ArticleScientific reports2022
Glaucoma diagnosis using multi-feature analysis and a deep learning technique.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Hypertuned boosting approach with Local Binary Pattern and Pivot Distribution Count method feature extractor for glaucoma identification.Scientific reports · 2026Article
- OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.Scientific reports · 2026Article
- Artificial intelligence-based apps for screening and diagnosing diabetic retinopathy and common ocular disorders.World journal of methodology · 2025Review
- Generative Artificial Intelligence for Retinal Image Translation to Improve Glaucoma Screening With Deep Learning.Translational vision science & technology · 2025Article
- Applications of machine learning in glaucoma diagnosis based on tabular data: a systematic review.BMC biomedical engineering · 2025Article
- Deep Learning-Based Glaucoma Detection Using Clinical Notes: A Comparative Study of Long Short-Term Memory and Convolutional Neural Network Models.Diagnostics (Basel, Switzerland) · 2025Article
- Lights and Shadows on Artificial Intelligence in Glaucoma: Transforming Screening, Monitoring, and Prognosis.Journal of clinical medicine · 2025Review
- Validation of a Novel Suprathreshold Strategy for Screening Visual Function.Journal of clinical medicine · 2025Article
- Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis.Biomedicines · 2025Review
- Diagnostic Decision-Making Variability Between Novice and Expert Optometrists for Glaucoma: Comparative Analysis to Inform AI System Design.JMIR medical informatics · 2025Article
- OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.Scientific reports · 2025Article
- CAD-EYE: An Automated System for Multi-Eye Disease Classification Using Feature Fusion with Deep Learning Models and Fluorescence Imaging for Enhanced Interpretability.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence and Advanced Technology in Glaucoma: A Review.Journal of personalized medicine · 2024Review
- Assessing the efficacy of 2D and 3D CNN algorithms in OCT-based glaucoma detection.Scientific reports · 2024Article
- CSDNet: A Novel Deep Learning Framework for Improved Cataract State Detection.Diagnostics (Basel, Switzerland) · 2024Article
- Deep ocular tumor classification model using cuckoo search algorithm and Caputo fractional gradient descent.PeerJ. Computer science · 2024Article
- Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis.International journal of ophthalmology · 2024Article
- Review
- Explainable AI for Retinoblastoma Diagnosis: Interpreting Deep Learning Models with LIME and SHAP.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this study, we aimed to facilitate the current diagnostic assessment of glaucoma by analyzing multiple features and introducing a new cross-sectional optic nerve head (ONH) feature from optical coherence tomography (OCT) images. The data (n = 100 for both glaucoma and control) were collected based on structural, functional, demographic and risk factors. The features were statistically analyzed, and the most significant four features were used to train machine learning (ML) algorithms. Two ML algorithms: deep learning (DL) and logistic regression (LR) were compared in terms of the classification accuracy for automated glaucoma detection. The performance of the ML models was evaluated on unseen test data, n = 55. An image segmentation pilot study was then performed on cross-sectional OCT scans. The ONH cup area was extracted, analyzed, and a new DL model was trained for glaucoma prediction. The DL model was estimated using five-fold cross-validation and compared with two pre-trained models. The DL model trained from the optimal features achieved significantly higher diagnostic performance (area under the receiver operating characteristic curve (AUC) 0.98 and accuracy of 97% on validation data and 96% on test data) compared to previous studies for automated glaucoma detection. The second DL model used in the pilot study also showed promising outcomes (AUC 0.99 and accuracy of 98.6%) to detect glaucoma compared to two pre-trained models. In combination, the result of the two studies strongly suggests the four features and the cross-sectional ONH cup area trained using deep learning have a great potential for use as an initial screening tool for glaucoma which will assist clinicians in making a precise decision.
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