Available
Project number:
2025_77
Start date:
October 2025
Project themes:
Main supervisor:
Clinician Scientist
Co-supervisor:
Professor Alistair Young
Additional Information:
HFpEF: AI imaging analysis of echocardiographic data to allow development of a fully integrated prediction model for heart failure with preserved ejection fraction (HFpEF) using both echo and clinical data
Background:
The BHF Centre of Research Excellence at King’s College London is currently at the cutting edge of data science approaches to Heart Failure with Preserved Ejection Fraction (HFpEF), leveraging artificial intelligence (AI) methods applied to data from the electronic health record (EHR) to generate both impactful research outputs (Wu etl al, EJHF 2023) and funding (BHF Cardiovascular Catalyst Grant, 2022). We will develop capability in AI analysis of echocardiographic images from patients with HFpEF to allow incorporation of unstructured imaging analysis data into our current models, resulting in a unique fully integrated HFpEF prediction model: iHFpEF
Novely & Importance
The novelty of our approach is best displayed though our work using natural language processing (NLP) via the Cogstack informatics platform to detect patients with undiagnosed HFpEF. This approach allowed us to define the problem and prognostic implications of underdiagnosis in HFpEF and led to our work developing an XGBoost prediction model to diagnose HFpEF from routinely collected EHR data (AIM-HFpEF model, submission pending). The next step and the purpose of this study is to include AI analysis of heart ultrasound images (echocardiograms).
Aims & objectives
Aim 1. To develop algorithms for automated image analysis of echocardiogram images, in particular the variables currently included in the AIM-HFpEF prediction model.
Aim 2. To integrate the results from aim 1 into AIM-HFpEF to result in a fully integrated and automated multi-modal disease prediction model embedded within the EHR
Aim 3. To perform unsupervised learning to identify novel echocardiographic predictors of a diagnosis of HFpEF.
Key Reference: Wu J, Biswas D… O’Gallagher K. Artificial intelligence methods for improved detection of undiagnosed heart failure with preserved ejection fraction. Eur J Heart Fail. 2024;26(2):302–310. doi: 10.1002/ejhf.3115
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