• Mashup Score: 3

    Rare disease diagnosis is challenging in medical image-based artificial intelligence due to a natural class imbalance in datasets, leading to biased prediction models. Inherited retinal diseases (IRDs) are a research domain that particularly faces this issue. This study investigates the applicability of synthetic data in improving AI-enabled diagnosis of IRDs using Generative Adversarial Networks…

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    • New study investigates the impact of synthetic data augmentation using generative adversarial networks in #deeplearning-enabled diagnosis of inherited retinal diseases from fundus autofluorescence images. https://t.co/gJaK7NyLhT https://t.co/ED6c0nAgi4

  • Mashup Score: 0

    To evaluate the diagnostic accuracy of machine learning (ML) techniques applied to radiomic features extracted from OCT and OCT angiography (OCTA) images for diabetes mellitus (DM), diabetic retinopathy (DR), and referable DR (R-DR) diagnosis.

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    • Study demonstrates that radiomic features extracted from OCTA images permit the identification of #diabeticretinopathy patients with #machinelearning classifiers & provides recommendations about specific scanning protocols for best model performances. https://t.co/ayE1YET5dE https://t.co/1Ijk6GjBPa