Congenital heart defect (CHD) is a significant, rapidly emerging global problem in child health and a leading cause of neonatal and childhood death. Prenatal detection of CHDs with the help of ultrasound allows better perinatal management of such pregnancies, leading to reduced neonatal mortality, morbidity and developmental complications. However, there is a wide variation in reported fetal heart problem detection rates from 34% to 85%, with some low- and middle-income countries detecting as low as 9.3% of cases before birth. Research has shown that deep learning-based or more general artificial intelligence (AI) models can support the detection of fetal CHDs more rapidly than humans performing ultrasound scan. Progress in this AI-based research depends on the availability of large, well-curated and diverse data of ultrasound images and videos of normal and abnormal fetal hearts. Currently, CHD detection based on AI models is not accurate enough for practical clinical use, in part due to the lack of ultrasound data available for machine learning as CHDs are rare and heterogeneous, the retrospective nature of published studies, the lack of multicentre and multidisciplinary collaboration, and utilisation of mostly standard planes still images of the fetal heart for AI models. Our aim is to develop AI models that could support clinicians in detecting fetal CHDs in real time, particularly in nonspecialist or low-resource settings where fetal echocardiography expertise is not readily available.

We have designed the Clinical Artificial Intelligence Fetal Echocardiography (CAIFE) study as an international multicentre multidisciplinary collaboration led by a clinical and an engineering team at the University of Oxford. This study involves five multicountry hospital sites for data collection (Oxford, UK (n=1), London, UK (n=3) and Southport, Australia (n=1)). We plan to curate 14 000 retrospective ultrasound scans of fetuses with normal hearts (n=13 000) and fetuses with CHDs (n=1000), as well as 2400 prospective ultrasound cardiac scans, including the proposed research-specific CAIFE 10 s video sweeps, from fetuses with normal hearts (n=2000) and fetuses diagnosed with major CHDs (n=400). This gives a total of 16 400 retrospective and prospective ultrasound scans from the participating hospital sites. We will build, train and validate computational models capable of differentiating between normal fetal hearts and those diagnosed with CHDs and recognise specific types of CHDs. Data will be analysed using statistical metrics, namely, sensitivity, specificity and accuracy, which include calculating positive and negative predictive values for each outcome, compared with manual assessment.

We will disseminate the findings through regional, national and international conferences and through peer-reviewed journals. The study was approved by the Health Research Authority, Care Research Wales and the Research Ethics Committee (Ref: 23/EM/0023; IRAS Project ID: 317510) on 8 March 2023. All collaborating hospitals have obtained the local trust research and development approvals.

While individuals living in rural areas often have poorer health outcomes and reduced access to healthcare services compared with those in urban areas, there is a disproportionate gap in research examining rural health issues and identifying solutions to healthcare challenges. This is likely due to the numerous barriers to conducting rural health research, including the centralisation of research in urban areas and limited trained personnel and resources to conduct research in rural communities. This realist review aims to identify articles focused on building rural health research capacity and develop an evidence-based framework to be used by researchers, clinicians and policymakers to improve rural health services and well-being for rural populations.

We will conduct a realist review using the following steps: (1) develop a search strategy, (2) conduct article screening and study selection, (3) perform data extraction, quality appraisal and synthesis, (4) engage stakeholders for feedback on our findings and (5) report our findings and engage in knowledge translation. Search terms include variations of the terms ‘research’, ‘capacity building’ and ‘rural’. Databases include (since inception) Ovid MEDLINE, Embase, CINAHL Plus, APA PsycINFO, ERIC and Scopus. A separate search of the same databases was also designed to identify relevant theories or frameworks related to research capacity building, using variations of the terms ‘research’, "‘capacity building’, ‘theory’ and ‘framework’. Studies will be screened by title and abstract and full text by two research team members and included based on their relevance to rural health research capacity building. We will exclude articles not published in English. We will also search the grey literature to identify rural health research centres, networks or training programmes that have not been described in the academic literature. Two research team members will extract relevant data from included studies and perform a qualitative analysis based on guidelines for realist reviews.

This review does not require ethical approval as it draws on secondary data that is publicly available. The findings will be disseminated at academic conferences, published in peer-reviewed journals and summarised in a lay report for individuals interested in developing strategies, programmes or policies to improve rural health research. The results will inform individuals developing rural health research training programmes, establishing rural research centres, or others interested in building rural health research capacity.

CRD42023444072.