Raman Group: Intergrating Multimodal Imaging, Genomics, and Clinical Data for Risk Stratification and Precision Therapy in Cardiomyopathies through Novel Artificial Intelligence Approaches

About the Research 

Our lab focuses on applying cutting-edge cardiac imaging and data science to understand the pathophysiology, progression, and clinical consequences of inherited and acquired cardiomyopathies. By combining advanced cardiovascular magnetic resonance (CMR), Echocardiogram (echo), computed tomography (CT), electrocardiography (ECG), and biochemical data, we aim to improve risk prediction, refine phenotyping, and accelerate the development of precision medicine approaches in heart muscle disease. This MSc or DPhil project will contribute to our overarching goal of transforming risk stratification and management in cardiomyopathy using artificial intelligence (AI). Inherited cardiomyopathies such as hypertrophic cardiomyopathy (HCM) are clinically and genetically heterogeneous, making outcome prediction and treatment selection challenging. Traditional risk models rely on a limited number of clinical and imaging parameters and often fail to capture the full complexity of disease biology. Our ambition is to integrate high-dimensional, multimodal data from large-scale registries, such as the Hypertrophic Cardiomyopathy Registry (HCMR), other DCM cohorts and UK Biobank (UKB), to train machine learning models that better predict outcomes like sudden cardiac death (SCD), heart failure progression, stroke and arrhythmias. The project will focus on integrating multimodal data, including ECGs, CMR-derived imaging and metabolic phenotypes, CT features, and clinical/demographic variables. Students will explore supervised and unsupervised machine learning techniques to identify new disease subtypes, predict clinical events, and potentially derive imaging-based biomarkers that could inform therapy selection. A key aspect will involve evaluating model performance and generalisability across diverse patient populations. Working closely with our multidisciplinary team of cardiologists, imaging scientists, and data scientists, the student will gain experience in: a)Curating and pre-processing multimodal data from large patient registries; b) Feature engineering across imaging, ECG, and clinical datasets; c) Applying and validating machine learning algorithms (random forests, neural networks, GAN, diffusion models, survival models); d) Performing model interpretability and explainability analyses; e) Collaborating in a translational research environment aimed at clinical implementation We envision this work contributing to the creation of AI-assisted clinical decision tools that integrate multi-source data to deliver individualised risk predictions and therapeutic recommendations. In the longer term, such approaches could help reduce overtreatment, optimise the use of emerging therapies (e.g. myosin inhibitors), and ultimately improve patient outcomes in cardiomyopathy.

This project is ideally suited to exceptional students with a background in biomedical sciences, engineering, computer science, or data science, with an interest in translational cardiovascular research. Prior experience with Python, MATLAB, or R will be essential. Enthusiasm for working at the interface of cardiology, medical imaging, and machine learning is welcomed.

Candidates are encouraged to reach out to PI Betty.raman@cardiov.ox.ac.uk with a copy of their CV to discuss project details.

This MSc by Research project may be suitable for part-time research. 

Training Opportunities

1. Multimodal Data Integration – Combining imaging (CMR, CT), ECG, and clinical datasets for unified analysis
2. Advance CMR and CT feature extraction – Learning about the role of advanced CMR and CT features in diagnosis and risk prediction of inherited heat disease.
3. Machine Learning Application – Building and validating models for risk prediction and patient stratification, as well as applying novel AI techniques directly to the images for identifying novel disease specific signatures. 
4. Feature Engineering – Extracting and selecting relevant features from cardiac imaging and ECG signal
5. Survival and Prognostic Modelling – Using time-to-event analysis to predict clinical outcomes
6. Programming in Python or R – For statistical computing, data visualisation, and model development
7. Interpretation of Cardiac Imaging Biomarkers – Understanding key parameters like fibrosis, wall thickness, and strain
8. Scientific Communication – Writing abstracts, posters, and contributing to manuscripts
9. Data Governance and Ethics – Working with patient-level health data in compliance with regulatory standards
10. Cross-Disciplinary Collaboration – Engaging with clinicians, imaging scientists, and data analysts
11. Clinical Insight into Cardiomyopathies – Gaining understanding of genotype–phenotype relationships and therapeutic strategies

Students are encouraged to attend the MRC Weatherall Institute of Molecular Medicine DPhil Course, which takes place in the autumn of their first year. Running over several days, this course helps students to develop basic research and presentation skills, as well as introducing them to a wide range of scientific techniques and principles, ensuring that students have the opportunity to build a broad-based understanding of differing research methodologies.

Generic skills training is offered through the Medical Sciences Division's Skills Training Programme. This programme offers a comprehensive range of courses covering many important areas of researcher development: knowledge and intellectual abilities, personal effectiveness, research governance and organisation, and engagement, influence, and impact. Students are actively encouraged to take advantage of the training opportunities available to them.

As well as the specific training detailed above, students will have access to a wide range of seminars and training opportunities through the many research institutes and centres based in Oxford.

The Department has a successful mentoring scheme, open to graduate students, which provides an additional possible channel for personal and professional development outside the regular supervisory framework. We hold an Athena SWAN Silver Award in recognition of our efforts to build a happy and rewarding environment where all staff and students are supported to achieve their full potential.

Additional Supervisors

1. Qiang Zhang

2. Stefan Neubauer 

3. Hugh Watkins

Publications