AI-Enabled Assessment of Cardiac Function from Echocardiography

Aim of the PhD Project:

Develop deep learning techniques for automated interpretation of echocardiography images and train/evaluate them using large-scale datasets.
Automated characterisation of cardiac function from echocardiography.
Automated assessment of valvular heart condition and function.
Machine learning based quality control of model inputs and outputs

Project Description:
Echocardiography is the first port-of-call for assessment and diagnosis of cardiovascular disease (CVD). However, for accurate and robust quantification of many clinical biomarkers cardiac magnetic resonance (CMR) imaging is required. Traditionally, estimating such biomarkers from CMR has required a significant amount of expert interaction, e.g. for contouring the boundaries of the left ventricular myocardium over the cardiac cycle. We have recently published a study that demonstrated that much of this interaction can be avoided using the latest deep learning techniques [1].

Nevertheless, it remains the case that echocardiography is cheaper and more widely available than CMR. In this project we aim to translate, adapt and extend the techniques we have developed for CMR into the realm of echocardiography. This is the right time for such a project - deep learning techniques normally require a large amount of data for training and validation, and recently a number of large-scale databases for echocardiography have become available [2,3]. Furthermore, we have access to thousands of echocardiography scans from the KCL/GSTT Biobank. We believe that our expertise in developing automated machine learning based pipelines for biomarker estimation, and the availability of such datasets, creates an exciting opportunity for high-impact translational research.

Previous work on using deep learning for echocardiography analysis has mainly focused on automated segmentation of the left ventricular (LV) endocardial boundary [4-6] for estimation of ejection fraction (EF). However, boundary identification is prone to errors due to low image quality, the presence of artefacts, and unusual image features linked to different pathologies. As a result, these algorithms can lack robustness. To overcome this limitation, some works have focused on the direct estimation of EF without endocardial border segmentation [7-8]. Although this solution could be more reliable, it is less interpretable and more difficult for clinicians to assess the accuracy of the results.

We propose to develop machine and deep learning-based techniques for automated quantification of echocardiography scans. We will investigate the use of transfer learning from our CMR-based models, and design domain adaption techniques to take advantage of our established knowledge in this new task. Furthermore, we will seek to go beyond the estimation of simple metrics such as end-diastolic and end-systolic volumes and EF, to paint a much richer picture of the heart in health and disease.

In addition, echocardiography remains the first line technique for assessing valvular heart disease and regurgitation due to its excellent visualisation of the valve leaflets, which are not visible in CMR. Part of this project will focus on the automated assessment of the anatomy and function of the different heart valves. With appropriate quality control tools and confidence measures, the techniques could, in principle, work in milliseconds and give the sonographer real-time feedback whilst scanning. The subsequent analysis of the estimated biomarkers at scale could enable interesting and valuable research into the nature of CVD and the progression of the heart into disease.

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.