The deep learning procedure used here follows the method described in Janowczyk and Madabhushi 2016 a deep learning tutorial with source code is hosted at. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.ĭata Availability: WND-CHARM is open-source and hosted at. Received: JAccepted: JanuPublished: April 3, 2018Ĭopyright: © 2018 Nirschl et al. PLoS ONE 13(4):Įditor: Alison Marsden, Stanford University, UNITED STATES (2018) A deep-learning classifier identifies patients with clinical heart failure using whole-slide images of H&E tissue. Our results suggest that deep learning analytics of EMB can be used to predict cardiac outcome.Ĭitation: Nirschl JJ, Janowczyk A, Peyster EG, Frank R, Margulies KB, Feldman MD, et al. Importantly, the CNN outperformed two expert pathologists by nearly 20%. The CNN was able to identify patients with heart failure or severe pathology with a 99% sensitivity and 94% specificity on the test set, outperforming conventional feature-engineering approaches. In this study, we develop a CNN classifier to detect clinical heart failure from H&E stained whole-slide images from a total of 209 patients, 104 patients were used for training and the remaining 105 patients for independent testing. Deep convolutional neural networks (CNNs) have been successfully applied to detect cancer, diabetic retinopathy, and dermatologic lesions from images. However, manual EMB interpretation has high inter-rater variability. When the cause of heart failure cannot be identified, endomyocardial biopsy (EMB) represents the gold-standard for the evaluation of disease. Over 26 million people worldwide suffer from heart failure annually.
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