Title: Intelligent prediction of preterm birth using AI-empowered electrohysterography sensing
Funder: Department of Health and Social Care

Abstract: Background Preterm birth increases not only the risks of neonatal mortality, but numerous health risks for surviving babies. Pregnant women have been defined by the NIHR as an under-served group whose inclusion needs to be improved in clinical research.The public sector costs of preterm delivery in England and Wales have been estimated at £3.4bn annually. Prediction of preterm birth is crucial to deliver interventions that are time-dependent for neonatal survival. The main device currently available for routine uterine contraction detection is the tocodynamometer (TOCO), which suffers from several drawbacks and provides limited information for the prediction of premature birth. The more comfortable technique called electrohysterograhy (EHG) offers an alternative for uterine contraction monitoring. Aims and Objectives Using available EHG signals from existing datasets (one online database, one from our previous research), we aim to develop an AI solution to: 1) translate EHG to TOCO-like waveforms that could help facilitate the clinical acceptance for EHG adoption into maternal monitoring practice; and 2) predict preterm birth by an AI algorithm. Work plan, methods and timelines The project has four work packages: 1) Develop TOCO-like waveform generation algorithm based on wavelet and Independent Component Analysis using online database (Months 1-4) 2) Develop an AI algorithm to predict preterm labour based on Dictionary Learning and Sparse Representation using online database (Months 4-10) 3) Pre-clinical efficacy test using our independent database (Months 10-12) 4) Co-creation with key stakeholders to determine their requirements and market analysis (Months 1-12) Anticipated Impact and Dissemination Our proposed technology is ultimately anticipated to reduce neonatal mortality, improve patient outcome and experience, and to save costs for the NHS. Follow-up work will include further development and clinical evaluation of the technology, in collaboration with health economists, clinicians and commercial partners to support future implementation and dissemination of our technology.

Lay abstract: Background Globally, around 15 million babies are born preterm ( Healthcare need: The main clinical device currently available for routine uterine contraction (UC) monitoring is the tocodynamometer (TOCO) which tracks small abdominal shape changes during contractions. It is, however, uncomfortable for the mother, restricting their movement and giving only limited information for preterm birth prediction. Currently, there is no clinically acceptable and accurate prediction method in clinical use for preterm labour. State-of-the-art and challenges: The more comfortable technique called electrohysterograhy (EHG) offers an alternative for UC 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 clinicians unfamiliarity with the EHG signal features and unsatisfactory preterm prediction accuracy for clinical use from current research studies. Aims and work plan Our aim is to develop an innovative solution utilising the power of artificial intelligence (AI) to help achieve the quick acceptance of EHG as a reliable clinical monitoring tool and for accurate prediction of preterm labour. The data we will use is from existing datasets (one online database, one from our previous research). The project has four work packages: 1) our TOCO-like waveform generation method; 2) method for prediction of premature labour; 3) pre-clinical efficacy test; 4) co-creation with key stakeholders and market analysis. PPI PPI meetings will be held throughout the project. They will involve patients, public and other key stakeholders (e.g., mothers, midwives), to ensure user needs are understood, acceptability is maximised and to explore the clinical adoption pathway of the technology. These stakeholders will help shape the research, its procedures and outcome measures. Dissemination and anticipated outcomes The deliverables corresponding to each work package, including innovative algorithms, pre-clinical efficacy data, etc., will be achieved. To raise awareness of the potential clinical value of our technology, the findings of the project will be demonstrated to clinicians at the clinical units involved in this project. Following this project, we plan to work with clinicians, health economists and potential industrial partners, with suitable IP agreements in place, to develop the prototype, generate clinical efficacy data and advance our health economic evaluations. Our AI technology is ultimately anticipated to improve the health outcomes for parents and children and save costs for the NHS.

Research activity codes: 4.1 DISCOVERY AND PRECLINICAL TESTING OF MARKERS AND TECHNOLOGIES;7.3 MANAGEMENT AND DECISION MAKING
Health categories: REPRODUCTIVE HEALTH AND CHILDBIRTH