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  3. Stage Challenge: Structural-functional Transition In Glaucoma Assessment.
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  3. Stage Challenge: Structural-functional Transition In Glaucoma Assessment.

Related Experiment Video

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

STAGE challenge: Structural-Functional Transition in Glaucoma Assessment.

Shiqi Zhou1, Yuancong Liang1, Huihui Fang2

  • 1School of Future Technology, South China University of Technology, Guangzhou, China; Pazhou Lab, Guangzhou, China.

Medical Image Analysis
|May 17, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces the STAGE Challenge, a new benchmark for predicting glaucoma's functional visual field loss from structural OCT scans using deep learning. The challenge highlights the effectiveness of task-specific models in linking ocular structure to visual function.

Keywords:
GlaucomaRetinal OCTStructural-function transitionVF examination

Related Experiment Videos

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential
07:11

Assessing Early Stage Open-Angle Glaucoma in Patients by Isolated-Check Visual Evoked Potential

Published on: May 25, 2020

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Glaucoma is a primary cause of irreversible blindness, with diagnosis relying on time-consuming visual field (VF) tests.
  • Current deep learning models for glaucoma often focus on structural images (OCT, fundus) but neglect functional VF outcomes.

Purpose of the Study:

  • To bridge the gap between structural imaging and functional vision assessment in glaucoma.
  • To establish a standardized benchmark and dataset for evaluating algorithms that predict functional VF indicators from structural OCT images.

Main Methods:

  • The STAGE Challenge utilized a dataset of 401 OCT volumes with corresponding VF labels and demographic data.
  • Seven finalist teams developed dual-branch deep learning architectures integrating OCT and tabular data.
  • Task-specific OCT models were investigated for improved performance in predicting VF indicators.

    • Main Results:

    • All participating teams employed dual-branch architectures combining OCT and tabular data.
    • Models utilizing task-specific OCT approaches demonstrated superior performance in predicting glaucoma's functional visual field indicators.
    • The challenge provided a standardized framework for comparing structure-function analysis algorithms.

    Conclusions:

    • Tailored deep learning strategies are crucial for accurately linking structural imaging data to functional outcomes in glaucoma assessment.
    • The STAGE Challenge dataset and framework serve as a foundation for future research in glaucoma structure-function analysis.