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- W2766648608 abstract "Are pathologists obsolete? Although the current answer is most certainly no, this question will be asked with increasing intensity as technological advances in clinical diagnostics, such as machine learning and artificial intelligence, prove to be as effective or even more accurate than the judgments of human experts.One area of pathology experiencing rapid expansion in the diagnostic power of computer technology is automated digital image analysis (DIA)2 (1). A recent study has suggested that automated scoring of biomarkers in breast cancer (Ki67, ER, PR, and HER2) by DIA can predict a molecular subtype of cancer with higher sensitivity and specificity than manual scoring by board-certified pathologists (2). The prospect of using such a tool is not restricted to research, as analyzers have been approved by the Food and Drug Administration (FDA) to score these biomarkers (3, 4).Clinical pathology has also become a fertile ground for DIA in the form of an FDA-approved analyzer for peripheral blood smear review (5). This analyzer identifies, classifies, and quantifies leukocytes by morphologic type using a machine learning approach to assist the technologist or pathologist performing manual differential counts. However, there currently is no similar tool for the morphologic characterization of erythrocytes (RBCs) that has been approved by the FDA.In this issue of Clinical Chemistry , Durant et al. present a novel DIA tool that uses very deep convolutional neural networks (CNNs) to predict the morphology of individual RBCs for the identification of significant abnormal populations that may aid in the diagnosis of hematologic and nonhematologic disease …" @default.
- W2766648608 created "2017-11-10" @default.
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- W2766648608 date "2017-12-01" @default.
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- W2766648608 title "Deep Learning Makes Its Way to the Clinical Laboratory" @default.
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- W2766648608 doi "https://doi.org/10.1373/clinchem.2017.280768" @default.
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