Optimization of Diagnosis and Treatment for AMD Based on Machine Learning

Lead Research Organisation: University College London
Department Name: Computer Science

Abstract

1) Brief description of the context of the research including potential impact

The PhD will employ advanced machine learning tool to optimize the diagnosis accuracy and treatment strategy for Age-related macular degeneration (AMD). This research would promote the medical AI application in the ophthalmology.

2) Aims and objectives

AMD is the leading cause of irreversible blindness in the UK, Europe, and North America. In the UK alone, nearly 200 people develop the blinding forms of AMD every single day. Much of the visual loss in AMD occurs due to the development of choroidal neovascularization (CNV) - so called wet or neovascular AMD. In recent years, this condition can be successfully treated with pharmacotherapies that block vascular endothelial growth factor (VEGF). Unfortunately, patients receiving this treatment require intraocular injections on a monthly basis over many years. In order to reduce the burden on patients, while preventing irreversible sight loss, a number of variable treatment regimens have evolved.

3) Novelty of the research methodology

With the boosting development of medical AI research, deep learning offers strong support in disease diagnosis due to the representation capability. By looking into the deep learning core principle, we could explore the reliability and interpretability of AI in ophthalmology. Simultaneously unsupervised learning can identify distinct groups and their trajectories of decline supporting precision assignment of individuals to the right treatment regime for them. The PhD will develop such algorithms and methods and work with clinicians to enable application on real-world datasets.

4) Alignment to EPSRC's strategies and research areas

The PhD's work fits primarily in the research themes of "Healthcare Technologies" and "Information and communication technologies", and within the Research areas of "Artificial Intelligence Technologies" and "Medical Imaging".

5) Any companies or collaborators involved

Moorfields Eye Hospital NHS Foundation Trust.

Publications

10 25 50

Studentship Projects

Project Reference Relationship Related To Start End Student Name
EP/S021930/1 01/10/2019 31/03/2028
2368226 Studentship EP/S021930/1 06/01/2020 05/01/2024 Yukun Zhou