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Background: Diabetes confers an increased risk of adverse cardiovascular and renal events. We wanted to determine the long-term renal effects of empagliflozin, an analysis that was a prespecified component of the secondary microvascular outcome of that trial. Methods: We randomly assigned patients with type 2 diabetes and an estimated glomerular filtration rate of at least 30 ml per minute per 1. Prespecified renal outcomes included incident or worsening nephropathy progression to macroalbuminuria, doubling of the serum creatinine level, initiation of renal-replacement therapy, or death from renal disease and incident albuminuria.
Results: Incident or worsening nephropathy occurred in of patients
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 these methods to medical imaging requires further assessment and validation. Objective: To apply deep learning to create an algorithm for automated detection of diabetic retinopathy and diabetic macular edema in retinal fundus photographs.
Design and setting: A specific type of neural network optimized for image classification called a deep convolutional neural network was trained using a retrospective development data set of retinal images, which were graded 3 to 7 times for diabetic retinopathy, diabetic macular edema, and image gradability by a panel of 54 US licensed ophthalmologists and ophthalmology senior residents between May and December The resultant algorithm was validated in January and February using 2 separate data sets, both graded by at least 7 US board-certified ophthalmologists with high intragrader consistency.
Exposure: Deep learning-trained algorithm. Main outcomes and measures: The sensitivity and specificity of the algorithm for detecting referable diabetic retinopathy RDR , defined as moderate and worse diabetic retinopathy, referable diabetic macular edema, or both, were generated based on the reference standard of the majority decision of the ophthalmologist panel.
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