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Completed STUDENTSHIP UKRI Gateway to Research

Quantum Inspired Imaging for Disease Diagnosis


Funder Engineering and Physical Sciences Research Council
Recipient Organization University of Glasgow
Country United Kingdom
Start Date Aug 31, 2021
End Date Mar 02, 2025
Duration 1,279 days
Number of Grantees 2
Roles Student; Supervisor
Data Source UKRI Gateway to Research
Grant ID 2588497
Grant Description

This project proposes to explore possible applications of quantum inspired imaging and artificial intelligence (AI) in the context of non-invasive imaging to monitor the health of individuals in the home. Small changes to behaviours or physiology, such as small changes in the rate of breathing, heart rate and blood flow and/ or distribution can precede the

development of more serious symptoms associated with the onset of disease. 1 The aim of this project is to develop technologies that combine quantum inspired sensors and AI2 to detect small but significant changes in a person's behaviour. This has the potential to allow for more timely intervention for disease as regards testing and

treatment. 3 An example of where this technology could be utilised is providing early warning of disease outbreaks in care homes, such as the annual flu, a context in which early detection of diseases is grave importance. A network of homes with this technology has the potential to give insight into the

progression and spread of infectious diseases, which has implications for quarantine of the infection and stopping the spread of the disease. At the heart of these technologies lies an issue of the privacy and security of the data collected, which are major concerns for the implementation of the benefits provided.

4 This adds a new element to the technology in terms of ethical considerations and work in their development will involve collaboration with ethics boards. This project will address the following objectives over the duration of the studies: 1. Establishment of a model for the treatment of the sensors signals into an AI platform

to identify features of interest . 2. Validation of the model on sensors' data from controlled environment 3. Optimisation and validation in real use-case.

All Grantees

University of Glasgow

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