The main aim is to create a deep learning model based on retinal pictures that can distinguish between people with Alzheimer’s disease and people without dementia with a level of accuracy that is consistent across time.
The most widespread type of dementia is Alzheimer’s disease (AD) and It is a brain condition that gradually robs people of their memory, thinking abilities, and ultimately their capacity to complete even the most basic tasks. Alzheimer’s disease (AD) is a complex neurodegenerative condition with a number of known and improbable causes, as well as a wide range of anatomical features. The retina, which is a part of the central nervous system (CNS), has been called a “window to the brain” and a brand-new indicator of Alzheimer’s disease (AD). Retina tests are appropriate for large-scale population screening and research into preclinical AD because of their low cost, simplicity of access, and non-invasive properties. Additionally, a number of cutting-edge techniques for retinal imaging, such as optical coherence tomography (OCT), have been developed that enable the visualisation of retinal alterations at a very fine resolution.
Therefore, the primary goal is to create a deep learning model based on retinal images to identify people who have Alzheimer’s disease and to consistently perform accurately in differentiating between people who have Alzheimer’s disease and people who do not have dementia with respect to both accuracy and sensitivity.
The Project can be completed within 36 months.
So the public can perform the Alzheimers screening at very low expense or even at free of cost and within short while. At present there is no such facility is available for this purpose in most of the developing countries like India. Right now the public have pay a lot of money for Alzheimers screening (for PET Scan or Brain imaging etc).