Precision Retinal Biomarkers for Inherited Retinal Diseases (PR-BIRD)

Background

Inherited Retinal Dystrophies (IRDs) are the leading cause of blindness in children and adults of working age1, affecting thousands of individuals across the UK. Caused by pathogenic variants in one of several hundred genes2, IRDs result in progressive degeneration of retinal photoreceptors and irreversible vision loss. The consequences extend far beyond visual impairment, impacting education, employment, independence, mental health, and quality of life, and contributing to significant health inequalities. Recent advances in gene-specific and gene-editing therapies have transformed prospects for patients with previously untreatable conditions. However, several clinical trials have struggled to demonstrate efficacy3, despite strong biological rationale. A major challenge is the lack of sensitive, reliable biomarkers capable of identifying the optimal treatment window, objectively measuring disease progression, and detecting therapeutic response. Existing clinical measures often change slowly, exhibit substantial variability, and may not fully capture clinically meaningful disease progression. This PhD project will address this unmet need by leveraging one of the UK's largest deeply phenotyped longitudinal IRD cohorts. Drawing on existing data collected from more than 500 patients with genetically characterised IRDs, the project will integrate multimodal retinal imaging, visual function testing, demographic information, and genotype data to develop robust imaging biomarkers for personalised disease prediction and clinical-trial evaluation.

Aims

To develop and validate quantitative retinal imaging biomarkers that enable personalised prediction of disease progression in inherited retinal dystrophies and support equitable access to emerging precision therapies.

Objectives

The student will: 

1. Develop a comprehensive, research-ready IRD dataset from the existing data by harmonising longitudinal clinical records, visual function measurements, multimodal retinal imaging, demographic characteristics, and genetic diagnoses. 

2. Apply advanced image analysis and machine-learning approaches to identify structural retinal biomarkers associated with disease severity and progression across diverse IRD genotypes. 

3. Develop personalised models of disease progression that integrate imaging, genotype, and clinical factors to predict future visual decline at the individual-patient level. 

4. Quantify uncertainty and heterogeneity in progression estimates, improving confidence in individual prognostic predictions and treatment decisions. 

5. Examine model performance across demographic and socioeconomic groups to ensure that emerging precision-medicine approaches are robust, generalisable, and do not inadvertently exacerbate health inequalities. 

6. Evaluate novel biomarkers as potential clinical-trial endpoints capable of detecting disease progression and treatment effects more sensitively than current measures.

Training Outcomes

• Inherited retinal dystrophies and precision medicine: understanding disease genetics, natural history, therapeutic development, and clinical-trial applications.
• Health data science and multimodal data integration: curating, managing, and linking longitudinal clinical, imaging, functional, and genetic datasets.
• Medical image analysis and AI: applying advanced image processing, machine learning, and deep learning methods for biomarker discovery and prediction. 
• Quantitative modelling and biomarker validation: developing and evaluating prognostic models, digital biomarkers, and clinical-trial endpoints using longitudinal data. 
• Responsible and translational research: data governance, ethics, reproducible research, scientific communication, patient engagement, and interdisciplinary collaboration.