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Evaluation of Handheld Optical Coherence Tomography to assess the presence of retinal disease in a longitudinal population-based study: NICOLA (Northern Ireland Cohort for the Longitudinal Study of Aging

School of Medicine, Dentistry and Biomedical Sciences | PHD

Applications are now CLOSED
Funding
Funded
Reference Number
SMED-2241-1025
Application Deadline
29 February 2024
Start Date
1 October 2024

Overview

PhD studentship available to explore "Evaluation of Handheld Optical Coherence Tomography in NICOLA Study." Explore the feasibility of using recently developed Handheld OCT as an epidemiological tool for retinal disease diagnosis. Contribute to comparing devices, exploring diagnostic accuracy and developing image co-alignment methods. Includes 3-month industry placement. Exciting opportunity at the intersection of technology, industry and healthcare.

Optical coherence tomography (OCT) imaging revolutionised how retinal disease is diagnosed and monitored; however the size and complexity of the equipment limits such evaluations to high-resource clinical settings. Handheld OCT offers the possibility of collecting OCT images in epidemiological studies or low-resource settings. This PhD studentship involves an innovative partnership between researchers at Queen’s University Belfast (School of Electronics,

Electrical Engineering and Computer Science and Centre for Public Health) and Heidelberg Engineering GmbH/Visotec GmbH who are developing the handheld device. It includes a 3- month placement with the industrial partner. This project, embedded within Wave 3 of the Northern Ireland Cohort for the Longitudinal Study of Aging (NICOLA study), aims to enhance retinal disease diagnosis in epidemiological or low resource settings. Our primary goal is to develop novel deep learning methodologies to compare Handheld Optical Coherence Tomography (OCT) and traditional Spectral-domain (SD)-OCT for diagnosing major retinal diseases, develop methods for automatic co-alignment of images captured from either device and use existing domain adaptation techniques to translate images one to the other.

Applications are strongly encouraged from those with a background or experience in one or more of the following areas: computer science, data science, computer vision, and medical image analysis

Funding Information

Funded by the Department for the Economy (DfE). For UK domiciled students the value of an award includes the cost of approved tuition fees and maintenance support the 2024/25 rates are still to be confirmed (current rates for 2023/24 are Fees £4,712, Stipend £18,622). To be considered eligible you must have been ordinarily resident in the UK for the full 3-year period prior to the start of the studentship and you must be ordinarily resident in Northern Ireland on the first day of the start of the studentship. For further information about eligibility criteria please refer to the DfE Postgraduate Studentship Terms and Conditions at https://www.economy-ni.gov.uk/publications/student-finance-postgraduate-studentships-terms-and-conditions

Project Summary
Supervisor

Dr Ruth Hogg

More Information

askmhls@qub.ac.uk

Research Profile


Mode of Study

Full-time: 3 Years


Funding Body
DfE
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