The right ventricle's role in risk prediction following mitral valve replacement: a combined imaging-modelling study

Aim of the PhD Project:

To develop and improve a combined pipeline with AI-based image segmentation and shape analysis using multi-modal imaging, and computational biophysical modelling of the left and right ventricle
To apply this tool for extracting phenotypic biomarkers to enhance post-operative risk assessment in mitral regurgitation patients
Project Description:

Clinical background: In a rapidly ageing population, the prevalence of clinically significant valvular heart diseases is estimated to double by 2050, with mitral regurgitation (MR) one of the most frequent conditions. Whilst most research on MR focuses on the left ventricle (LV), right ventricular (RV) pathophysiological changes are common sequelae in these patients, with low cardiac output state secondary to RV failure a main cause of mortality following mitral valve surgery. Abrupt and complete MR eradication results in large increases in systolic wall stress in both ventricles, leading to contractile impairment. Unlike the LV, the RV is thin-walled and not designed to accommodate elevated wall stress and afterload, which result in shape remodelling. This is particularly relevant in older patients, who are likely to present pre-existing pulmonary hypertension and impaired RV mechanics that can precipitate remodelling. When present, this labels a higher-risk cohort of patients with only 30% of cases showing reversible morphological and functional changes following treatment. Therefore, understanding the effects of MR eradication on the RV holds significant clinical implications, as it potentially marks out a group of patients who demonstrate less benefit from left-sided heart valve treatment.

Project description: This project is framed in the AI-enabled decision support for diagnosis and prognosis, and will test the main hypothesis that tracking changes in RV shape and mechanics can enhance post-operative risk prediction in MR patients undergoing mitral valve replacement. Owing to its shape, the RV cannot be readily appreciated upon standard echocardiographic imaging, which is a widespread, cost-effective, and safe imaging modality. A range of AI-enabled technologies including deep learning segmentation methods, latent variable regression and subspace methods will be used to rapidly identify the shape changes and features that are a signature of elevated risk of RV failure from CT and echocardiographic imaging data. These AI identified biomarkers will be complemented with mechanistic biophysical models to propose a combined AI pipeline where morphological and functional metrics will be quantified to (a) improve preoperative planning, and (b) predict postoperative risks from both the inductive (image analysis) and deductive (biophysical models) AI reasoning, which are the two synergetic pillars of the digital twin paradigm [1].

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.