Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Machine Learning With Optical Coherence Tomography for Glaucoma Diagnosis.

Journal of glaucoma·2026

Same author

Association between 24-Hour Blood Pressure and Rates of Retinal Nerve Fiber Layer Progression in Glaucoma: The Vascular Imaging in Glaucoma Study.

American journal of ophthalmology·2026

Same author

Detecting glaucoma progression through optic nerve head hemoglobin concentration using automated colorimetric analysis.

European journal of ophthalmology·2026

Same author

The association of cataract surgery with risk of falls and fractures among Medicare enrollees with cataract.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026

Same author

Performance of a Small Language Model Versus a Large Language Model in Answering Glaucoma Frequently Asked Patient Questions: Development and Usability Study.

JMIR AI·2026

Same author

Compact vision language models enable efficient and interpretable optical coherence tomography through layer-specific multimodal learning.

Communications medicine·2025

Search research articles

Related Experiment Video

Updated: Nov 19, 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

Predicting Age From Optical Coherence Tomography Scans With Deep Learning.

Leonardo S Shigueoka^1,2, Eduardo B Mariottoni¹, Atalie C Thompson¹

¹Vision, Imaging and Performance Laboratory (VIP), Duke Eye Center and Department of Ophthalmology, Duke University, Durham, NC, USA.

Translational Vision Science & Technology

|January 29, 2021

Summary

Age can be accurately predicted from retinal images using deep learning (DL) analysis of spectral-domain optical coherence tomography (SD-OCT) B-scans. This technology highlights how aging impacts various posterior eye structures.

Keywords:

aging artificial intelligence deep learning optical tomography coherence posterior eye segment

More Related Videos

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Published on: May 26, 2023

Application of Optical Coherence Tomography to a Mouse Model of Retinopathy

Application of Optical Coherence Tomography to a Mouse Model of Retinopathy

Published on: January 12, 2022

Related Experiment Videos

Last Updated: Nov 19, 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

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Author Spotlight: Ex Vivo OCT-Based Multimodal Imaging of Human Donor Eyes for Research into Age-Related Macular Degeneration

Published on: May 26, 2023

Application of Optical Coherence Tomography to a Mouse Model of Retinopathy

Application of Optical Coherence Tomography to a Mouse Model of Retinopathy

Published on: January 12, 2022

Area of Science:

Ophthalmology
Artificial Intelligence
Medical Imaging

Background:

Aging affects ocular structures, but non-invasive biomarkers are needed.
Spectral-domain optical coherence tomography (SD-OCT) provides detailed cross-sectional retinal images.
Deep learning (DL) shows potential for analyzing complex medical image data.

Purpose of the Study:

To determine if chronological age can be predicted from peripapillary SD-OCT B-scans using DL.
To identify which retinal areas are most crucial for accurate age prediction.

Main Methods:

Developed DL convolutional neural networks to predict age from peripapillary SD-OCT B-scans.
Employed image ablation techniques to assess the importance of specific retinal regions.
Validated model performance using cross-validation, Mean Absolute Error (MAE), and correlation coefficients.

Main Results:

DL models accurately predicted age from whole B-scans (MAE = 5.82 years, r = 0.860).
The model achieved high discrimination between age tertiles (AUC = 0.962).
The choroid and vitreous showed the strongest correlation with age (r = 0.736), while the retinal nerve fiber layer had the lowest (r = 0.492).

Conclusions:

Deep learning algorithms can reliably predict age using peripapillary SD-OCT B-scans.
Aging impacts multiple posterior eye segment layers, as indicated by DL analysis.