AIMC Topic: Diagnostic Techniques, Ophthalmological

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Deep learning applications in ophthalmology.

Current opinion in ophthalmology May 1, 2018
PURPOSE OF REVIEW: To describe the emerging applications of deep learning in ophthalmology.
Diabetic Retinopathy Glaucoma Machine Learning Humans Photography Tomography, Optical Coherence Posterior Eye Segment Diagnostic Techniques, Ophthalmological Macular Degeneration
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Improved Automated Detection of Diabetic Retinopathy on a Publicly Available Dataset Through Integration of Deep Learning.

Investigative ophthalmology & visual science Oct 1, 2016
PURPOSE: To compare performance of a deep-learning enhanced algorithm for automated detection of diabetic retinopathy (DR), to the previously published performance of that algorithm, the Iowa Detection Program (IDP)-without deep learning components-o...
Diabetic Retinopathy Neural Networks, Computer Algorithms Humans Ophthalmologists ROC Curve Follow-Up Studies Automation Diagnostic Techniques, Ophthalmological Retina Male Middle Aged Diagnosis, Computer-Assisted Retrospective Studies Female
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Evaluation of a Machine-Learning Classifier for Keratoconus Detection Based on Scheimpflug Tomography.

Cornea Jun 1, 2016
PURPOSE: To evaluate the performance of a support vector machine algorithm that automatically and objectively identifies corneal patterns based on a combination of 22 parameters obtained from Pentacam measurements and to compare this method with othe...
Keratoconus Corneal Topography Diagnostic Techniques, Ophthalmological Middle Aged Adolescent Corneal Pachymetry Reproducibility of Results Young Adult Algorithms Sensitivity and Specificity Retrospective Studies Male Adult Female Humans Machine Learning
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Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus.

Journal of cataract and refractive surgery Feb 1, 2016
PURPOSE: To describe the topographic and tomographic characteristics of normal fellow eyes of unilateral keratoconus cases and to evaluate the accuracy of machine learning classifiers in discriminating healthy corneas from the normal fellow corneas.
Retrospective Studies Female Young Adult Adult Reproducibility of Results Humans Machine Learning Keratoconus Corneal Topography Photography Corneal Pachymetry Cornea Diagnostic Techniques, Ophthalmological Middle Aged Male Case-Control Studies ROC Curve
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Artificial Neural Network Approach for Differentiating Open-Angle Glaucoma From Glaucoma Suspect Without a Visual Field Test.

Investigative ophthalmology & visual science Jun 1, 2015
PURPOSE: To increase the effectiveness of treating open-angle glaucoma (OAG), we tried to find a screening method of differentiating OAG from glaucoma suspect (GS) without a visual field (VF) test.
Ocular Hypertension Refractive Errors Visual Acuity Diagnostic Techniques, Ophthalmological Diagnosis, Differential Glaucoma, Open-Angle Male Middle Aged Aged Cross-Sectional Studies Nerve Fibers Glaucoma Adult Intraocular Pressure Female Retinal Ganglion Cells Sensitivity and Specificity Risk Factors Humans Multivariate Analysis Predictive Value of Tests Neural Networks, Computer Tomography, Optical Coherence
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