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

Yuki Hagiwara, Octavia-Andreea Ciora, Maureen Monnet, Gino Lancho, Jeanette Miriam Lorenz

TL;DR

A comprehensive overview of AI techniques utilized in CADx systems for glaucoma diagnosis is provided, emphasizing the need for improved safety, reliability, interpretability, and explainability in these systems.

Abstract

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.

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

TL;DR

A comprehensive overview of AI techniques utilized in CADx systems for glaucoma diagnosis is provided, emphasizing the need for improved safety, reliability, interpretability, and explainability in these systems.

Abstract

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.

Paper Structure

This paper contains 30 sections, 6 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Search Strategy (Methodology)
  3. Result
  4. Data
  5. OCT data
  6. Fundus data
  7. Multiple source data
  8. Other data
  9. Medical Application Area
  10. Categorization of ML-specific Task
  11. Computer Vision and Traditional Machine Learning Approaches
  12. Preprocessing
  13. Segmentation
  14. Features Extraction
  15. Features Selection, Reduction, Ranking
  16. ...and 15 more sections

Figures (6)

  • Figure 1: Workflow of a typical system pipeline.
  • Figure 2: The role of system in clinical environment.
  • Figure 3: Breakdown of the number of papers from 2013 to 2023.
  • Figure 4: Inclusion of relevant papers based on guidelines.
  • Figure 5: Example of an scan (taken from Mendeley dataset).
  • ...and 1 more figures