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

The Retina01:32

The Retina

66.2K
The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.
66.2K

You might also read

Related Articles

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

Sort by
Same author

Interpreting peripheral oxygen saturation variability in critical illness: A directional framework adjusted for hypoxia severity.

Experimental physiology·2026
Same author

Identification of sensorineural hearing loss subtypes using unsupervised machine learning and assessment of their replicability.

Scientific reports·2026
Same author

Automating the extraction of otology symptoms from clinic letters: a methodological study using natural language processing.

BMC medical informatics and decision making·2025
Same author

Uncovering Phenotypes in Sensorineural Hearing Loss: A Systematic Review of Unsupervised Machine Learning Approaches.

Ear and hearing·2025
Same author

Impact of anti-VEGF treatment for diabetic macular oedema on progression to proliferative diabetic retinopathy: data-driven insights from a multicentre study.

BMJ open ophthalmology·2025
Same author

An automated classification pipeline for tables in pharmacokinetic literature.

Scientific reports·2025

Related Experiment Video

Updated: May 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K

Invited Session III: Machine Learning and AI Approaches to Retinal Diagnostics: Towards more robust AI models in

Adam M Dubis1, Mustafa Arikan2, James Willoughby2

  • 1University of Utah, Moran Eye Center.

Journal of Vision
|April 11, 2025
PubMed
Summary

This study focuses on developing safe and robust artificial intelligence (AI) models for ophthalmology. Researchers explore using uncertainty quantification and attention-based networks to improve AI performance in medical imaging tasks.

More Related Videos

Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.3K
Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.4K

Related Experiment Videos

Last Updated: May 8, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.6K
Artificial Intelligence Approaches to Assessing Primary Cilia
08:58

Artificial Intelligence Approaches to Assessing Primary Cilia

Published on: May 1, 2021

3.3K
Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.4K

Area of Science:

  • Ophthalmology
  • Medical Artificial Intelligence (AI)
  • Computer Vision

Background:

  • Ophthalmic care has rigorous approval processes for new treatments, ensuring safety and efficacy.
  • Artificial intelligence (AI) offers transformative potential in healthcare, including ophthalmology.
  • Current AI applications in medicine face challenges in meeting established safety and robustness standards.

Purpose of the Study:

  • To develop safe and robust AI models for ophthalmology functions.
  • To explore the use of uncertainty quantification for determining data value and enhancing model robustness.
  • To address challenges in medical AI, such as imbalanced data and small object detection.

Main Methods:

  • Developing AI models for segmentation, classification, and object detection tasks.
  • Utilizing uncertainty quantification to improve model robustness against adversarial attacks.
  • Implementing attention-based network features to leverage retinal anatomy for object detection.

Main Results:

  • Demonstrated strategies for enhancing AI model robustness using uncertainty.
  • Showcased methods for improving AI performance on imbalanced datasets in ophthalmology.
  • Applied techniques to tasks like image segmentation, classification, and object detection.

Conclusions:

  • Uncertainty quantification is crucial for developing reliable AI in ophthalmology.
  • Attention-based networks can effectively utilize anatomical structures for improved AI performance.
  • Further development is needed to ensure AI meets rigorous standards for medical applications.