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Fertility: In Vitro, in Silico, In Clinico


We aim to improve the success rate of In-Vitro Fertilization (IVF) by using computational models, data science, and artificial intelligence. A combination of mathematical and statistical methodologies and close collaboration with experimentalists and clinicians will allow us to better understand the complex biological processes involved in fertility and reproduction.  

Improving IVF would lead to significant societal impact across the world; in developed countries, infertility rates are rising, while in developing countries access to IVF remains limited. Enhancing the effectiveness and accessibility of IVF treatment will improve the lives of thousands of infertile couples and their families. Moreover, by leveraging interdisciplinary collaboration between academia and clinics, IVF modelling can lead to significant advancements in reproductive medicine and contribute to broader research related to health and wellbeing. 

This interdisciplinary, impactful IVF research is directly relevant to the Global Challenges Research Fund and to the UKRI Challenge Fund research aims, as it addresses multiple challenges related to global health and wellbeing, including reducing health inequalities, improving healthcare delivery and outcomes, and advancing research and innovation in reproductive medicine. Additionally, advances in IVF modelling can contribute to broader research themes related to sustainable development and economic growth, and fulfil the urgent need for more effective and accessible healthcare solutions. 


Project Summary

Around 50,000 people undergo fertility treatment in the UK annually, but IVF can be arduous and expensive, with each round costing more than £7,000. Many patients have to undertake IVF treatment privately when they fall outside NHS’ criteria. Worldwide, more than 2.5 million in vitro fertilisation (IVF) cycles are performed every year. Meanwhile infertility rates continue to rise whereas the success rate in each IVF round is only ~23%; this rate has not improved for many years. By 2029, the IVF market is expected to grow with a fast rate of ~9%, to 1063 million GBP.

A holistic, interdisciplinary, academia-clinic approach to IVF is more essential than ever to tackle IVF’s multiple and multifaceted challenges. We are going to look at IVF from many angles – eggs, sperm, embryos. Some open challenges that are amenable to quantification, mathematical modelling, data analytics and AI have been identified and are as follows:

1) Select the best egg/sperm/embryo

2) Freeze and thaw embryos without damage

3) Handle storage/freezing space

4) Transport eggs/sperm/embryos from other clinics/banks without damage

5) Automate clinical protocols (currently mostly manual)

6) Improve the infertility diagnosis pathway

7) Reduce pollution by IVF clinics – sustainability

8) Democratisation of IVF (same-sex couples, cancer patients, developing countries)

Through our interdisciplinary, academia-clinic network activities we are going to discuss and describe these challenges in more detail and prioritise them in terms of feasibility and urgency. Our network consists of academics from all GW4 universities as well as other universities in the UK and abroad, many clinics and some companies related to IVF.

Working closely with all partners, we are going to devise a short-term plan and a long-term strategy for tackling them in order to improve IVF success rates, reduce cost and improve patients’ wellbeing.

We are actively welcoming new network members to join us from academia, clinic or industry.

University of Bath
University of Bristol
Cardiff University
University of Exeter