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IMany long-term studies rely on repeated eye scans, using Optical Coherence Tomography (OCT), to understand how vision changes as people get older. Traditionally, these scans are taken using large, stationary machines in specialist clinics. While effective, this approach can be inconvenient and sometimes impractical for older adults, people with limited mobility, or those who need frequent follow-up appointments. Handheld OCT scanners offer a more flexible alternative. They can be used in a wider range of settings and make it easier to collect scans from people who might otherwise struggle to attend regular clinic visits. However, because these devices are portable, the images they produce can vary in quality, making it difficult to compare scans reliably over time. The main focus of my work is to assess whether handheld scanning is truly feasible for long-term studies, and whether it can provide information that is consistent enough to be trusted. To do this, I create and use artificial intelligence models as a practical tool to analyse large numbers of scans in a consistent way. This allows subtle changes to be tracked over time, even when images are not perfectly captured. The aim is not to replace clinicians, but to understand whether handheld scanning, supported by intelligent analysis, can reduce barriers to participation, improve follow-up rates, and make long-term eye monitoring more inclusive. If successful, this approach could help reshape how eye health studies are carried out in ageing populations, making them more flexible, scalable, and patient-friendly.
Visit Alexander's LinkedIn page here.
Primary Supervisor - Professor Ruth Hogg
Secondary Supervisors - Dr. Richard Gault & Dr. Abbas Haidar
Partner Organisation - Heidelberg Engineering
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