OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning
Abstract Glaucoma poses a growing health challenge projected to escalate in the coming decades. However, current automated diagnostic approaches on Glaucoma diagnosis solely rely on black-box deep learning models, lacking explainability and trustworthiness. To address the issue, this study uses opti...
Saved in:
| Main Authors: | Md Mahmudul Hasan, Jack Phu, Henrietta Wang, Arcot Sowmya, Michael Kalloniatis, Erik Meijering |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-87219-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Predicting visual field global and local parameters from OCT measurements using explainable machine learning
by: Md Mahmudul Hasan, et al.
Published: (2025-02-01) -
Diagnosis of early glaucoma likely combined with high myopia by integrating OCT thickness map and standard automated and Pulsar perimetries
by: Ai-Su Yang, et al.
Published: (2025-04-01) -
Impact of race and ethnicity on glaucoma progression detection by perimetry and optical coherence tomography
by: Luiz A. F. Beniz, et al.
Published: (2024-12-01) -
Current perspectives in tackling glaucoma blindness
by: Shibal Bhartiya, et al.
Published: (2025-03-01) -
Preperimetric glaucoma
by: N. A. Bakunina, et al.
Published: (2021-03-01)