AIMC Topic: Macular Edema

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Deep Learning Predicts OCT Measures of Diabetic Macular Thickening From Color Fundus Photographs.

Investigative ophthalmology & visual science Mar 1, 2019
PURPOSE: To develop deep learning (DL) models for the automatic detection of optical coherence tomography (OCT) measures of diabetic macular thickening (MT) from color fundus photographs (CFPs).
Humans Diabetic Retinopathy Female Sensitivity and Specificity Neural Networks, Computer Retrospective Studies Image Interpretation, Computer-Assisted False Positive Reactions Macular Edema Vascular Endothelial Growth Factor A Macula Lutea Ranibizumab Photography Intravitreal Injections Diagnostic Techniques, Ophthalmological Randomized Controlled Trials as Topic Predictive Value of Tests Tomography, Optical Coherence Fundus Oculi Deep Learning Angiogenesis Inhibitors Male Middle Aged
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Fusing Results of Several Deep Learning Architectures for Automatic Classification of Normal and Diabetic Macular Edema in Optical Coherence Tomography.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2018
Diabetic Macular Edema (DME) is a severe eye disease that can lead to irreversible blindness if it is left untreated. DME diagnosis still relies on manual evaluation from opthalmologists, thus the process is time consuming and diagnosis may be subjec...
Macular Edema Diabetes Complications Algorithms Deep Learning Principal Component Analysis Tomography, Optical Coherence Humans Neural Networks, Computer
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Diabetic macular edema grading in retinal images using vector quantization and semi-supervised learning.

Technology and health care : official journal of the European Society for Engineering and Medicine Jan 1, 2018
BACKGROUND: Diabetic macular edema (DME) is one of the severe complication of diabetic retinopathy causing severe vision loss and leads to blindness in severe cases if left untreated.
Image Interpretation, Computer-Assisted Exudates and Transudates Sensitivity and Specificity Humans Severity of Illness Index Supervised Machine Learning Diabetic Retinopathy Macular Edema
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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.

JAMA Dec 13, 2016
IMPORTANCE: Deep learning is a family of computational methods that allow an algorithm to program itself by learning from a large set of examples that demonstrate the desired behavior, removing the need to specify rules explicitly. Application of the...
Middle Aged Fundus Oculi Neural Networks, Computer Macular Edema Machine Learning Observer Variation Humans Photography Sensitivity and Specificity Algorithms Diabetic Retinopathy Ophthalmologists Female Male
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Anatomical and visual outcome of intravitreal bevacizumab (Avastin) in patients with diabetic macular edema.

Nepalese journal of ophthalmology : a biannual peer-reviewed academic journal of the Nepal Ophthalmic Society : NEPJOPH Jan 1, 2016
BACKGROUND: Intravitreal bevacizumab has been shown to be an effective treatment of diabetic macular edema.
Visual Acuity Angiogenesis Inhibitors Fluorescein Angiography Corneal Topography Intravitreal Injections Treatment Outcome Humans Bevacizumab Tomography, Optical Coherence Recurrence Diabetic Retinopathy Macular Edema
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