ArticleDiagnostics (Basel, Switzerland)2021
Explainable Machine Learning Model for Glaucoma Diagnosis and Its Interpretation.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
30 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Explainable Multimodal OCT and OCTA Analysis Reveals Structural and Vascular Features of Mild Glaucoma Across Ethnicities.Investigative ophthalmology & visual science · 2026Observational
- OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.Scientific reports · 2026Article
- Multiple attention based deep multimodal fusion network for glaucoma and neurodegenerative disease diagnosis.Scientific reports · 2026Article
- Article
- A Machine Learning Guided System for Therapeutic Decision-Making in Intra-Arterial Therapy of Hepatocellular Carcinoma.Journal of hepatocellular carcinoma · 2026Article
- Predicting Intraocular Pressure From Glaucoma Patients Receiving Medication Treatment Using Explainable Machine Learning.BioMed research international · 2026Article
- Development and Evaluation of an Artificial Intelligence Model to Set Target IOP for Glaucoma.American journal of ophthalmology · 2025Article
- Eye-XAI: an explainable artificial intelligence approach for eye disease detection using symptom analysis.BMC medical informatics and decision making · 2025Article
- Predicting Postoperative Delirium in Older Patients Before Elective Surgery: Multicenter Retrospective Cohort Study.JMIR aging · 2025Article
- Analyzing Retinal Vessel Morphology in MS Using Interpretable AI on Deep Learning-Segmented IR-SLO Images.Bioengineering (Basel, Switzerland) · 2025Article
- Applications of machine learning in glaucoma diagnosis based on tabular data: a systematic review.BMC biomedical engineering · 2025Article
- Machine learning technology in the classification of glaucoma severity using fundus photographs.Scientific reports · 2025Article
- OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.Scientific reports · 2025Article
- Artificial Intelligence in Predicting Ocular Hypertension After Descemet Membrane Endothelial Keratoplasty.Investigative ophthalmology & visual science · 2025Article
- Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence.Life (Basel, Switzerland) · 2024Review
- Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis.International journal of ophthalmology · 2024Article
- Interventional Glaucoma: Improving the Patient-Provider Educational Exchange.Clinical ophthalmology (Auckland, N.Z.) · 2024Article
- Artificial intelligence and glaucoma: a lucid and comprehensive review.Frontiers in medicine · 2024Review
- Assessing Machine Learning Models for Predicting Age with Intracranial Vessel Tortuosity and Thickness Information.Brain sciences · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
The aim is to develop a machine learning prediction model for the diagnosis of glaucoma and an explanation system for a specific prediction. Clinical data of the patients based on a visual field test, a retinal nerve fiber layer optical coherence tomography (RNFL OCT) test, a general examination including an intraocular pressure (IOP) measurement, and fundus photography were provided for the feature selection process. Five selected features (variables) were used to develop a machine learning prediction model. The support vector machine, C5.0, random forest, and XGboost algorithms were tested for the prediction model. The performance of the prediction models was tested with 10-fold cross-validation. Statistical charts, such as gauge, radar, and Shapley Additive Explanations (SHAP), were used to explain the prediction case. All four models achieved similarly high diagnostic performance, with accuracy values ranging from 0.903 to 0.947. The XGboost model is the best model with an accuracy of 0.947, sensitivity of 0.941, specificity of 0.950, and AUC of 0.945. Three statistical charts were established to explain the prediction based on the characteristics of the XGboost model. Higher diagnostic performance was achieved with the XGboost model. These three statistical charts can help us understand why the machine learning model produces a specific prediction result. This may be the first attempt to apply "explainable artificial intelligence" to eye disease diagnosis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.