Study level

  • PhD
  • Master of Philosophy
  • Honours

Faculty/School

Faculty of Science

School of Computer Science

Topic status

We're looking for students to study this topic.

Research centre

Centre for Data Science

Supervisors

Professor Yuefeng Li
Position
Professor
Division / Faculty
Faculty of Science

Overview

Cardiovascular disease (CVD) is the leading cause of morbidity and mortality globally. According to the World Health Organization (WHO), it is estimated CVD takes 17.9 million lives every year. In Australian, the statistical data from the Australia Heart Foundation shows CVD is a major cause of death in Australia. It occupies 26% of all deaths, responsible for an average 118 deaths every day. Four of the main types of CVD are coronary heart disease, strokes and transient ischaemic attack, peripheral arterial disease, and aortic disease. Identifying some risk factors early and with early treatment and changing lifestyle can save a lot of lives.

In recent years, studies and research have shown that the origins of CVD may be traced to vascular and metabolic processes in early life. The effects of early life conditions and diseases that may influence the development of CVD in later life have been studied in several longitudinal studies (Barker et al., 2009, Harding 2001). More and more evidence has emerged to support the view that structural and functional changes in the retinal microvasculature are associated with CVD risk factors in early life.

Fundus imaging technology is a common approach to capture the retinal blood vessels and can present 2D or 3D visualizations. It is the base of the current techniques to study and assess microvasculature through the retina. It is well known that the structure of retinal vessels and its changes are showing important relationships with eye diseases. It has been found that retinal structure changes are also associated with hypertension, stroke and other cardiovascular diseases. Precise measurements of retinal microvasculature and artery/ vein parameters are the crucial step to achieve finding the relationship of the parameters with the diseases.

Research activities

  • The project will discuss or contribute leading techniques for IT Industry to build AI-based data analysis systems on Multiple Imaging Modalities
  • QUT research group of AI-based Data Analysis will foster the development of students through the transition of knowledge from supervisors and industry partners
  • for VRES or honours students, you will obtain research experience in the ares of machine learning and data mining and their applications in the real world
  • for HDR students, you will learn how to develop world-leading research capabilities or skills for your career.

Outcomes

  • For VRES or honours students, the aim of the project is to develop survey reports or a conceptual model to understand the current research trends or issues for AI-based data analysis systems
  • for HDR students, the aim of the project is to develop new models or algorithms to solve the research gaps in one of the fields.

Skills and experience

  • You are expected to have solid background in computer science
  • you have python or java programming experience
  • GPA > = 5.5

Keywords

Contact

Professor Yuefeng Li

Academic Lead HDR, School of Computer Science
Faculty of Science | Queensland University of Technology (QUT)
S Block, Level 10, Room 1024, Gardens Point Campus
ph +61 7 3138 5212 | email y2.li@qut.edu.au