  Predicting Ploidy Status.
• Understand the Barriers to Clinical Usage of AI
  Tools in IVF.
• Realize the Importance of Large-Scale RCTs for
  Validation and Regulatory Approval.

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Introducing STORK-A
A recent study published in The Lancet Digital
Health by Josue Barnes and colleagues introduced
an innovative AI-driven method called STORK-A.
Reportedly, this non-invasive technique can
predict the ploidy status of embryos, thereby
making it easy to select healthy blastocysts for
implantation.
We must know that STORK-A is never intended to be
used as a replacement option for conventional
preimplantation genetic testing for aneuploid
status. Instead, it would strictly act as a
complementary way to assist clinicians in their
decision-making.
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Since 2020, multiple studies have showcased the
promise of AI tools in embryo selection. Take the
Convolutional Neural Network (CNN) tool, for
instance, developed by Sonya Diakiw and
colleagues, which resulted in about a 12
reduction in time-to-pregnancy. Among
other notable mentions are Charles Bormann and
colleagues studies, where CNN outperformed 15
embryologists in accurately predicting embryos
with the highest implantation potential. Such
findings underscore the potential of Artificial
Intelligence in Improving IVF.
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Complications Around
Despite a string of encouraging results, a bunch
of barriers hinder the clinical implementation of
AI tools for IVF. Starting with the black box
effect, which is caused by the complexity and
proprietary algorithms that are seen in typical
AI-based approaches. A good way to address this
would be to opt for interpretable and transparent
models to do away with hidden biases.
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How To Resolve?

• A handful of first-base approaches have been
  identified to act as an immediate fix and
  include
• Rigorous evaluation via high-quality Randomised
  Controlled Trials (RCTs) to assess the efficacy
  of AI-driven tools for embryo selection. A
  necessary doing before any regulatory approval
  comes through for widespread clinical adoption.
• In the absence of published RCTs evaluating
  AI-driven tools meant specifically for IVF,
  externally robust and validated approaches and
  subsequent validation can come in handy.

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AI-assisted Embryo Selection
A systematic review and meta-analysis from 2022
have already revealed the inherent disparities in
IVF outcomes. Especially with black women
experiencing higher rates of spontaneous
abortions and lower live birth rates compared to
white females. Although recent studies
conducted by Hajirasouliha and colleagues offered
validation across multiple populations, the
disparity of race or ethnicity data of
participants still remains a vital aspect for all
forms of future research. Inclusive
participation across multiple trials and studies
is vital to ensuring generalizability and
equitable benefits.
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Undoubtedly, the integration of AI in embryo
selection for IVF holds untold potential for the
future. However, significant challenges lie
ahead that will call for repeated reassuring on
multiple grounds before such methods can become a
part of routine clinical decisions.