AI-estimated age using optical coherence tomography to predict disease progression and cognitive function in multiple sclerosis.

Age-related biomarkers such as telomere length and ‘brain age’ have shown accelerated biological aging and are associated with the accumulation of disability and cognitive dysfunction in individuals with multiple sclerosis (MS). Optical coherence tomography (OCT) high resolution imaging of the retina and is increasingly being performed in the management of MS to evaluate visual function and monitoring of disease progression. Our collaborators have developed and validated an artificial intelligence (AI) model to estimate biological age using OCT images. We are undertaking an innovative prospective cohort study to collect OCT images, longitudinal cognitive function outcomes and clinical data to determine the association between AI-derived retinal biological age, disease progression and cognitive decline in MS. We are looking for talented PhD students with an interest in clinical research in the fields of Neuro-ophthalmology, Neuroscience or implementation science. Roles involved with this project will include data collection and curation; data analyses may include longitudinal growth models, survival analysis and machine learning models. This project will also utilise clinical outcomes data derived from the international MSBase Registry, and patient-reported data obtained through participant surveys. Outcomes and impact: Using routine OCT imaging and innovative digital biomarkers of cognitive function will enhance our knowledge on the effects of accelerated biological aging on disability progression and cognitive decline; in MS and will guide development on new biomarkers of disease progression in multiple sclerosis. Research environment: This project is set in a multidisciplinary environment including neurologists, neuro-ophthalmologists, neuroscientists, nurses and biostatisticians.