ArticleDiagnostics (Basel, Switzerland)2021
Comparison of Different Machine Learning Classifiers for Glaucoma Diagnosis Based on Spectralis OCT.
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 26 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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
26 citing papers in PubMed, 2 syntheses or guidelines pooled it, 45 citations in OpenAlex.
- AI for glaucoma, Are we reporting well? a systematic literature review of DECIDE-AI checklist adherence.Eye (London, England) · 2025Pooled it
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- OCT-based optic neuropathy diagnosis using explainable and privacy-preserving machine learning.Scientific reports · 2026Article
- The Comparison of Insulin Resistance Between Normal and Early Menopause Women Younger than Fifty Years Old by Machine Learning Methods.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction.International journal of molecular sciences · 2025Review
- PyGlaucoMetrics: A Stacked Weight-Based Machine Learning Approach for Glaucoma Detection Using Visual Field Data.Medicina (Kaunas, Lithuania) · 2025Article
- Predicting visual field global and local parameters from OCT measurements using explainable machine learning.Scientific reports · 2025Article
- OCT-based diagnosis of glaucoma and glaucoma stages using explainable machine learning.Scientific reports · 2025Article
- Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma.Scientific reports · 2024Article
- Artificial intelligence for the detection of glaucoma with SD-OCT images: a systematic review and Meta-analysis.International journal of ophthalmology · 2024Article
- Using Machine Learning to Identify the Relationships between Demographic, Biochemical, and Lifestyle Parameters and Plasma Vitamin D Concentration in Healthy Premenopausal Chinese Women.Life (Basel, Switzerland) · 2023Article
- Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes.World journal of clinical cases · 2023Article
- Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.Journal of the Chinese Medical Association : JCMA · 2023Article
- Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese.World journal of clinical cases · 2023Article
- Relationships of Macular Functional Impairment With Structural and Vascular Changes According to Glaucoma Severity.Investigative ophthalmology & visual science · 2023Article
- Machine Learning Predictive Models for Evaluating Risk Factors Affecting Sperm Count: Predictions Based on Health Screening Indicators.Journal of clinical medicine · 2023Article
- Integrated Machine Learning Decision Tree Model for Risk Evaluation in Patients with Non-Valvular Atrial Fibrillation When Taking Different Doses of Dabigatran.International journal of environmental research and public health · 2023Article
- Classification Algorithms Used in Predicting Glaucoma Progression.Healthcare (Basel, Switzerland) · 2022Article
- Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes.Diagnostics (Basel, Switzerland) · 2022Article
- Decision Trees for Glaucoma Screening Based on the Asymmetry of the Retinal Nerve Fiber Layer in Optical Coherence Tomography.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Early detection is important in glaucoma management. By using optical coherence tomography (OCT), the subtle structural changes caused by glaucoma can be detected. Though OCT provided abundant parameters for comprehensive information, clinicians may be confused once the results conflict. Machine learning classifiers (MLCs) are good tools for considering numerous parameters and generating reliable diagnoses in glaucoma practice. Here we aim to compare different MLCs based on Spectralis OCT parameters, including circumpapillary retinal nerve fiber layer (cRNFL) thickness, Bruch's membrane opening-minimum rim width (BMO-MRW), Early Treatment Diabetes Retinopathy Study (ETDRS) macular thickness, and posterior pole asymmetry analysis (PPAA), in discriminating normal from glaucomatous eyes. Five MLCs were proposed, namely conditional inference trees (CIT), logistic model tree (LMT), C5.0 decision tree, random forest (RF), and extreme gradient boosting (XGBoost). Logistic regression (LGR) was used as a benchmark for comparison. RF was shown to be the best model. Ganglion cell layer measurements were the most important predictors in early glaucoma detection and cRNFL measurements were more important as the glaucoma severity increased. The global, temporal, inferior, superotemporal, and inferotemporal sites were relatively influential locations among all parameters. Clinicians should cautiously integrate the Spectralis OCT results into the entire clinical picture when diagnosing glaucoma.
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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.