ArticleInternational ophthalmology2019
Comparison between support vector machine and deep learning, machine-learning technologies for detecting epiretinal membrane using 3D-OCT.
Article in International ophthalmology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Optical Coherence Tomography Characteristics Between Idiopathic Epiretinal Membranes and Secondary Epiretinal Membranes due to Peripheral Retinal Hole.Journal of ophthalmology · 2025Article
- Deactivation and collective phasic muscular tuning for pointing direction: Insights from machine learning.Heliyon · 2024Article
- Clinical evaluation of deep learning systems for assisting in the diagnosis of the epiretinal membrane grade in general ophthalmologists.Eye (London, England) · 2024Article
- Development of a generative deep learning model to improve epiretinal membrane detection in fundus photography.BMC medical informatics and decision making · 2024Article
- Screening of idiopathic epiretinal membrane using fundus images combined with blood oxygen saturation and vascular morphological features.International ophthalmology · 2023Article
- iERM: An Interpretable Deep Learning System to Classify Epiretinal Membrane for Different Optical Coherence Tomography Devices: A Multi-Center Analysis.Journal of clinical medicine · 2023Article
- Prediction of postoperative visual acuity after vitrectomy for macular hole using deep learning-based artificial intelligence.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Article
- Developing an iOS application that uses machine learning for the automated diagnosis of blepharoptosis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2022Observational
- Comparisons of Glutamate in the Brains of Alzheimer's Disease Mice Under Chemical Exchange Saturation Transfer Imaging Based on Machine Learning Analysis.Frontiers in neuroscience · 2022Article
- Artificial intelligence-based detection of epimacular membrane from color fundus photographs.Scientific reports · 2021Article
- Automatic screening of tear meniscus from lacrimal duct obstructions using anterior segment optical coherence tomography images by deep learning.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2021Article
- Machine learning and deep learning to predict mortality in patients with spontaneous coronary artery dissection.Scientific reports · 2021Article
- Prediction of age and brachial-ankle pulse-wave velocity using ultra-wide-field pseudo-color images by deep learning.Scientific reports · 2020Article
- Article
- Severity Classification of Conjunctival Hyperaemia by Deep Neural Network Ensembles.Journal of ophthalmology · 2019Article
- The Role of Artificial Intelligence in Epiretinal Membrane Care: A Scoping Review.Ophthalmology scienceReview
- Understanding required to consider artificial intelligence applications to the field of ophthalmology.Taiwan journal of ophthalmologyArticle
- The use of artificial intelligence in detecting papilledema from fundus photographs.Taiwan journal of ophthalmologyReview
- Portable hardware & software technologies for addressing ophthalmic health disparities: A systematic review.Digital healthReview
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeIn this study, we compared deep learning (DL) with support vector machine (SVM), both of which use three-dimensional optical coherence tomography (3D-OCT) images for detecting epiretinal membrane (ERM).
methodsIn total, 529 3D-OCT images from the Tsukazaki hospital ophthalmology database (184 non-ERM subjects and 205 ERM patients) were assessed; 80% of the images were divided for training, and 20% for test as follows: 423 training (non-ERM 245, ERM 178) and 106 test (non-ERM 59, ERM 47) images. Using the 423 training images, a model was created with deep convolutional neural network and SVM, and the test data were evaluated.
resultsThe DL model's sensitivity was 97.6% [95% confidence interval (CI), 87.7-99.9%] and specificity was 98.0% (95% CI, 89.7-99.9%), and the area under the curve (AUC) was 0.993 (95% CI, 0.993-0.994). In contrast, the SVM model's sensitivity was 97.6% (95% CI, 87.7-99.9%), specificity was 94.2% (95% CI, 84.0-98.7%), and AUC was 0.988 (95% CI, 0.987-0.988).
conclusionDL model is better than SVM model in detecting ERM by using 3D-OCT images.
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