传统的核型分析可提供全面的细胞遗传学信息，在血液肿瘤的诊断和风险分层中发挥着重要作用。这种方法的主要局限性包括周转时间长和费力。因此，我们开发了一套基于深度学习的完整的骨髓中期R带核型分析系统。评估R带骨髓中期内部模型和整个核型分析系统的性能。总共4442套收集R带正常骨髓中期和核型图。因此，针对核型分析的不同分析阶段（包括去噪、分割、分类和极性识别）开发了 4 个基于深度学习的模型，并将其集成为 R 带骨髓核型分析系统。对每个模型进行五倍交叉验证。整个系统通过自动和半自动工作流程两种策略来实现。使用 885 个中期相的测试集来评估整个系统。去噪模型实现了中期相采集的交集比联合 (IoU) 为 99.20%，Dice 相似系数 (DSC) 为 99.58%。该分割模型的染色体分割 IoU 为 91.95%，DSC 为 95.79%。分割、分类和极性识别模型的准确率分别为96.77%、98.77%和99.93%。整个系统在自动策略下的准确率达到了93.33%，在半自动策略下的准确率达到了99.06%。无论是内部模型还是整个系统的性能都令人满意。这种基于深度学习的核型分析系统具有临床应用潜力。© 2023 美国病理学家学院。

Conventional karyotype analysis, which provides comprehensive cytogenetic information, plays a significant role in the diagnosis and risk stratification of hematologic neoplasms. The main limitations of this approach include long turnaround time and laboriousness. Therefore, we developed an integral R-banded karyotype analysis system for bone marrow metaphases, based on deep learning.To evaluate the performance of the internal models and the entire karyotype analysis system for R-banded bone marrow metaphase.A total of 4442 sets of R-banded normal bone marrow metaphases and karyograms were collected. Accordingly, 4 deep learning-based models for different analytic stages of karyotyping, including denoising, segmentation, classification, and polarity recognition, were developed and integrated as an R-banded bone marrow karyotype analysis system. Five-fold cross validation was performed on each model. The whole system was implemented by 2 strategies of automatic and semiautomatic workflows. A test set of 885 metaphases was used to assess the entire system.The denoising model achieved an intersection-over-union (IoU) of 99.20% and a Dice similarity coefficient (DSC) of 99.58% for metaphase acquisition. The segmentation model achieved an IoU of 91.95% and a DSC of 95.79% for chromosome segmentation. The accuracies of the segmentation, classification, and polarity recognition models were 96.77%, 98.77%, and 99.93%, respectively. The whole system achieved an accuracy of 93.33% with the automatic strategy and an accuracy of 99.06% with the semiautomatic strategy.The performance of both the internal models and the entire system is desirable. This deep learning-based karyotype analysis system has potential in clinical application.© 2023 College of American Pathologists.