Artificial Intelligence Role in IVF and Reproductive Medicine

This report provides an overview of the application of Artificial Intelligence (AI) in reproductive medicine, focusing on its potential to improve in vitro fertilization (IVF) outcomes and address current challenges. It discusses the use of AI and machine learning (ML) in various aspects, including sperm and embryo selection, optimization of ovarian reserve parameters, and analysis of patient outcomes. The report highlights the advantages of AI in image analysis, error reduction, and efficiency improvement, while also acknowledging the limitations, such as data bias, lack of standardization, and data privacy concerns. The report also explores potential solutions like data hubs and federal learning to address these challenges, emphasizing the importance of high-quality data for AI model development. The limitations of the current research in the integration of the analysis of obtained data are also discussed, and the need for more research in diagnosis, treatment, and automatic reproduction is highlighted. Furthermore, the report touches upon the significance of data privacy and governance in healthcare, particularly the use of federal learning (FL) as a solution that enables algorithms training without the exchange of datasets. Overall, the report emphasizes the need for more research and data sharing to unlock the full potential of AI in reproductive medicine.

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Artificial Intelligence in Reproductive Medicine
Despite many problems, clinical pregnancy rate since the birth of the in vitro
fertilization baby. It is still difficult to predict the chances of success and any cause of failures in
a pregnancy because there are no methods to accurately judge the quality of the sperm or egg.
Artificial intelligence (AI) is believed be of great efficiency and efficacy in the clinical sector
and may be the solution to these dilemmas.
Overview of Artificial Intelligence and Machine Learning
AI is the capability of machines to learn and show intelligence in contrast to human and
animal natural intelligence. AI can be used in image analysis where images from the incubator
are analysed to obtain morphokinetic and morphological information hence minimizing human
errors. Machine learning (ML) helps computer algorithms to create a model between two data
inputs then use the models for prediction. For accuracy ML algorithms need a large quantity of
high quality data. With the recent breakthrough in medicine, large volume of complex
biomedical data has exceeded doctors’ capabilities to analyse using conventional methods hence
the need for AI.
Overview of AI in reproductive medicine
AI would be of great importance in selection and evaluation of sperm and embryo and
optimization of ovarian reserve parameters assessment. AI could be used in analyzing different
infertility patient outcomes as well as in third party reproduction. AI would provide a better
method in human embryo evaluation hence can identify main embryo developmental stages. The

beginning point of applying AI in IVF lab is in the selection and development of embryo due to
its high level of success and high quality image info. Different experiments have been used to
examine the live birth rate and implantation ability of IVF embryo from images. A lot is
unknown on this topic but AI and ML are the ways to uncover what is hidden in these massive
medical data. More research on ART treatment is being done on how increase implantation and
efficacy. AI image analysis would be used to reduce errors, improve recognition efficiency and
reduce minimal manual categorization workload by adapting automatic grouping of embryos,
oocytes and sperms as well as consent sharing and gamete donation and matching. More research
is needed to integrate AI into medicine, though much of the focus is on embryo and sperm image
analysis.
Problems faced by AI in IVF
The greatest limitation is the inability to predict IVF outcomes despite the improved
accuracy from 59% to 84.4% with the application of AI hence the model cannot be used in
medical practice. Another limitation is the bias of data as much of data used is collected from
one clinic hence other ethnic are not considered. Patient factors such as medical history,
demographics, clinical pregnancy outcomes, follicular growth patterns and pre-implantation
genetic screening and diagnosis are controlled by incompatible systems hence researchers find it
more difficult to analyse and relate these factors to one patient. Researcher cannot deduce the
right conclusions due to lack of standardization within clinics. Another limitation is the variation
of data types as oocytes, images, embryos, patients and cycles.
Clinics are more independent nowadays and do not easily share their clinical data,
which affects the quality of research. HFEA national registry, being a centralized database,
would be a breach of international standards if they share medical data. The available data in

clinics miss some lifestyle factors which could be of essential role in prediction and could be
confounding variables in the studies. The existing literature is contrasting on the effect of
lifestyle on IVF and fertility. A study in Spain indicates that women from the highest quantile
have a 44% lower chances in having pregnancy complications as compared to those in the lower
quartile despite all adhering to a Mediterranean diet.
The accuracy of Ml is associated to certain factors such as quality and quantity of data,
which could lead to poor decision if the data is biased. Selection bias from collected data lead to
poor results of ML models in medical settings. It stresses on the need for data collection and
sharing which would enable efficient utilization of high quality data. In consideration to these
complex application, modern Deep Learning models use large quantity of parameters learned
and validated on clinical datasets. Large data volumes are needed for models that can attain
clinical grade accuracy as well as fairness, equity and safety of the data. The limitations derived
from the data quantity and quality that influences the applicability, generalizability and
performance of the trained model are small quantity, retrospective and single source data models.
The research lacks integration of the analysis of obtained data, though limited to application of
algorithms in predicting and classification. The use of AI in medical reproduction limited and
semi-automatic, calling for more research in diagnosis, treatment and automatic reproduction.
There is need to find a way to acquire the quantity of data needed to make high quality
advancements in reproductive medicine as this data is the way to AI medicine development.
Data hub/ Federal learning as a solution
Unlike other forms of data, heath data is sensitive and subject to regulation and cannot
be used without ethical approval and patient consent. Federal learning (FL) is an approach
designed to address data privacy and governance training algorithms without exchange of

datasets. Application of FL in digital heath helps to attain insights across institutions. In FL, ML
process is done locally in the institutions whereby only model features are attained. Substra is a
framework used for collaborative and traceable ML. it is also used to maintain data privacy by
tracking any interactions with the data. The development of Substra is based on 3 principles:
collaboration (the state of the art ML is within a network of partners), privacy (Substra grands
data access to the owner or an authorized algorithm) and traceability (ML operations are traced
to ensure data privacy and also maintain the training of predictive model untampered).