Title: Artificial Intelligence to Improve Antenatal Detection of Congenital Heart Disease
Funder: Department of Health and Social Care

Abstract: Background: Congenital heart disease (CHD) is the most common group of fetal malformations, occurring in roughly 1% of pregnancies. Cardiac views are part of the screening ultrasound offered to all pregnant women in the UK, but currently only around 54% of cases of severe CHD are diagnosed before birth. This is a problem because antenatal detection has been shown to result in reduced mortality in children with CHD, and reduced rates of long-term morbidity such as neurodisability.Machine learning (ML) is a form of artificial intelligence that has been shown to achieve human expert-level performance in some medical classification tasks. Our group has developed ML tools that can automatically detect and store standard image planes from ultrasound data, and automatically measure biometry. Our early work has shown promising results in using ML to automatically detect abnormal fetal hearts. I will build on this early work to explore AI-based methods to automatically identify fetuses that should be referred to specialist fetal cardiology services, and thus improve CHD detection rates. Research question: How can deep learning algorithms be developed and utilised in the setting of fetal anomaly screening to improve antenatal detection rates of congenital heart disease? Aims: To refine existing and develop new machine learning algorithms to automatically identify which fetuses need referral to specialist fetal cardiac services To show the clinical utility of the developed AI-based tools in a real-world setting, and demonstrate the potential of these tools to cause a paradigm shift in fetal anomaly screening worldwideDesign and study methods: The proposed project is in two phases. Phase 1: I will use existing retrospective imaging data to develop and test new algorithms to detect CHD. I will explore different potential routes to this, including direct abnormality detection, use of cardiac biometry to identify markers of CHD, and combination of different models in an ensemble learning approach. The models will be combined into a clinically usable tool that will be tested in phase 2. Phase 2: I will recruit 87 pregnant women, of which 29 will be carrying a fetus with CHD, and 58 volunteer sonographers, blinded to the CHD status of the fetuses. A full anomaly ultrasound scan will be performed between 18+0 and 23+6 weeks' gestation. Sonographers will be randomised to scanning either in the standard fashion or using the ML tool. Each woman will be scanned using both techniques. I will compare the detection rate of CHD between the two groups, the time taken for each scan, and the sonographer's subjective experience. Anticipated impact of the project My goal is to develop a clinically useful tool that will increase the antenatal detection rate of CHD. If the proposed system can demonstrate this, then it is likely to translate into an improved postnatal condition of affected infants. In turn this is likely to reduce postnatal mortality, and the incidence of neurological injury, which may reduce some of the long-term morbidity associated with CHD.

Lay abstract: Aim: This project aims to investigate whether a sophisticated type of computer program known as Artificial Intelligence (AI) can help us identify heart conditions in babies, before birth. Background: When a baby has a heart problem before birth, this is known as congenital heart disease (CHD). CHD is the most common health issue affecting unborn babies, and is present in around 1 in 100 pregnancies. All pregnant women in the UK are offered an ultrasound scan of their baby at roughly half-way through their pregnancy, to try and identify certain health issues including CHD. These scans are performed because we know that babies are more likely to survive if we identify CHD before birth, compared to after birth. They are also less likely to have damage to other organs, such as the brain. This is because we can offer the best treatment immediately at birth if we identify the problem early. Unfortunately, in the UK only around half of babies with CHD are diagnosed before birth. This is because the ultrasound scans are difficult to perform, and there is a shortage of people able to do these scans. Some areas of the UK have much lower rates of detection of CHD before birth than others. We want to find out if AI can help the people doing these scans, by assisting them and telling them when the baby has CHD. Our research group has already used computers to help recognise what part of the baby is being looked at in a scan, and to automatically measure the baby's size. We would like to develop these computer systems to also tell us which babies have heart conditions. Design and methods: We plan to divide this study into two phases.: We will use existing ultrasound scans to teach the AI program how to recognise differences between hearts with CHD and healthy hearts. We will then see how well these programs work by testing them with existing scan pictures where we already know the diagnosis. We will then test how these new systems work in 'real life' by scanning pregnant women volunteers. Some of these women will be carrying a baby with CHD, and some will be carrying a baby without CHD. The people doing the scan will not know which babies have CHD beforehand. We will compare our new programs with a normal type of scan, and we will see how well our new programmes can help identify CHD.Patient and public involvement: As part of this project, a focus group will be formed that will include a wide range of people who have an interest in this area, including pregnant women, parents of children with CHD, people who perform ultrasound scans, and adults living with CHD. This group will meet annually to discuss the project, guide the future direction, and ensure the research priorities are in keeping with their own. In addition, a public/patient representative will be recruited who will become part of the formal research steering group, meeting every six months to oversee the project. Dissemination: The scientific findings will be shared with the medical community through journal publications and conference presentations. The focus group discussion will be attended by a focus group evaluator and a graphic artist. They will produce both a written report and a graphical representation of the group's discussions and outputs, which can be shared with the wider community through both traditional and social media, and a project website. We will continue the recent strong public engagement work of the department with the help of our in-house engagement and communication teams.

Research activity codes: 4.2 EVALUATION OF MARKERS AND TECHNOLOGIES;8.4 RESEARCH DESIGN AND METHODOLOGIES (HEALTH SERVICES)
Health categories: CARDIOVASCULAR