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.

To investigate the application effect and clinical value of AI in prenatal karyotype diagnosis.

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.

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) .

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.

染色体异常是导致出生缺陷的常见原因，核型分析仍是产前诊断染色体异常的重要方法，也是出生缺陷防控的有效手段，但目前核型分析尤其是染色体图像分割分类主要依靠人工，费时费力。人工智能（AI）是核型分析的一种新方式，研究其在产前染色体核型诊断中的价值具有重要意义。

探讨AI在产前染色体核型诊断中的应用效果和临床价值。

选取2020—2022年在无锡市妇幼保健院医学遗传与产前诊断科接受介入性产前诊断、行羊水染色体核型分析的1 000例孕妇。采用双线模式：一线AI阅片后，由1名遗传医师审核，二线由另1名遗传医师应用Ikaros核型分析工作站阅片，记录各自的诊断结果及所需时间。样本的最终诊断结果以一线的人工审核和二线的人工阅片结果为准。

1 000例羊水样本中，AI诊断正常核型735例、非整倍体233例、结构异常0例、嵌合体32例。AI辅助遗传医师的诊断结果与遗传医师应用Ikaros系统的诊断结果完全一致，正常核型、非整倍体、结构异常、嵌合体分别是689、233、45、33例。与AI辅助遗传医师相比，AI诊断具有强一致性（Kappa值=0.895，95%CI=0.866~0.924，P<0.01）。AI诊断准确率为95.4%，灵敏度为95.4%，阳性预测值为100.0%。其中，诊断正常核型、非整倍体、结构异常、嵌合体的灵敏度分别为100.0%、100.0%、0、97.0%；阳性预测值分别为100.0%、100.0%、0、100.0%。AI平均诊断用时少于AI辅助遗传医师和Ikaros辅助遗传医师（P<0.001）；AI辅助遗传医师平均诊断用时少于Ikaros辅助遗传医师组（P<0.001）。

AI分析羊水核型的自动化程度高，但识别染色体结构异常的能力有待提高，建议采用AI联合遗传医师阅片的方式应用于临床，以保证产前诊断的质量并提高效率。