PURPOSE: To develop a deep learning (DL) model to detect morphologic patterns of diabetic macular edema (DME) based on optical coherence tomography (OCT) images.
PURPOSE: To develop a reliable algorithm for the automated identification, localization, and volume measurement of exudative manifestations in neovascular age-related macular degeneration (nAMD), including intraretinal (IRF), subretinal fluid (SRF), ...
Der Ophthalmologe : Zeitschrift der Deutschen Ophthalmologischen Gesellschaft
Oct 1, 2020
BACKGROUND: Empirical models have been an integral part of everyday clinical practice in ophthalmology since the introduction of the Sanders-Retzlaff-Kraff (SRK) formula. Recent developments in the field of statistical learning (artificial intelligen...
PURPOSE: To compare area measurements between swept source optical coherence tomography angiography (SSOCTA), fluorescein angiography (FA), and indocyanine green angiography (ICGA) after applying a novel deep-learning-assisted algorithm for accurate ...
IMPORTANCE: Large amounts of optical coherence tomographic (OCT) data of diabetic macular edema (DME) are acquired, but many morphologic features have yet to be identified and quantified.
Asia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Jan 1, 2020
Investigators, scientists, and physicians continue to develop new methods of intraocular lens (IOL) calculation to improve the refractive accuracy after cataract surgery. To gain more accurate prediction of IOL power, vergence lens formulas have inco...
PURPOSE: In diabetic patients presenting with macular edema (ME) shortly after cataract surgery, identifying the underlying pathology can be challenging and influence management. Our aim was to develop a simple clinical classifier able to confirm a d...
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