1. Classification of fluorescent R-Band metaphase chromosomes using a convolutional neural network is precise and fast in generating karyograms of hematologic neoplastic cells.
- Author
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Vajen, Beate, Hänselmann, Siegfried, Lutterloh, Friederike, Käfer, Simon, Espenkötter, Jennifer, Beening, Anna, Bogin, Jochen, Schlegelberger, Brigitte, and Göhring, Gudrun
- Subjects
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CONVOLUTIONAL neural networks , *CHROMOSOMES , *HEMATOLOGIC malignancies , *ARTIFICIAL intelligence - Abstract
• Karyotype analysis has a great impact on the diagnosis, treatment and prognosis in hematologic neoplasms. • Identification and characterization of chromosomes is a challenging process and needs experienced personal. • The presented novel laboratory approach using a convolutional neural network (CNN) saves time and reduces mistakes in the karyotyping workflow. Karyotype analysis has a great impact on the diagnosis, treatment and prognosis in hematologic neoplasms. The identification and characterization of chromosomes is a challenging process and needs experienced personal. Artificial intelligence provides novel support tools. However, their safe and reliable application in diagnostics needs to be evaluated. Here, we present a novel laboratory approach to identify chromosomes in cancer cells using a convolutional neural network (CNN). The CNN identified the correct chromosome class for 98.8% of chromosomes, which led to a time saving of 42% for the karyotyping workflow. These results demonstrate that the CNN has potential application value in chromosome classification of hematologic neoplasms. This study contributes to the development of an automatic karyotyping platform. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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