An Integral R-Banded Karyotype Analysis System of Bone Marrow Metaphases Based on Deep Learning

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 clinical application.