Remote Monitoring in Urology Using AI

Project overview:

The growth of AI in the healthcare sector is delivering real world change and promises to transform the future of medicine. There is a critical need to increase efficiency in healthcare through AI due to an aging population, limited human resources and rising costs. Urinary tract infections lead to 150,000 hospitalisations in the UK each year costing the NHS an estimated £380 million annually.

The project introduces a novel approach to managing common urological scenarios by integrating remote monitoring systems with AI and machine learning to predict clinical outcomes. The innovative aspects of this research lie in its use of AI-driven predictive models that analyse real-time data from IoT-enabled devices to forecast patient outcomes, such as the need for catheter reinsertion or the risk of sepsis. This proactive approach enables timely interventions, significantly reducing emergency hospital admissions and improving patient care in community settings.

The PhD researcher will develop a comprehensive remote monitoring system, capable of continuous tracking of clinical variables like urination frequency, clinical measurements and bladder volume. The development of AI formulated algorithms to predict clinical outcomes at an early stage represents a major advancement in patient management.

The supervisory team will comprise Prof William Holderbaum at the University of Reading and Christopher Blick, who is a consultant at the Royal Berkshire Hospital.

PhD Objectives:

·        Data Acquisition and Analysis: To collect and analyse data to predict outcomes for patients with urological conditions,.

·        Development of Remote Monitoring Devices: To design and implement remote monitoring systems that continuously track relevant clinical variables, such as urination frequency etc.

·        Integration of AI and Machine Learning: To develop and integrate AI algorithms, including machine learning and generative AI models, that can predict patient outcomes based on real-time data, providing personalized care recommendations.

·        Implementation of a Robust Monitoring System: To create a comprehensive remote monitoring system that supports patients, community care teams.

This project offers the student a comprehensive training experience that spans both technical and interdisciplinary domains. Training and Experimental Work: The student will gain hands-on experience in cutting-edge AI and machine learning techniques, with a focus on developing and applying predictive models to real-time data from IoT-enabled devices. This experience will provide expertise that will be transferable across a broad range of health tech and healthcare settings. The student will be involved in full in the project, from data acquisition and analysis to the development and implementation of AI-driven remote monitoring systems. Additionally, the student will have the opportunity to work closely with clinical data, enhancing their understanding of medical informatics and healthcare analytics.

School of Biological Sciences, University of Reading:

The University of Reading, located west of London, England, provides world-class research education programs. The University’s main Whiteknights Campus is set in 130 hectares of beautiful parkland, a 30-minute train ride to central London and 40 minutes from London Heathrow airport. 

Our School of Biological Sciences conducts high-impact research, tackling current global challenges faced by society and the planet. Our research ranges from understanding and improving human health and combating disease, through to understanding evolutionary processes and uncovering new ways to protect the natural world. In 2020, we moved into a stunning new ~£60 million Health & Life Sciences building. This state-of-the-art facility is purpose-built for science research and teaching. It houses the Cole Museum of Zoology, a café and social spaces.

In the School of Biological Sciences, you will be joining a vibrant community of ~180 PhD students representing ~40 nationalities. Our students publish in high-impact journals, present at international conferences, and organise a range of exciting outreach and public engagement activities.

During your PhD at the University of Reading, you will expand your research knowledge and skills, receiving supervision in one-to-one and small group sessions. You will have access to cutting-edge technology and learn the latest research techniques. We also provide dedicated training in important transferable skills that will support your career aspirations. If English is not your first language, the University’s excellent International Study and Language Institute will help you develop your academic English skills.

The University of Reading is a welcoming community for people of all faiths and cultures. We are committed to a healthy work-life balance and will work to ensure that you are supported personally and academically.

Eligibility:

Applicants should have a good degree (minimum of a UK Upper Second (2:1) undergraduate degree or equivalent) in Computing and Electronics and knowledge of Mathematical Modelling and Statistical Analysis or a strongly-related discipline. With a commitment to improving diversity in science and engineering, we encourage applications from underrepresented groups.

How to apply:

·        Apply for a PhD in Biomedical Engineering https://bit.ly/ReadingPhDApply

·        Please quote the reference (DRC24-138) in the ‘Scholarships applied for’ box.

·        Please upload a CV and Cover Letter with your application. If the application system prompts you to submit a research proposal, paste in the project title and move on to the next step.

Further information:

http://www.reading.ac.uk/biologicalsciences/SchoolofBiologicalSciences/PhD/sbs-phd.aspx

Enquiries:

Professor William Holderbaum, email: [email protected]

Dr Christopher Blick email: [email protected]