mlsanityData to 7 Oct 2026

AI-Driven Approaches for Glaucoma Detection -- A Comprehensive Review

arXiv 2410.15947 ↗First posted 21 Oct 2024eess.IV cs.AI cs.CVcited by 0

Not on the map. Why?

Abstract (arXiv)

The diagnosis of glaucoma plays a critical role in the management and treatment of this vision-threatening disease. Glaucoma is a group of eye diseases that cause blindness by damaging the optic nerve at the back of the eye. Often called "silent thief of sight", it exhibits no symptoms during the early stages. Therefore, early detection is crucial to prevent vision loss. With the rise of Artificial Intelligence (AI), particularly Deep Learning (DL) techniques, Computer-Aided Diagnosis (CADx) systems have emerged as promising tools to assist clinicians in accurately diagnosing glaucoma early. This paper aims to provide a comprehensive overview of AI techniques utilized in CADx systems for glaucoma diagnosis. Through a detailed analysis of current literature, we identify key gaps and challenges in these systems, emphasizing the need for improved safety, reliability, interpretability, and explainability. By identifying research gaps, we aim to advance the field of CADx systems especially for the early diagnosis of glaucoma, in order to prevent any potential loss of vision.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 18 of its 246 references that are in the map. First posted on arXiv 21 Oct 2024.

Builds on

18 of 246 references.

References · 18

  1. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  2. 2014ImageNet Large Scale Visual Recognition Challengecited by 8,441
  3. 2017Deep Learning for Medical Image Analysiscited by 399
  4. 2019REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographscited by 226
  5. 2019Attention Based Glaucoma Detection: A Large-scale Database and CNN Modelcited by 75
  6. 2019Patch-based Output Space Adversarial Learning for Joint Optic Disc and Cup Segmentationcited by 44
  7. 2018Disc-aware Ensemble Network for Glaucoma Screening from Fundus Imagecited by 39
  8. 2023AIROGS: Artificial Intelligence for RObust Glaucoma Screening Challengecited by 35
  9. 2020G1020: A Benchmark Retinal Fundus Image Dataset for Computer-Aided Glaucoma Detectioncited by 31
  10. 2020AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomographycited by 15
  11. 2021FUTURE-AI: Guiding Principles and Consensus Recommendations for Trustworthy Artificial Intelligence in Medical Imagingcited by 13
  12. 2022Dataset and Evaluation algorithm design for GOALS Challengecited by 13
6 more
  1. 2018A feature agnostic approach for glaucoma detection in OCT volumescited by 12
  2. 2021Real-Time Glaucoma Detection from Digital Fundus Images using Self-ONNscited by 12
  3. 2020Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learningcited by 11
  4. 2018From Machine to Machine: An OCT-trained Deep Learning Algorithm for Objective Quantification of Glaucomatous Damage in Fundus Photographscited by 9
  5. 2019Fully Convolutional Networks for Monocular Retinal Depth Estimation and Optic Disc-Cup Segmentationcited by 7
  6. 2021Dynamic region proposal networks for semantic segmentation in automated glaucoma screeningcited by 2

Built on by

Not cited yet.