Application of Artificial Intelligence-assisted Chromosome Karyotyping Analysis in Prenatal Diagnosis

Abstract

Background Chromosomal abnormalities are one of the common causes of birth defects, and karyotype analysis is still an important method for prenatal diagnosis of chromosomal abnormalities as well as an effective way to prevent and control birth defects. However, karyotype analysis, especially chromosomal image segmentation and classification mainly depends on manual work at present, which is laborious and time-consuming. As an emerging approach to karyotype analysis, it is of great significance to investigate the application value of artificial intelligence (AI) in prenatal chromosomal karyotype diagnosis. Objective To investigate the application effect and clinical value of AI in prenatal karyotype diagnosis. Methods A total of 1 000 pregnant women who received interventional prenatal diagnosis and karyotype analysis of amniotic fluid cells in the department of medical genetics and prenatal diagnosis of Wuxi Maternity and Child Health Care Hospital between 2020 and 2022 were selected as the study subjects. The karyotype analysis of all cases was performed using two-line mode, the results of the AI reading were reviewed by one geneticist in the first line, and another geneticist analyzed the karyotypes by Ikaros karyotype analysis workstation in the second line, the diagnostic results and time were recorded respectively. The final diagnosis of the samples were based on the manual review of the first line and the manual reading of the second line. Results Among the 1 000 amniotic fluid samples, 735 cases were diagnosed as normal karyotype, 233 cases as aneuploidy, 0 case as structural abnormality and 32 cases as mosaicism by AI. The numbers of normal karyotype, aneuploidy, structural abnormality and mosaicism assessed by AI-assisted geneticist were 689, 233, 45 and 33, which were completely consistent with those evaluated by geneticist using Ikaros system. Compared with AI-assisted geneticist, AI-based diagnosis had strong consistency (Kappa=0.895, 95%CI=0.866-0.924, P<0.01). The diagnostic accuracy, sensitivity and positive predictive value of AI-based diagnosis was 95.4%, 95.4% and 100.0%, respectively, among which the normal karyotype, aneuploidy, structural abnormality and mosaicism were detected with a sensitivity of 100.0%, 100.0%, 0 and 97.0%, and the positive predictive value of 100.0%, 100.0%, 0 and 100.0%. The average diagnostic time of AI was shorter than that of AI-assisted geneticist and Ikaros-assisted geneticist (P<0.001), and AI-assisted geneticist took less time on average to diagnose than the Ikaros-assisted geneticist (P<0.001) . Conclusion AI-assisted karyotype analysis of amniotic fluid cells has a high degree of automation, but its ability to recognize chromosomal structural abnormalities needs to be improved. It is suggested that AI be combined with the geneticist for karyotype analysis in clinical application to ensure the quality of prenatal diagnosis and improve efficiency.

Keywords

• karyotype analysis|artificial intelligence|prenatal diagnosis|convolutional neural networks|image segmentation|chromosome classification