1. An Integral R-Banded Karyotype Analysis System of Bone Marrow Metaphases Based on Deep Learning.
- Author
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Wang J, Xia C, Fan Y, Jiang L, Yang G, Chen Z, Yang J, and Chen B
- Subjects
- Humans, Chromosome Banding methods, Bone Marrow pathology, Hematologic Neoplasms genetics, Hematologic Neoplasms diagnosis, Hematologic Neoplasms pathology, Deep Learning, Metaphase, Karyotyping methods
- Abstract
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 a clinical application., (© 2024 College of American Pathologists.)
- Published
- 2024
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