ArticleScientific reports2020
Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it, 30 citations in OpenAlex.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- Machine learning technology in the classification of glaucoma severity using fundus photographs.Scientific reports · 2025Article
- Interpretable Machine Learning Predictions of Bruch's Membrane Opening-Minimum Rim Width Using Retinal Nerve Fiber Layer Values and Visual Field Global Indexes.Bioengineering (Basel, Switzerland) · 2025Article
- A hybrid multi model artificial intelligence approach for glaucoma screening using fundus images.NPJ digital medicine · 2025Article
- Automated Classification of Physiologic, Glaucomatous, and Glaucoma-Suspected Optic Discs Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Deep learning visual field global index prediction with optical coherence tomography parameters in glaucoma patients.Scientific reports · 2023Article
- Identification of Glaucoma in Diabetics Using the Laguna-ONhE Colourimetric Method and OCT Spectralis.Journal of clinical medicine · 2023Article
- Application of a deep learning system in glaucoma screening and further classification with colour fundus photographs: a case control study.BMC ophthalmology · 2022Article
- Real-Time Risk Score for Glaucoma Mass Screening by Spectral Domain Optical Coherence Tomography: Development and Validation.Translational vision science & technology · 2022Article
- Glaucoma Detection Using Support Vector Machine Method Based on Spectralis OCT.Diagnostics (Basel, Switzerland) · 2022Article
- Deep learning classification of early normal-tension glaucoma and glaucoma suspect eyes using Bruch's membrane opening-based disc photography.Frontiers in medicine · 2022Article
- Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT.Diagnostics (Basel, Switzerland) · 2021Article
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Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We aimed to classify early normal-tension glaucoma (NTG) and glaucoma suspect (GS) using Bruch's membrane opening-minimum rim width (BMO-MRW), peripapillary retinal nerve fiber layer (RNFL), and the color classification of RNFL based on a deep-learning model. Discriminating early-stage glaucoma and GS is challenging and a deep-learning model may be helpful to clinicians. NTG accounts for an average 77% of open-angle glaucoma in Asians. BMO-MRW is a new structural parameter that has advantages in assessing neuroretinal rim tissue more accurately than conventional parameters. A dataset consisted of 229 eyes out of 277 GS and 168 eyes of 285 patients with early NTG. A deep-learning algorithm was developed to discriminate between GS and early NTG using a training set, and its accuracy was validated in the testing dataset using the area under the curve (AUC) of the receiver operating characteristic curve (ROC). The deep neural network model (DNN) achieved highest diagnostic performance, with an AUC of 0.966 (95%confidence interval 0.929-1.000) in classifying either GS or early NTG, while AUCs of 0.927-0.947 were obtained by other machine-learning models. The performance of the DNN model considering all three OCT-based parameters was the highest (AUC 0.966) compared to the combinations of just two parameters. As a single parameter, BMO-MRW (0.959) performed better than RNFL alone (0.914).
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