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Multi-Modal Data Analysis on Retina Data Using AI at the Cell Biology of Vision group

Project Overview:

Age-related Macular Degeneration (AMD) is a progressive eye condition that affects the macula, the central part of the retina responsible for sharp, central vision. It is one of the leading causes of vision loss in individuals over the age of 50. Both genetic predisposition and environmental factors, such as smoking, play a crucial role in AMD development and progression. This project explores the application of artificial intelligence (AI) for multi-modal data analysis in the context of retinal data. By integrating diverse data types, such as in vivo imaging and omics data, the aim is to enhance the precision of retinal disease diagnosis, monitor disease progression, and develop personalized treatment strategies for conditions such as Age-related Macular Degeneration (AMD) and Diabetic Retinopathy.

Research Objectives:

  1. Develop AI algorithms to integrate multi-modal data, including optical coherence tomography (OCT), optical coherence tomography angiography (OCTA), electroretinogram, microelectrode arrays, and various omics data (genomics, proteomics, metabolomics) from retinal organoids, considering both genetic risk factors and environmental influences like smoking.
  2. Create AI models capable of accurately identifying and classifying retinal diseases by analysing the combined imaging, genetic, and environmental data to provide deeper insights into disease mechanisms and progression..

Eligibility:

  • Master students with a major in computer science, bioinformatics, biomedical engineering, artificial intelligence, or a related area in the final stage of master level studies are invited to apply.
  • Proficiency in programming with Python or MATLAB.
  • Experience or interest in machine learning, medical data analysis.
  • The project can be tailored to either a Master's thesis or internship project.

If you interested, send your CV to Zohreh Hosseinzadeh(zohreh.hosseinzadeh@radboudumc.nl) and Nadieh Khalili(nadieh.khalili@radboudumc.nl), or contact Tom Claassen (tom.claassen@ru.nl) for more information.