Predicting premature birth from MRI using deep learning

Aim of the PhD Project:

Deep Generative Modelling for prediction of gestation and weight at birth from fetal MRI and birth metrics
Emphasis on the use of model interpretation to improve understanding of elements involved in preterm birth and late fetal growth restriction
Link prediction algorithm and acquisition to inform future protocol developments and thus facilitate clinical transition.

Project Description:

Human pregnancy and birth are among the most fascinating processes in life but also carry significant risks for both mother and unborn child. Consequences from preterm birth and fetal growth restriction are severe and lifelong [1].

Human development occurs largely hidden: clinically available Ultrasound screening only catch glimpses into the womb. Advanced knowledge of the appropriate gestation when the birth should start, the size of the fetus, as well as the type of delivery most likely to occur, would allow for a step change in adequate preparation and monitoring of all but especially high-risk pregnancies.

Recent advances in fetal MRI techniques [2,3] have successfully been able to assess the fetal organs and placenta both structurally as well as functionally. These show altered T2* values in pregnancies with fetal growth restriction and reduced lung volume in pregnancies threatened by preterm labour. Dynamic information such as the frequency of fetal motion and lung breathing exercises is an indicator for preterm birth. Fetal MRI scans contain a vast amount of information and typically cover the uterus in multiple planes. However, only subsets of the data acquired are currently analysed, e.g. only the brain structure or placental attachment. This is in parts due to the lack of appropriate analysis tools able to benefit from the full extent of obtained information.

However, regardless of scan indication the questions of when the baby will be born and how much they will weigh is of high relevance for clinical planning of all births. A prediction for these questions would allow to optimize antenatal care and time point of delivery.

Recent deep learning (DL) [4] techniques provide two important opportunities: They allow to seek patterns from complex image data sets without making simplifying assumptions, and at the same time can be tuned to return features from the images which are salient to answering these questions. Thus, the goal of this project is to use generative Deep Learning to develop PLATYPUS (Prediction of Length of gestation, Approximate weight and TYpe of delivery from Pregnancy whole-Uterus MRI Scanning: a tool for asking fundamental questions about each pregnancy, mode and time of delivery. A significant emphasis will be to leverage recent developments in model interpretability to inform improved acquisition strategies which will continuously map development in the last weeks before birth.

The extensive database available from large scale fetal projects at King's provides the well characterized training data which is critical for deep learning.

Strains on the healthcare system in the UK create an acute need for finding more effective, efficient, safe, and accurate non-invasive imaging solutions for clinical decision-making, both in terms of diagnosis and prognosis, and to reduce unnecessary treatment procedures and associated costs. Medical imaging is currently undergoing a step-change facilitated through the advent of artificial intelligence (AI) techniques, in particular deep learning and statistical machine learning, the development of targeted molecular imaging probes and novel "push-button" imaging techniques. There is also the availability of low-cost imaging solutions, creating unique opportunities to improve sensitivity and specificity of treatment options leading to better patient outcome, improved clinical workflow and healthcare economics. However, a skills gap exists between these disciplines which this CDT is aiming to fill.

Consistent with our vision for the CDT in Smart Medical Imaging to train the next generation of medical imaging scientists, we will engage with the key beneficiaries of the CDT: (1) PhD students & their supervisors; (2) patient groups & their carers; (3) clinicians & healthcare providers; (4) healthcare industries; and (5) the general public. We have identified the following areas of impact resulting from the operation of the CDT.

- Academic Impact: The proposed multidisciplinary training and skills development are designed to lead to an appreciation of clinical translation of technology and generating pathways to impact in the healthcare system. Impact will be measured in terms of our students' generation of knowledge, such as their research outputs, conference presentations, awards, software, patents, as well as successful career destinations to a wide range of sectors; as well as newly stimulated academic collaborations, and the positive effect these will have on their supervisors, their career progression and added value to their research group, and the universities as a whole in attracting new academic talent at all career levels.

- Economic Impact: Our students will have high employability in a wide range of sectors thanks to their broad interdisciplinary training, transferable skills sets and exposure to industry, international labs, and the hospital environment. Healthcare providers (e.g. the NHS) will gain access to new technologies that are more precise and cost-efficient, reducing patient treatment and monitoring costs. Relevant healthcare industries (from major companies to SMEs) will benefit and ultimately profit from collaborative research with high emphasis on clinical translation and validation, and from a unique cohort of newly skilled and multidisciplinary researchers who value and understand the role of industry in developing and applying novel imaging technologies to the entire patient pathway.

- Societal Impact: Patients and their professional carers will be the ultimate beneficiaries of the new imaging technologies created by our students, and by the emerging cohort of graduated medical imaging scientists and engineers who will have a strong emphasis on patient healthcare. This will have significant societal impact in terms of health and quality of life. Clinicians will benefit from new technologies aimed at enabling more robust, accurate, and precise diagnoses, treatment and follow-up monitoring. The general public will benefit from learning about new, cutting-edge medical imaging technology, and new talent will be drawn into STEM(M) professions as a consequence, further filling the current skills gap between healthcare provision and engineering.

We have developed detailed pathways to impact activities, coordinated by a dedicated Impact & Engagement Manager, that include impact training provision, translational activities with clinicians and patient groups, industry cooperation and entrepreneurship training, international collaboration and networks, and engagement with the General Public.