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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Machine Learning To Quantitate Neutrophil Netosis, Laila Elsherif, Noah Sciaky, Carrington A. Metts, Md. Modasshir, Ioannis Rekleitis, Christine A. Burris, Joshua A. Walker, Nadeem Ramadan, Tina M. Leisner, Stephen P. Holly, Martis W. Cowles, Kenneth I. Ataga, Joshua N. Cooper, Leslie V. Parise
Machine Learning To Quantitate Neutrophil Netosis, Laila Elsherif, Noah Sciaky, Carrington A. Metts, Md. Modasshir, Ioannis Rekleitis, Christine A. Burris, Joshua A. Walker, Nadeem Ramadan, Tina M. Leisner, Stephen P. Holly, Martis W. Cowles, Kenneth I. Ataga, Joshua N. Cooper, Leslie V. Parise
Faculty Publications
We introduce machine learning (ML) to perform classifcation and quantitation of images of nuclei from human blood neutrophils. Here we assessed the use of convolutional neural networks (CNNs) using free, open source software to accurately quantitate neutrophil NETosis, a recently discovered process involved in multiple human diseases. CNNs achieved >94% in performance accuracy in diferentiating NETotic from non-NETotic cells and vastly facilitated dose-response analysis and screening of the NETotic response in neutrophils from patients. Using only features learned from nuclear morphology, CNNs can distinguish between NETosis and necrosis and between distinct NETosis signaling pathways, making them a precise tool for …