The Machines and the Heart: Intelligent Heart Disease Prediction by Artificial Intelligence and Big Data

Description

This is a funded PhD position in applied medical imaging and deep learning to heart diseases, suited to candidates with an applied mathematics, computer science, electrical engineering, medical imaging, biomedical engineering, physics or equivalent MSc/BSc degree.

The overarching aim of this project is to develop new prediction tools of heart disease using big data by integrating machine learning and statistical learning approaches. Cardiovascular diseases (CVDs) are diseases of the heart and blood vessels. According to the World Health Organisation (WHO), 17.9 million people die each year from CVDs, an estimated 31% of all deaths worldwide. Large amounts of data in various forms, such as Electrocardiogram (ECG), imaging, clinical tests and patient characteristics, are routinely generated in clinical research and practice. The successful candidate will develop novel deep-learning image analysis solutions to support the prediction of CVDs based on an unprecedented large image dataset in Liverpool.

The successful PhD candidate will benefit from working with a multidisciplinary team in which there exists extensive experience in the areas of computer science, image processing, high performance computing, mathematics, and medicine. All postgraduate students undertake the PGR Development Programme which aims to enhance their skills for a successful research experience and career. They are required to maintain an online record of their progress and record their personal and professional development throughout their research degree. The 1st Year Development Workshops encourage inter- and cross-disciplinary thinking and identify and develop the knowledge, skills, behaviours and personal qualities that all students require. In the 2nd year all students take part in a Poster Day to provide an opportunity to present their research to a degree educated general public, and in the 3rd year students complete a career development module. Other online training, such as ‘Managing your supervisor’ and ‘Thesis writing’ is provided centrally.

The Institute of Life Course and Medical Science is fully committed to promoting gender equality in all activities. In recruitment we emphasize the supportive nature of the working environment and the flexible family support that the University provides. The Institute holds a silver Athena SWAN award in recognition of on-going commitment to ensuring that the Athena SWAN principles are embedded in its activities and strategic initiatives. The Institute of Life Course and Medical Science is fully committed to promoting gender equality in all activities. In recruitment we emphasize the supportive nature of the working environment and the flexible family support that the University provides. The Institute holds a silver Athena SWAN award in recognition of on-going commitment to ensuring that the Athena SWAN principles are embedded in its activities and strategic initiatives.

Informal enquiries regarding this project should be made to Dr Yalin Zheng () and Prof Gregory Lip (). All general enquiries should be directed to Mrs Eleanor Toole ()

To apply please send your CV and a covering letter to Dr Zheng () with a copy to 

Availability

Open to students worldwide

Funding information

Funded studentship

Stipend (approx): £15,000 per annum tax-free, full UK home tuition fees and research bench fees paid. Exact amount TBC

Supervisors

References

1. Goodfellow I, Bengio Y, Courville A. Deep learning: MIT Press; 2016.
2. Shen D, Wu G, Suk H-I. Deep Learning in Medical Image Analysis. Annual Review of Biomedical Engineering. 2017;19(1):221-48.
3. Mou L, Zhao Y, Fu H, Liu Y, Cheng J, Zheng Y, et al. CS2-Net: Deep learning segmentation of curvilinear structures in medical imaging. Medical Image Analysis. 2020;in press. 10.1016/j.media.2020.101874.
4. Bridge J, Harding SP, Zheng Y. Development and validation of a novel prognostic model for predicting AMD progression using longitudinal fundus images. BMJ Open Ophthalmology. 2020;5:e000569.
5. Chen X, Williams B, Vallabhaneni S, Czanner G, Williams R, Zheng Y. Learning active contour models for medical image segmentation. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Long Beach, CA: IEEE; 2019. p. 11632-40