Development of Artificial Intelligence Solution for Prediction of Preterm Birth

Introduction

This is an exciting opportunity to study a PhD as part of a cotutelle arrangement between Coventry University, UK and Deakin University, Melbourne, Australia. Starting at Coventry, the PhD Student will graduate with two PhDs, one from Deakin University and one from Coventry University, each of which recognises that the programme was carried out as part of a jointly supervised doctoral programme.

The programme is for a duration of 3.5 years (funding only for 3.5 years, maximum allowed time 4 years) and scheduled to commence in January 2025. 

Project details

Preterm birth is a major global health burden affecting up to 15 million pregnancies per annum. It confers considerable neonatal mortality within one month of birth and numerous health risks for surviving babies. The main device currently available for routine uterine contraction detection is the tocodynamometer, which suffers from several drawbacks and provides limited information for the prediction of premature birth.

Currently, there is no clinically acceptable and accurate prediction method in clinical use for preterm labour. The more comfortable technique, electrohysterograhy (EHG), offers an alternative for uterine contraction monitoring, which could be used to identify preterm labour. As a minimum it requires only a pair of sticky electrodes placed on the lower abdomen. EHG, however, has not been used in clinical practice due to the difficulty of interpreting the raw EHG signals and unsatisfactory preterm prediction accuracy for clinical use from current research studies.

This interdisciplinary project aims to develop an innovative Artificial Intelligence (AI) solution to help achieve the quick acceptance of EHG as a reliable clinical monitoring tool and for accurate prediction of preterm labour. The data will be used is from existing datasets (two online databases, two in-house databases). The measurable objectives include: 1) Develop advanced algorithm to extract clinically useful features from the EHG recordings; 2) Develop AI algorithm to predict term/preterm labour based on deep learning method; 3) Pre-clinical efficacy test to further investigate and optimise the AI algorithm.

We seek a highly-talented, motivated, and open-minded candidate, with background in biomedical/electronic engineering, computer science or a related discipline. Experience in analysis of electrophysiological signals, programming language (e.g., Matlab, Python) as well as signal processing and machine learning techniques is highly desirable. Ability to manage time and work to strict deadlines is required.

Funding

Tuition fees and bursary

Benefits

The successful candidate will receive comprehensive research training including technical, personal and professional skills.

All researchers at Coventry University (from PhD to Professor) are part of the Doctoral and Researcher College, which provides support with high-quality training and career development activities. 

Candidate specification

Applicants must meet the admission and scholarship criteria for both Coventry University and Deakin University for entry to the cotutelle programme and apply at both Institutes

• Applicants should have graduated within the top 15% of their undergraduate cohort. This might include a high 2:1 in a relevant discipline/subject area with a minimum 70% mark (80% for Australian graduates) in the project element or equivalent with a minimum 70% overall module average (80% for Australian graduates).
• A Bachelor’s degree in a relevant field requiring at least four years of full-time study, and which normally includes a research component which is equivalent to at least 25% of a year’s full-time study in the fourth year, with achievement of a grade for the project equivalent to a H1 standard or 80%

 OR

• A Masters degree, with a significant research component, in a relevant subject area, with overall mark at minimum Distinction.
• • In addition, the mark for the Masters thesis (or equivalent) must be a minimum of 80%.
• Please note that where a candidate has 70-79% and can provide evidence of research experience to meet equivalency to the minimum first-class honours equivalent (80%+) additional evidence can be submitted and may include independently peer-reviewed publications, research-related awards or prizes and/or professional reports.
• Language proficiency (IELTS overall minimum score of 7.0 with a minimum of 6.5 in each component).

The potential to engage in innovative research and to complete the PhD within a prescribed period of study

Additional Requirements

• Experience in analysis of electrophysiological signals or similar
• Experience in signal processing, machine learning techniques
• Experience of a programming language (e.g., Matlab, Python)
• A record of presenting papers at conferences and/or of publishing peer reviewed research papers (desired);
• Ability to think innovatively and critically analyse data and results;
• Good written and oral communication skills
• Ability to manage time and work to strict deadlines

How to apply

To find out more about the project, please contact yuhang.xu@coventry.ac.uk

All applications require full supporting documentation, a covering letter, plus a 2000-word supporting statement showing how the applicant’s expertise and interests are relevant to the project.