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

Search research articles

Related Experiment Video

Updated: Jan 4, 2026

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge.

Prasanna Porwal¹, Samiksha Pachade¹, Manesh Kokare²

¹Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India; School of Biomedical Informatics, University of Texas Health Science Center at Houston, USA.

Medical Image Analysis

|November 1, 2019

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

Identification and Experimental Verification of Key Genes Related to the Ras Signaling Pathway and the Hippo Signaling Pathway in Osteoarthritis Based on Transcriptome Data.

The journal of gene medicine·2026

Same author

Visible-Light-Induced Radical Cascade Sulfonylation/Cyclization to Access Sulfonylated Benzazepines and Benzoxepines by EDA Complexes.

The Journal of organic chemistry·2026

Same author

Human-Like Multimodal Fake News Detection via Reflective Summarization and Large-Small Model Collaboration.

IEEE transactions on neural networks and learning systems·2026

Same author

The role of Poller screws in intramedullary nailing for lower limb extra-isthmic fractures.

Journal of orthopaedic surgery and research·2026

Same author

Dataset Distillation via a Noise-Unconstrained Generative Model.

IEEE transactions on pattern analysis and machine intelligence·2026

Same author

AI framework for multidisease detection via retinal imaging.

Nature medicine·2026

Same journal

Embracing intra-class heterogeneity for semi-supervised medical image segmentation: From diversity to precision.

Medical image analysis·2026

Same journal

Real-time patient-specific microwave ablation zone prediction via a unified bioheat solver and MRI-informed perturbation learning.

Medical image analysis·2026

Same journal

Generative morphodynamic forecasting enables robust zero-shot volumetric medical segmentation.

Medical image analysis·2026

Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026

Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026

Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026

See all related articles

Summary

This summary is machine-generated.

Computer-aided diagnosis using machine learning offers a sustainable solution for screening diabetic retinopathy (DR), a leading cause of preventable vision loss. This challenge advanced automatic DR diagnosis through retinal image analysis.

Area of Science:

Biomedical imaging
Artificial intelligence in healthcare
Ophthalmology

Background:

Diabetic Retinopathy (DR) is a primary cause of avoidable vision loss globally, particularly in the working-age population.
Current DR screening programs face challenges due to a shortage of medical professionals for a growing diabetic population.
Computer-aided diagnosis in retinal image analysis presents a sustainable solution for large-scale DR screening.

Purpose of the Study:

To advance the state-of-the-art in automatic Diabetic Retinopathy diagnosis.
To evaluate the generalizability of algorithms for lesion segmentation, disease grading, and retinal landmark localization.
To report the setup, methods, and results of the "Diabetic Retinopathy - Segmentation and Grading" grand challenge.

Main Methods:

Keywords:

Challenge Deep learning Diabetic Retinopathy Retinal image analysis

More Related Videos

Retinal Pathophysiological Evaluation in a Rat Model

Retinal Pathophysiological Evaluation in a Rat Model

Published on: May 6, 2022

Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature

Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature

Published on: January 24, 2025

Related Experiment Videos

Last Updated: Jan 4, 2026

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Retinal Pathophysiological Evaluation in a Rat Model

Retinal Pathophysiological Evaluation in a Rat Model

Published on: May 6, 2022

Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature

Quantification of Diabetes-induced Adherent Leukocytes in Retinal Vasculature

Published on: January 24, 2025

Organized a grand challenge focused on Diabetic Retinopathy (DR) segmentation and grading using the Indian Diabetic Retinopathy Image Dataset (IDRiD).
Included three sub-challenges: lesion segmentation, disease severity grading, and retinal landmark localization.
Collected 148 submissions from 495 registered participants, evaluating top-performing solutions.

Main Results:

Top-performing solutions employed a combination of clinical information, data augmentation, and ensemble modeling techniques.
The challenge successfully tested the generalizability of algorithms across multiple DR-related tasks.
The event fostered significant scientific community engagement with a high number of submissions.

Conclusions:

The findings from the challenge have the potential to drive new developments in retinal image analysis.
Image-based DR screening can be significantly enhanced through advanced computational approaches.
This work highlights the efficacy of machine learning in addressing challenges in large-scale medical screening.