ArticlePloS one2017
Classification of optic disc shape in glaucoma using machine learning based on quantified ocular parameters.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- A scoping review of advancements in machine learning for glaucoma: current trends and future direction.Frontiers in medicine · 2025Article
- Red wavelength-induced photobiomodulation enhances indocyanine green-based anticancer photodynamic therapy.Medical oncology (Northwood, London, England) · 2024Article
- Grad-CAM-Based Investigation into Acute-Stage Fluorescein Angiography Images to Predict Long-Term Visual Prognosis of Branch Retinal Vein Occlusion.Journal of clinical medicine · 2024Article
- Bibliometric analysis of artificial intelligence and optical coherence tomography images: research hotspots and frontiers.International journal of ophthalmology · 2023Article
- Predicting Axial Length From Choroidal Thickness on Optical Coherence Tomography Images With Machine Learning Based Algorithms.Frontiers in medicine · 2022Article
- Development of glaucoma predictive model and risk factors assessment based on supervised models.BioData mining · 2021Article
- Hierarchical deep learning models using transfer learning for disease detection and classification based on small number of medical images.Scientific reports · 2021Article
- Article
- Deep learning classification of early normal-tension glaucoma and glaucoma suspects using Bruch's membrane opening-minimum rim width and RNFL.Scientific reports · 2020Article
- Artificial Intelligence Algorithms to Diagnose Glaucoma and Detect Glaucoma Progression: Translation to Clinical Practice.Translational vision science & technology · 2020Article
- Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard.Ophthalmology · 2020Article
- A machine learning-based algorithm used to estimate the physiological elongation of ocular axial length in myopic children.Eye and vision (London, England) · 2020Article
- Application of machine learning in ophthalmic imaging modalities.Eye and vision (London, England) · 2020Review
- Joint retina segmentation and classification for early glaucoma diagnosis.Biomedical optics express · 2019Article
- Application of artificial intelligence in ophthalmology.International journal of ophthalmology · 2018Review
- Comparison of Machine-Learning Classification Models for Glaucoma Management.Journal of healthcare engineering · 2018Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeThis study aimed to develop a machine learning-based algorithm for objective classification of the optic disc in patients with open-angle glaucoma (OAG), using quantitative parameters obtained from ophthalmic examination instruments.
methodsThis study enrolled 163 eyes of 105 OAG patients (age: 62.3 ± 12.6, mean deviation of Humphrey field analyzer: -8.9 ± 7.5 dB). The eyes were classified into Nicolela's 4 optic disc types by 3 glaucoma specialists. Randomly, 114 eyes were selected for training data and 49 for test data. A neural network (NN) was trained with the training data and evaluated with the test data. We used 91 types of quantitative data, including 7 patient background characteristics, 48 quantified OCT (swept-source OCT; DRI OCT Atlantis, Topcon) values, including optic disc topography and circumpapillary retinal nerve fiber layer thickness (cpRNFLT), and 36 blood flow parameters from laser speckle flowgraphy, to build the machine learning classification model. To extract the important features among 91 parameters, minimum redundancy maximum relevance and a genetic feature selection were used.
resultsThe validated accuracy against test data for the NN was 87.8% (Cohen's Kappa = 0.83). The important features in the NN were horizontal disc angle, spherical equivalent, cup area, age, 6-sector superotemporal cpRNFLT, average cup depth, average nasal rim disc ratio, maximum cup depth, and superior-quadrant cpRNFLT.
conclusionThe proposed machine learning system has proved to be good identifiers for different disc types with high accuracy. Additionally, the calculated confidence levels reported here should be very helpful for OAG care.
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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.