1. Detection Of Morphologic Patterns Of Diabetic Macular Edema Using A Deep Learning Approach Based On Optical Coherence Tomography Images.

    Detection Of Morphologic Patterns Of Diabetic Macular Edema Using A Deep Learning Approach Based On Optical Coherence Tomography Images.

    Purpose: To develop a deep learning (DL) model to detect morphologic patterns of diabetic macular edema (DME) based on optical coherence tomography (OCT) images. Methods: In the training set, 12,365 OCT images were extracted from a public dataset and an ophthalmic center. A total of 656 OCT images were extracted from another ophthalmic center for external validation. The presence or absence of three OCT patterns of DME, including diffused retinal thickening (DRT), cystoid macular edema (CME), and serous retinal detachment (SRD) were labeled with 1 or 0, respectively. A DL model was trained to detect three OCT patterns of ...

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