Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Depth Perception and Spatial Vision01:15

Depth Perception and Spatial Vision

1.5K
Depth perception is the ability to perceive objects three-dimensionally. It relies on two types of cues: binocular and monocular. Binocular cues depend on the combination of images from both eyes and how the eyes work together. Since the eyes are in slightly different positions, each eye captures a slightly different image. This disparity between images, known as binocular disparity, helps the brain interpret depth. When the brain compares these images, it determines the distance to an object.
1.5K

You might also read

Related Articles

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

Sort by
Same author

Correction: Daily sitting time and past-year falls in Japanese adults: an exploratory cross-sectional analysis.

Frontiers in sports and active living·2026
Same author

Usefulness of the Entropy of the Bayesian Posterior Distribution of the Threshold to Assess the Reliability of Measured Visual Field.

Current eye research·2026
Same author

Effect of 2,2,2-trifluoroethyl methacrylate on the gelation characteristics of light-cured soft denture liners based on 2-ethylhexyl methacrylate and acetyl tributyl citrate.

Dental materials journal·2026
Same author

Intraocular Distribution and Aqueous-Vitreous Correlation of TGF-β Isoforms and GDF-15 in Retinal Diseases.

Current eye research·2026
Same author

A case report of double Meckel's diverticulum with a mobile cecum.

BMC surgery·2026
Same author

Twelve-Month Safety Profile of PreserFlo MicroShunt on Corneal Endothelium in Glaucoma Subtypes.

Journal of glaucoma·2026

Related Experiment Video

Updated: Dec 12, 2025

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

8.8K

Improving Visual Field Trend Analysis with OCT and Deeply Regularized Latent-Space Linear Regression.

Linchuan Xu1, Ryo Asaoka2, Hiroshi Murata3

  • 1Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan; Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong.

Ophthalmology. Glaucoma
|August 14, 2020
PubMed
Summary

Integrating Optical Coherence Tomography (OCT) measurements significantly improves visual field (VF) trend analysis in glaucoma patients. This approach enhances the prediction of future VF progression, particularly when using limited VF data.

Keywords:
Deep learningGlaucomaOCTProgressionVisual field

More Related Videos

Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment
07:02

Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment

Published on: June 30, 2023

2.0K
Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.8K

Related Experiment Videos

Last Updated: Dec 12, 2025

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT
12:22

Multimodal Volumetric Retinal Imaging by Oblique Scanning Laser Ophthalmoscopy oSLO and Optical Coherence Tomography OCT

Published on: August 4, 2018

8.8K
Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment
07:02

Author Spotlight: Advancements in In Vivo and Ex Vivo Retinal Imaging for Improved Glaucoma Diagnosis and Treatment

Published on: June 30, 2023

2.0K
Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography
11:21

Integrated Photoacoustic Ophthalmoscopy and Spectral-domain Optical Coherence Tomography

Published on: January 15, 2013

11.8K

Area of Science:

  • Ophthalmology
  • Medical technology
  • Data science in healthcare

Background:

  • Glaucoma is a progressive optic neuropathy leading to irreversible vision loss.
  • Accurate prediction of visual field (VF) progression is crucial for timely intervention in glaucoma management.
  • Current VF trend analyses may lack precision, especially with limited longitudinal data.

Purpose of the Study:

  • To evaluate the efficacy of incorporating Optical Coherence Tomography (OCT) measurements into a deep learning model for enhanced visual field (VF) trend analysis in glaucoma.
  • To compare the predictive accuracy of the Deeply Regularized Latent-space Linear Regression (DLLR) model with OCT data against traditional methods.

Main Methods:

  • A retrospective cohort study utilized data from 592 glaucoma patients (998 eyes).
  • Visual field (VF) test results and OCT measurements (retinal nerve fiber layer, ganglion cell layer, inner plexiform layer, outer segment, and retinal pigment epithelium thickness) were collected.
  • The DLLR model integrated OCT data to predict the eighth VF test results, comparing its accuracy (RMSE) against pointwise linear regression (PLR).

Main Results:

  • The DLLR model demonstrated significantly lower Root Mean Square Error (RMSE) compared to PLR across different data series lengths.
  • For predicting the eighth VF from the first two tests (VF1-2), DLLR achieved an RMSE of 4.57 ± 2.71 dB, versus 27.48 ± 16.14 dB for PLR (P < 0.001).
  • For longer series (VF1-7), DLLR's RMSE was 3.65 ± 2.27 dB, only slightly better than PLR's 3.98 ± 2.25 dB, highlighting DLLR's advantage with shorter data.

Conclusions:

  • Incorporating OCT measurements into the DLLR model substantially improves the accuracy of visual field (VF) trend analysis in glaucoma.
  • This multimodal approach is particularly beneficial for predicting glaucoma progression when limited VF data is available.
  • The findings support the clinical utility of OCT in refining glaucoma monitoring and management strategies.