Machine learning applied to retinal image processing for glaucoma detection: review and perspective
importance 5/10
ai assisted
Retinal Imaging and Analysis ophthalmology stage: research
Problem
Detecting and diagnosing glaucoma from retinal images using machine learning classification algorithms.
Why it matters
Computer-aided glaucoma detection supports ophthalmologists making diagnostic decisions.
What the AI did
Systematic review of ML classification methods for retinal image processing in glaucoma detection, covering papers from 2014-2019.
Results
not stated in source
Impact
Summarizes 2014-2019 ML approaches to guide glaucoma-detection research.
Record
| Venue | BioMedical Engineering OnLine |
|---|---|
| Published | 2020 |
| Work type | article |
| Authors | Daniele M. S. Barros, Julio C. C. Moura, Cefas Rodrigues Freire, Alexandre Chater Taleb, Ricardo Alexsandro de Medeiros Valentim, Philippi Sedir Grilo de Morais |
| Citations | 138 (OpenAlex, at import time) |
| References | 81 |
| Model or method | not stated in source |
| Organization | not stated in source |
| Primary source | https://doi.org/10.1186/s12938-020-00767-2 |
| Open access | gold |
| Keywords | machine learning, glaucoma, retinal images, computer-aided diagnosis, systematic review |
Importance rating
5
real but narrower advance
Glaucoma detection aids ophthalmologist decisions, a real clinical need, but this systematic review states no scale, metrics, or disease burden figures.