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

You might also read

Related Articles

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

Sort by
Same author

Synthetic data-driven diabetic retinopathy diagnosis with explainable AI: a clinically interpretable framework.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2026
Same author

Unfolding the diagnostic pipeline of diabetic retinopathy with artificial intelligence: A systematic review.

Survey of ophthalmology·2025
Same author

Morphological Abnormalities Classification of Red Blood Cells Using Fusion Method on Imbalance Datasets.

Microscopy research and technique·2025
Same author

Deep feed forward neural network-based screening system for diabetic retinopathy severity classification using the lion optimization algorithm.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2021

Related Experiment Video

Updated: Jul 2, 2025

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies
12:28

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Published on: March 12, 2022

3.6K

An enumerative pre-processing approach for retinopathy severity grading using an interpretable classifier: a

Hemanth Kumar Vasireddi1,2, Suganya Devi K3, G N V Raja Reddy1,4

  • 1Computer Science and Engineering, National Institute of Technology, Silchar, 788010, Assam, India.

Graefe'S Archive for Clinical and Experimental Ophthalmology = Albrecht Von Graefes Archiv Fur Klinische Und Experimentelle Ophthalmologie
|February 24, 2024
PubMed
Summary

An artificial intelligence (AI) system was developed to improve diabetic retinopathy (DR) screening. The novel approach achieved high accuracy in DR severity grading, enhancing diagnostic efficacy.

Keywords:
Artificial intelligenceDeep learningDiabetic retinopathyInterpretable classifierNeural networksOptimizationPre-processing

More Related Videos

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.6K
In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility
07:35

In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility

Published on: April 21, 2022

2.2K

Related Experiment Videos

Last Updated: Jul 2, 2025

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies
12:28

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Published on: March 12, 2022

3.6K
Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.6K
In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility
07:35

In Vivo Vascular Injury Readouts in Mouse Retina to Promote Reproducibility

Published on: April 21, 2022

2.2K

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Diabetic retinopathy (DR) is a leading cause of irreversible vision loss.
  • Increasing DR prevalence necessitates efficient diagnostic tools.
  • Current artificial intelligence (AI) systems for DR screening require accuracy improvements.

Purpose of the Study:

  • To develop and implement an AI-based screening system for diabetic retinopathy (DR) using color fundus photographs.
  • To enhance the accuracy and efficiency of DR diagnosis.

Main Methods:

  • An enumerative pre-processing approach was integrated into a deep learning model for DR severity grading.
  • The proposed model was compared against various pre-trained models and optimization algorithms.
  • Performance metrics including accuracy, precision, recall, and F1 score were evaluated on the MESSIDOR dataset.

Main Results:

  • The enumerative pipeline combination K1-K2-K3-DFNN-LOA demonstrated superior performance.
  • The proposed model achieved a maximum accuracy of 97.60%, precision of 94.60%, recall of 98.40%, and F1 score of 94.60%.
  • The macro-averaged metric reached 0.97, indicating robust performance.

Conclusions:

  • The developed AI system effectively screens diabetic retinopathy from color fundus images.
  • This system has the potential to improve the efficiency and accessibility of DR diagnosis.
  • Further advancements in AI can enhance clinical decision-making for eye conditions.