Identification of Incorrect Karyotypes Using Deep Learning

Karyotyping is a vital cytogenetics technique widely applied in prenatal diagnosis and genetic screening. Heavily dependent on the experience of the cytogeneticist and easily affected by the attention, karyotype analysis is a time-consuming and error-prone task, and incorrect karyotypes may result in misdiagnosis conclusions. This paper proposes an effective identification framework for incorrect karyotypes based on deep learning technology. Firstly, a chromosome classifier is trained and utilized to classify chromosome instances in karyotypes performed manually by cytogeneticists. Afterward, when the categories of chromosome instances classified by the classifier are not identical to those categories classified by cytogeneticists, the proposed framework identifies these corresponding karyotypes as unreliable. Finally, the expert team review these unreliable karyotypes and confirmed their correctness. Extensive experiments show that the proposed framework achieves 100% recall and 88.89% F1 score on incorrect karyotypes, which demonstrates the advancement and promising effectiveness of the proposed framework to address the issue of incorrect karyotypes.