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Clinical Ophthalmology: A Systematic Approach

TLDR
Ocular side-effects of systemic medication 21.1.
Abstract
1. Eyelids 2. Lacrimal Drainage System 3. Orbit 4. Dry Eye Disorders 5. Conjunctiva 6. Cornea 7. Corneal and Refractive Surgery 8. Episclera and Sclera 9. Lens 10. Glaucoma 11. Uveitis 12. Ocular Tumours 13. Retinal Vascular Disease 14. Acquired Macular Disorders 15. Hereditary Fundus Dystrophies 16. Retinal Detachment 17. Vitreous Opacities 18. Strabismus 19. Neuro-ophthalmology 20. Ocular side-effects of systemic medication 21. Trauma Index

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Predicting bacterial cause in infectious conjunctivitis: cohort study on informativeness of combinations of signs and symptoms

TL;DR: A bacterial origin of complaints indicative of acute infectious conjunctivitis can be made much more likely or unlikely by the answers to three simple questions posed during clinical history taking.
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Late in-the-bag intraocular lens dislocation requiring explantation: risk factors and outcomes

TL;DR: High myopia was the main risk factor for late in-the-bag intraocular lens (IOL) dislocation and surgical treatment significantly improved the CDVA in the authors' sample and was associated with a low complication rate.
Journal ArticleDOI

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

TL;DR: An automated method for the detection of new vessels from retinal images is presented based on a dual classification approach that combines a support vector machine (SVM) classifier with a genetic algorithm based feature selection approach.
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Central Serous Chorioretinopathy

TL;DR: An overview of central serous chorioretinopathy's epidemiology, the current understanding of its pathogenesis as well as systemic and ocular risk factors are given, particularly in the light of a better understanding of corticosteroids and their receptors involved in its pathogenic.
Journal ArticleDOI

Application of deep learning for retinal image analysis: A review

TL;DR: A review of deep learning techniques applied to 2-D fundus and 3-D Optical Coherence Tomography retinal images for automated classification of retinal landmarks, pathology, and disease classification.
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