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Related Experiment Video

Updated: May 24, 2025

Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats

Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats

Published on: January 10, 2019

Oculomics: Current concepts and evidence.

Zhuoting Zhu¹, Yueye Wang², Ziyi Qi³

¹Centre for Eye Research Australia, Ophthalmology, University of Melbourne, Melbourne, VIC, Australia; Department of Surgery (Ophthalmology), University of Melbourne, Melbourne, VIC, Australia.

Progress in Retinal and Eye Research

|March 6, 2025

Summary

This summary is machine-generated.

Oculomics uses eye imaging to reveal systemic health conditions, accelerating disease detection and personalized medicine. Advances in AI and big data are key to understanding the eye-body connection for better health outcomes.

Keywords:

Artificial intelligence Big data Disease detection Disease prediction Oculomics Retinal imaging Systemic diseases

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Last Updated: May 24, 2025

Using Optical Coherence Tomography and Optokinetic Response As Structural and Functional Visual System Readouts in Mice and Rats

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Published on: January 10, 2019

Using Looming Visual Stimuli to Evaluate Mouse Vision

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Published on: June 13, 2019

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

Area of Science:

Ophthalmology and Systemic Health
Biomarker Discovery
Medical Imaging Analysis

Background:

The eye's structure and function reflect systemic health, particularly cardiovascular, neurodegenerative, and kidney diseases.
Oculomics applies ophthalmic biomarkers to understand disease mechanisms and predict health outcomes.
Growing evidence links ocular findings to various systemic health conditions.

Purpose of the Study:

To review current concepts and evidence in oculomics.
To highlight progress, challenges, and future opportunities in the field.
To explore the role of artificial intelligence in advancing oculomics.

Main Methods:

Leveraging high-resolution, non-invasive ophthalmic imaging technologies.
Utilizing large-scale datasets linking ocular imaging with systemic health data.
Applying advanced analytical methods, including artificial intelligence (AI).

Main Results:

Oculomics enables detection, screening, diagnosis, and monitoring of systemic diseases.
AI integration enhances the prediction of systemic disease risk and facilitates personalized medicine.
Ophthalmic biomarkers offer novel insights into disease pathogenesis and development.

Conclusions:

Oculomics is a rapidly advancing field with significant potential for diagnostics and therapeutics.
AI and big data are crucial for unlocking the full potential of oculomics.
Further research is needed to address remaining challenges and expand clinical applications.