Available
Project number:
2025_59
Start date:
October 2025
Project themes:
Main supervisor:
Consultant Cardiologist / Senior Clinical Lecturer
Co-supervisor:
Professor James Teo
Additional Information:
AI-ECG and natural language processing for large scale Inherited Cardiac Conditions diagnosis and risk stratification
Background
Recent advancements in artificial intelligence (AI) applied to electrocardiograms (ECGs) have expanded the ability to diagnose cardiovascular conditions and clinical outcomes, and not just arrhythmias. The AI-ECG Risk Estimation (AIRE) deep learning model, can now detect heart failure and multimorbidity profiles, as well as being able to predict life-threatening arrhythmias, sudden death and all-cause mortality. Trained on over 3 million ECGs, AIRE has proven to be accurate, explainable, and actionable. Inherited cardiac conditions (ICCs), like hypertrophic cardiomyopathy and Brugada syndrome, pose a risk of fatal arrhythmias, making effective risk stratification essential. However, identifying key predictive patterns in ECG data remains challenging, even for specialists.
Novelty & Importance
This project will assess AIRE’s effectiveness in predicting outcomes such as ventricular arrhythmias, heart failure, and death in ICC patients using NHS data. We will enhance the analysis with data from electronic health records (EHRs) via the CogStack platform, which uses natural language processing (NLP) to extract relevant clinical insights from free-text records. By integrating AI-driven models into clinical practice, this project offers the potential for earlier and more precise diagnoses, personalised treatment plans, and improved patient outcomes.
Aims & Objectives
The primary aims of this project are to validate AIRE’s predictive accuracy in real-world clinical settings, implement AI-ECG models into NHS care pathways at leading hospitals, and collaborate with clinicians and patient groups to ensure equitable and patient-centered care. The project will also create tailored risk assessment tools that can be integrated into existing clinical decision-making frameworks.
We are now accepting applications for 1 October 2025
How to apply
Candidates should possess or be expected to achieve a 1st or upper 2nd class degree in a relevant subject including the biosciences, computer science, mathematics, statistics, data science, chemistry, physics, and be enthusiastic about combining their expertise with other disciplines in the field of healthcare.
Important information for International Students:
It is the responsibility of the student to apply for their Student Visa. Please note that the EPSRC DRIVE-Health studentship does not cover the visa application fees or the Immigration Health Surcharge (IHS) required for access to the National Health Service. The IHS is mandatory for anyone entering the UK on a Student Visa and is currently £776 per year for each year of study. Further detail can be found under the International Students tab below.
Next Steps
- Applications submitted by the closing date of Thursday 6 February 2025 will be considered by the CDT. We will contact shortlisted applicants with information about this part of the recruitment process.
- Candidates will be invited to attend an interview. Interviews are projected to take place in April 2025.
- Project selection will be through a panel interview chaired by either Professor Richard Dobson and Professor Vasa Curcin (CDT Directors) followed by informal discussion with prospective supervisors.
- If you have any questions related to the specific project you are applying for, please contact the main supervisor of the project directly.
For any other questions about the recruitment process, please email us at