![]() ![]() The CNN identified the correct chromosome class for 98.8% of chromosomes, which led to a time saving of 42% for the karyotyping workflow. Here, we present a novel laboratory approach to identify chromosomes in cancer cells using a convolutional neural network (CNN). However, their safe and reliable application in diagnostics needs to be evaluated. Artificial intelligence provides novel support tools. The identification and characterization of chromosomes is a challenging process and needs experienced personal. Karyotype analysis has a great impact on the diagnosis, treatment and prognosis in hematologic neoplasms. "Classification of fluorescent R-Band metaphase chromosomes using a convolutional neural network is precise and fast in generating karyograms of hematologic neoplastic cells"īeate Vajen, Siegfried Hänselmann, Friederike Lutterloh, Simon Käfer, Jennifer Espenkötter, Anna Beening, Jochen Bogin, Brigitte Schlegelberger, Gudrun Göhring
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