Mr Michael Sharkey

by | 5 Oct 2023 | Academy fellows | 0 comments


Cohort: 7
PhD Start date: October 2023

I graduated from The University of Manchester in 2009 with a degree in MEng Mechanical Engineering and initially worked in industrial engineering.

In 2012, I joined the NHS Scientist Training Programme, specialising in Medical Physics at Sheffield Teaching Hospitals. I completed my training in 2015, earning an MSc with distinction in Medical Physics and Engineering from the University of Liverpool.

Since 2015, I’ve been working as a registered clinical scientist, mainly providing specialised imaging analysis for research trials and clinical practice. My focus has been on standardising and automating image analysis procedures in clinical settings to enhance quantitative measurements and provide improved visualisations for treatment planning and communication.

In 2020, I was seconded to a research position at the University of Sheffield, primarily focusing on AI algorithms for cardiovascular disease prediction from medical imaging with the aim to improving heart and lung assessments. From 2022 onwards, I’ve had a dual role between the NHS and the University, conducting research and translating advancements into clinical practice. Our team received recognition with the NHS Parliamentary Award for Innovation relating to our work on AI analysis of cardiac MRI.

I’m committed to advancing medical technology and look forward to making further contributions to the field throughout my 4Ward North PhD Fellowship.

PhD title

TRIAGE-PH Tertiary Referral using artificial intelligence (AI) for Pulmonary Hypertension

Brief summary of PhD project

Delayed diagnosis of Pulmonary Hypertension (PH) is a common problem, often leading to worse patient outcomes and increased healthcare costs. PH is a complex condition that requires specialised expertise for accurate diagnosis, resulting in delayed recognition. My PhD research aims to tackle this issue by developing an AI-based triage tool that uses non-invasive imaging biomarkers, clinical parameters, and patient demographics to predict the presence and severity of PH accurately. This tool will assist healthcare professionals in promptly assessing the likelihood of PH, allowing for timely patient stratification and referral. Ultimately, it seeks to facilitate earlier intervention, leading to improved patient outcomes and more efficient healthcare.


  • Dr Andy Swift (University of Sheffield)
  • Professor David Kiely (University of Sheffield)
  • Professor Alex Frangi (The University of Manchester)
  • Professor Wendy Tindale (University of Sheffield)

Key collaborators

  • Dr Lilian Meijboom (Amsterdam UMC)
  • Professor Anton Vonk Noordegraaf (Amsterdam UMC)

Specialty interest/techniques

  • Adaptable Multi-Modal AI Techniques: the development of AI algorithms capable of integrating data from various sources, like imaging and clinical measurements.
  • Real-World Algorithms: A critical aspect of my research revolves around the practicality of AI models in real-world clinical settings including the challenges of missing or erroneous data.

Career aspirations

This PhD fellowship will develop my imaging and artificial intelligence expertise. I aspire to be an academic clinical scientist and principal investigator in a research lab that facilitates interdisciplinary research and clinical translation. My aim is to develop clinically applicable tools that improve patient care.