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Vision

Vision

Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.

Visual System

Visual System

Light enters the eye through the cornea, a transparent, dome-shaped surface covering the surface of the eyeball that helps to direct and focus incoming light. This light is then channeled toward the pupil, an adjustable opening whose size is controlled by the iris. The iris, a pigmented muscle, regulates the amount of light entering the eye by contracting or dilating the pupil, thereby ensuring optimal light levels for clear vision.
Once through the pupil, the light passes through the lens, a...

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Related Experiment Video

Updated: Jun 28, 2026

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Vision transformer based interpretable metabolic syndrome classification using retinal Images.

Tae Kwan Lee¹, So Yeon Kim^1,2, Hyuk Jin Choi^3,4

¹Department of Artificial Intelligence, Ajou University, Suwon, South Korea.

NPJ Digital Medicine

|April 11, 2025

Summary

This summary is machine-generated.

This study shows retinal images can help detect metabolic syndrome, a condition increasing diabetes and cardiovascular disease risk. Combining eye scans with clinical data significantly improved detection accuracy.

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Area of Science:

Ophthalmology
Cardiology
Endocrinology

Background:

Metabolic syndrome (MetS) is a growing global health concern, significantly elevating risks for type 2 diabetes and cardiovascular diseases.
Early detection of MetS is crucial for timely intervention and prevention of associated complications.

Purpose of the Study:

To develop and evaluate a model for classifying MetS using retinal fundus images.
To assess the added value of combining retinal imaging with clinical data for MetS classification.
To enhance the interpretability of MetS detection through visualization of relevant retinal features.

Main Methods:

A classification model was developed utilizing retinal fundus photographs obtained during routine health examinations.
The model's performance was evaluated using the Area Under the Curve (AUC) metric.
A visualization technique was employed to identify and highlight areas in retinal images associated with MetS.

Main Results:

The model achieved an AUC of 0.7752 (95% CI: 0.7719-0.7786) when using only retinal images.
Combining retinal images with basic clinical features improved the AUC to 0.8725 (95% CI: 0.8669-0.8781).
Visualizations successfully identified MetS-related regions within the retinal images, aiding interpretability.

Conclusions:

Retinal fundus images hold significant potential as a non-invasive tool for classifying MetS.
Integrating retinal imaging with clinical data offers a more accurate approach to MetS detection.
The developed visualization method enhances understanding of the link between retinal features and MetS.