Deep 3D Morphable Models of Syndromic Craniofacial Patients

1) Brief description of the context of the research including potential impact

Over 7,000 genetic syndromes have been identified in humans, with 30-40% of these presenting in dysmorphic craniofacial features affecting the face shape. These syndromes are one of the most common birth defects and often result in several development problems.
Craniofacial syndromes can have an impact on functions such as breathing, vision, swallowing, speech, hearing and cognitive development, so early detection of these genetic conditions is beneficial to allow for treatment as soon as possible and avoid vital function deterioration. However, at a mean ago of 6 years, 50% of patients affected by craniofacial syndromes are currently undiagnosed. Having an early and accurate diagnosis can improve clinical decisions and allow access to more targeted prognosis, resources and treatments.
Genetic testing is the gold standard to diagnose inherited syndromes, but for some genetic diseases, many different mutations can occur in the same gene and result in the disease, making DNA testing challenging. It remains expensive, and, therefore, inaccessible for lowmiddle-income countries. Thus, creating models that can support detection of some genetic syndromes from easily affordable and accessible techniques may be beneficial. Prenatal ultrasound (US) is routinely performed in pregnant women and, although some craniofacial syndrome facial features can be subtle and challenging to detect, imaging acquired during fetal scans could be used to build a model for predictions of craniofacial syndromes. 3D fetal face US imaging could be used for early stage diagnosis and, therefore, parents' counselling and clinical planning for delivery and care immediate after birth.

2) Aims and Objectives

-The specific objectives are to:
- Develop machine learning model that will generate prediction of the development with age of craniofacial syndromes.
- Use this model to determine what specific syndrome the patient has, how sever it will develop and in what regions of the face/skull.
- These approaches will assist in early advanced diagnosis, understanding the syndromes severity and allowing for the correct treatment as soon as possible to avoid vital function deterioration.

3) Novelty of Research Methodology

Create a machine learning model that will be able to diagnose craniofacial syndromes from fetal images and show how it will progress over time from pre-natal through to all post-natal ages. Previous work has been done mostly only using post-natal data and for the use of diagnosis and this research will be the first of its kind to generate the disease progression model from pre-natal fetal images.

4) Alignment to EPSRC's strategies and research areas

This research project aligns with the EPSRC's strategy for supporting the area of Medical Imaging under the theme of Engineering and also Healthcare Technologies. The priority area this research fits is Novel Imaging Technologies. This is research that is focused on the development of next-generation imaging technologies for diagnostic, monitoring and therapeutic applications, with improved accuracy, affordability and incorporating new modalities.

5) Any companies or collaborators involved

In collaboration with Great Ormond Street Hospital.

The critical mass of scientists and engineers that i4health will produce will ensure the UK's continued standing as a world-leader in medical imaging and healthcare technology research. In addition to continued academic excellence, they will further support a future culture of industry and entrepreneurship in healthcare technologies driven by highly trained engineers with deep understanding of the key factors involved in delivering effective translatable and marketable technology. They will achieve this through high quality engineering and imaging science, a broad view of other relevant technological areas, the ability to pinpoint clinical gaps and needs, consideration of clinical user requirements, and patient considerations. Our graduates will provide the drive, determination and enthusiasm to build future UK industry in this vital area via start-ups and spin-outs adding to the burgeoning community of healthcare-related SMEs in London and the rest of the UK. The training in entrepreneurship, coupled with the vibrant environment we are developing for this topic via unique linkage of Engineering and Medicine at UCL, is specifically designed to foster such outcomes. These same innovative leaders will bolster the UK's presence in medical multinationals - pharmaceutical companies, scanner manufacturers, etc. - and ensure the UK's competitiveness as a location for future R&D and medical engineering. They will also provide an invaluable source of expertise for the future NHS and other healthcare-delivery services enabling rapid translation and uptake of the latest imaging and healthcare technologies at the clinical front line. The ultimate impact will be on people and patients, both in the UK and internationally, who will benefit from the increased knowledge of health and disease, as well as better treatment and healthcare management provided by the future technologies our trainees will produce.

In addition to impact in healthcare research, development, and capability, the CDT will have major impact on the students we will attract and train. We will provide our talented cohorts of students with the skills required to lead academic research in this area, to lead industrial development and to make a significant impact as advocates of the science and engineering of their discipline. The i4health CDT's combination of the highest academic standards of research with excellent in-depth training in core skills will mean that our cohorts of students will be in great demand placing them in a powerful position to sculpt their own careers, have major impact within our discipline, while influencing the international mindset and direction. Strong evidence demonstrates this in our existing cohorts of students through high levels of conference podium talks in the most prestigious venues in our field, conference prizes, high impact publications in both engineering, clinical, and general science journals, as well as post-PhD fellowships and career progression. The content and training innovations we propose in i4health will ensure this continues and expands over the next decade.