Supervisors
Heather Yorston, heather.yorston@ed.ac.uk
Stuart King, s.king@ed.ac.uk
Project Description
Idiopathic full-thickness macular holes (MHs) form secondary to age-related abnormalities of the vitreoretinal interface with a prevalence of up to 3 in 1000 people over the age of 55. They appear as a small dehiscence in the neurosensory retina at the centre of the fovea, a highly specialised part of the human retina responsible for fine acuity and colour vision. Spectral-domain (SD) optical coherence tomography (OCT) imaging allows ophthalmologists to diagnose, classify and measure MHs. OCT is a non-invasive, high-resolution imaging technique that uses infrared light to image the retina in 3D.
Macular holes can be effectively treated by closing the hole using vitrectomy surgery. They are one of the commonest indications for vitrectomy surgery accounting for ~4000 surgeries in UK and more than 200,000 globally per annum. Predicting the visual outcome after surgery is important to guide the decision to operate and manage patients’ expectations, as well providing insight into their pathology.
Several studies have shown that postoperative visual acuity (VA) is correlated with a variety of measures of macular hole size that can be measured on SD-OCT. Various studies have attempted to precisely predict postoperative VA using manual 2D measurements of MHs and preoperative VA, although their predictive ability has been limited. Three-dimensional automated image reconstruction has improved this ability, but there are no current standards for shape, size, and resolution of OCT imaging data captured by different OCT devices for this task. There are also many qualitative features and subtle alterations in retinal anatomy, for example, associated with chronicity, which may be predictive of acuity outcomes and that are difficult to measure. Additionally, image artefacts related to a patient’s eye movement and media opacity pose a further challenge in developing image informatics methods.
Most existing machine learning (ML) and deep learning (DL) approaches have focused on the automated classification of macular diseases, such as age-related macular degeneration (AMD), diabetic macular oedema (DME), and MHs from OCT image data. More recently, some DL approaches have attempted to improve the prediction of VA outcomes using OCT data although these have been very limited and mainly in diseases other than MH. This project will try novel ML/DL approaches guided by clinical knowledge to improve the prediction of VA outcome for patients.
