Context.—

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.

Objective.—

To evaluate the performance of the internal models and the entire karyotype analysis system for R-banded bone marrow metaphase.

Design.—

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.

Results.—

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.

Conclusions.—

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.

This content is only available as a PDF.

Author notes

Supplemental digital content is available for this article. See text for hyperlink.

Wang, Xia, J. Yang, and B. Chen contributed equally to this work, with Wang and Xia considered co-first authors.

This study was supported by the National Natural Science Foundation of China Grant (81670137), Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support grant (20152501), State Key Laboratory of Medical Genomics Support Grant (201802), and Sjtu Trans-med Awards Research (2022102).

Competing Interests

The authors have no relevant financial interest in the products or companies described in this article.

Supplementary data