Department of Human Genetics, Hannover Medical School, Hannover 30625, Germany.
MetaSystems Hard and Software GmbH, Robert-Bosch-Str. 6, Altlussheim 68804, Germany.
Cancer Genet. 2022 Jan;260-261:23-29. doi: 10.1016/j.cancergen.2021.11.005. Epub 2021 Nov 20.
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.
核型分析对血液系统肿瘤的诊断、治疗和预后有重要影响。识别和描述染色体是一个具有挑战性的过程，需要有经验的专业人员。人工智能提供了新的支持工具。然而，它们在诊断中的安全可靠应用需要进行评估。在这里，我们提出了一种使用卷积神经网络（CNN）识别癌细胞中染色体的新实验室方法。CNN 正确识别了 98.8%的染色体的类别，使核型分析工作流程节省了 42%的时间。这些结果表明，CNN 在血液系统肿瘤的染色体分类中有潜在的应用价值。本研究为自动核型分析平台的开发做出了贡献。
