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Related Experiment Video

Updated: Dec 14, 2025

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography

In vivo Structural Assessments of Ocular Disease in Rodent Models using Optical Coherence Tomography

Published on: July 24, 2020

Insights into Systemic Disease through Retinal Imaging-Based Oculomics.

Siegfried K Wagner¹, Dun Jack Fu¹, Livia Faes^1,2

¹NIHR Biomedical Research Center at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK.

Translational Vision Science & Technology

|July 25, 2020

Summary

Ophthalmology advancements reveal eye imaging can detect systemic diseases like cardiovascular disease and dementia. This "oculomics" approach uses retinal scans for early risk assessment.

Keywords:

artificial intelligence deep learning optical coherence tomography

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Last Updated: Dec 14, 2025

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Area of Science:

Ophthalmology
Oculomics
Biomarkers

Background:

High-resolution, non-invasive imaging in ophthalmology has advanced significantly.
Evidence suggests ocular imaging can identify systemic disease biomarkers, a field termed "oculomics".

Purpose of the Study:

To review the role of retinal morphology in cardiovascular disease and dementia.
To explore the potential of artificial intelligence in analyzing eye-body relationships for disease risk stratification.

Main Methods:

Review of population-based analyses using fundus photography for retinal microvascular indices.
Examination of studies linking neurosensory retina structure to neurodegenerative diseases like Alzheimer's disease.

Main Results:

Retinal microvascular indices predict cardiovascular events like heart attack and stroke.
Established associations exist between retinal structure and neurodegenerative conditions, notably Alzheimer's disease.

Conclusions:

Oculomics offers a promising avenue for identifying systemic disease biomarkers.
Artificial intelligence, particularly deep learning, can enhance the analysis of multimodal datasets for improved risk stratification of age-related chronic diseases.