Leveraging machine learning to improve assisted reproductive technology outcomes for those with endometriosis.

About the Project

Infertility is a disease of the reproductive system defined as a failure to achieve a clinical pregnancy after 12 months or more of regular unprotected intercourse. Infertility is common, affecting 1 in 6 heterosexual couples, and rates are rising, especially with the current trend of delayed childbearing. Endometriosis is an oestrogen dependent chronic inflammatory condition that affects 1 in 10 of the general population and up to 1 in 2 suffering with infertility. Infertility can lead to distress, depression, discrimination and ostracism. Although assisted reproductive techniques (ART) have advanced considerably since their advent, these technologies remain mostly unsuccessful (live birth rate (LBR) of approximately 27% per embryo transfer (ET)), inaccessible and unaffordable. A high proportion of women with endometriosis will conceive without ART but we do not know why the inflammation present seems to be a problem when trying to conceive for some but not all. Therefore, it is of upmost importance to understand these differences between fertile women with endometriosis and infertile women with endometriosis to optimise ART protocols and ensure best outcomes for patients. Digital twins are computational models of real-world patients that aim to predict their future outcomes. These twins are based on machine learning (ML) and mathematical models. This project will combine a histological dataset of ovulation timed endometrial biopsies (n=100) with demographic data, ML, and mathematical models to predict which patients will be able to achieve a pregnancy without ART assistance. We hypothesise that factors most influential in the prediction of ART success can be targeted to increase LBR.

Objectives

Predict the likelihood of pregnancy in women with/without endometriosis.

WP1: Histology and dataset curation

Dataset of successful (women with endometriosis and previous baby) and unsuccessful (women with endometriosis having fertility treatment) cohorts with a timed endometrial biopsy using retrospective biobank specimens from women treated at primary institution will be curated.

WP2: Population-level ML - Can we predict who will have a live birth?

Using curated data, multi-modal deep learning algorithms used to integrate histological features with longitudinal patient data to predict which women will have successful pregnancy. Starting with standard ML algorithms, eg.convolutional neural networks, the candidate will test several algorithms to predict who will have a live birth.

WP3: Validation ML – Can we validate our prediction model?

Prediction model will be used on prospective samples collected throughout the PhD from primary institute, data will provide external validation of the algorithm.

WP4: Digital twin of the endometrium – Can we explain the predictions?

A digital twin will be developed linking aspects of the endometrium, such as vascularisation and gland formation, to outcomes. This can be personalised to an individual such that we can eventually use the twin to develop and test possible therapies to enhance fertility in those with endometriosis.

Novelty

This collaborative interdisciplinary project uses in silico modelling and longitudinal clinical data to build digital twins to predict success of pregnancy in those with endometriosis, with the ultimate aim to provide personalised fertility therapies.

Timeliness

With rising infertility rates and decreasing NHS funding for treatments this project directly aligns with the MRC to drive more comprehensive understanding of causes and progression of a common women’s health conditions that occur with high incidence in advanced reproductive age.

Experimental Approach

This PhD incorporates histological methods, AI, mathematical modelling, and software development.

The work to be undertaken will be conducted between the Department of Women and Children’s Health and the Department of Cardiovascular and Metabolic Medicine as a collaboration between biomedical engineers (Dr El-Bouri) and clinical fertility experts (Dr Tempest). This project will ideally suit individuals with a background in applied mathematics, computer science, or engineering.