ArticleJournal of healthcare engineering2018
Comparison of Machine-Learning Classification Models for Glaucoma Management.
Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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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
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- The march to harmonized imaging standards for retinal imaging.Progress in retinal and eye research · 2025Review
- The use of artificial neural networks in studying the progression of glaucoma.Scientific reports · 2024Article
- Characteristics of a Large, Labeled Data Set for the Training of Artificial Intelligence for Glaucoma Screening with Fundus Photographs.Ophthalmology science · 2023Article
- Agreement between Five Experts and the Laguna ONhE Automatic Colourimetric Application Interpreting the Glaucomatous Aspect of the Optic Nerve.Journal of clinical medicine · 2023Article
- Clinical Relevance of Choroidal Thickness in Obese and Healthy Children: A Machine Learning Study.Turkish journal of ophthalmology · 2023Article
- Assessing the external validity of machine learning-based detection of glaucoma.Scientific reports · 2023Article
- Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions.Diagnostics (Basel, Switzerland) · 2022Review
- Integrated multimodal artificial intelligence framework for healthcare applications.NPJ digital medicine · 2022Article
- Development of the Integrated Glaucoma Risk Index.Diagnostics (Basel, Switzerland) · 2022Article
- Glaucoma disease diagnosis with an artificial algae-based deep learning algorithm.Medical & biological engineering & computing · 2022Article
- Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study.Interactive journal of medical research · 2022Article
- Multimodal fusion with deep neural networks for leveraging CT imaging and electronic health record: a case-study in pulmonary embolism detection.Scientific reports · 2020Article
- Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard.Ophthalmology · 2020Article
- Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines.NPJ digital medicine · 2020Review
- A Technical Review of Convolutional Neural Network-Based Mammographic Breast Cancer Diagnosis.Computational and mathematical methods in medicine · 2019Review
- Artificial intelligence on diabetic retinopathy diagnosis: an automatic classification method based on grey level co-occurrence matrix and naive Bayesian model.International journal of ophthalmology · 2019Article
- Big data in visual field testing for glaucoma.Taiwan journal of ophthalmologyReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study develops an objective machine-learning classification model for classifying glaucomatous optic discs and reveals the classificatory criteria to assist in clinical glaucoma management. In this study, 163 glaucoma eyes were labelled with four optic disc types by three glaucoma specialists and then randomly separated into training and test data. All the images of these eyes were captured using optical coherence tomography and laser speckle flowgraphy to quantify the ocular structure and blood-flow-related parameters. A total of 91 parameters were extracted from each eye along with the patients' background information. Machine-learning classifiers, including the neural network (NN), naïve Bayes (NB), support vector machine (SVM), and gradient boosted decision trees (GBDT), were trained to build the classification models, and a hybrid feature selection method that combines minimum redundancy maximum relevance and genetic-algorithm-based feature selection was applied to find the most valid and relevant features for NN, NB, and SVM. A comparison of the performance of the three machine-learning classification models showed that the NN had the best classification performance with a validated accuracy of 87.8% using only nine ocular parameters. These selected quantified parameters enabled the trained NN to classify glaucomatous optic discs with relatively high performance without requiring color fundus images.
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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.