Difference between revisions of "Retinal vasculature"
| Line 12: | Line 12: | ||
'''The advantage in working with RV'''<br> | '''The advantage in working with RV'''<br> | ||
<small>There is a significant interest in working with RV. These blood vessels can provide important diagnostic information about the health of the eye and the body as a whole. Changes in the RV can be a sign of various medical conditions, including diabetes, hypertension, and cardiovascular disease. Therefore, the examination of the retinal vasculature is an important part of a comprehensive eye exam and can help detect and monitor these conditions. | <small>There is a significant interest in working with RV. These blood vessels can provide important diagnostic information about the health of the eye and the body as a whole. Changes in the RV can be a sign of various medical conditions, including diabetes, hypertension, and cardiovascular disease. Therefore, the examination of the retinal vasculature is an important part of a comprehensive eye exam and can help detect and monitor these conditions. | ||
| − | Moreover, obtaining images of the retinal vasculature can be relatively easy and cost-effective with modern technology, such as during a routine exam at the ophtalmologiste. There are various methods known to be non-invasive and generally well-tolerated by patients.</small> | + | Moreover, obtaining images of the retinal vasculature can be relatively easy and cost-effective with modern technology, such as during a routine exam at the ophtalmologiste. There are various methods known to be non-invasive and generally well-tolerated by patients.</small><br> |
| + | <br> | ||
| + | '''Deep Learning: Revealing Insights through Retinal Vasculature Embedding'''<br> | ||
| + | <small>Our aim is to use deep learning to embed retinal vasculature images into a lower dimensional space, enabling us to gain insights or make predictions.<br> | ||
| + | To do so, we will first build DL algorithms to produce a latent space to detect and highlight specific features.<br> | ||
| + | Then, the results can be used to learn about the behavior of the algorithm, use this latent space for potential inference on diagnostics and genetic correlation.</small> | ||
Revision as of 12:06, 27 May 2023
Project presented by Leïla Ouhamma & Audran Feuvrier
Supervised by Daniel Krefl
Introduction
The Retinal vasculature (RV) refers to the blood vessels that supply the retina, which is the thin layer of tissue located at the back of the eye.
It is responsible for detecting light and sending visual information to the brain.
The retinal vasculature includes both arteries and veins, which branch out from the optic nerve and supply oxygen and nutrients to the retina.
The advantage in working with RV
There is a significant interest in working with RV. These blood vessels can provide important diagnostic information about the health of the eye and the body as a whole. Changes in the RV can be a sign of various medical conditions, including diabetes, hypertension, and cardiovascular disease. Therefore, the examination of the retinal vasculature is an important part of a comprehensive eye exam and can help detect and monitor these conditions.
Moreover, obtaining images of the retinal vasculature can be relatively easy and cost-effective with modern technology, such as during a routine exam at the ophtalmologiste. There are various methods known to be non-invasive and generally well-tolerated by patients.
Deep Learning: Revealing Insights through Retinal Vasculature Embedding
Our aim is to use deep learning to embed retinal vasculature images into a lower dimensional space, enabling us to gain insights or make predictions.
To do so, we will first build DL algorithms to produce a latent space to detect and highlight specific features.
Then, the results can be used to learn about the behavior of the algorithm, use this latent space for potential inference on diagnostics and genetic correlation.
