Rosenblum Lynne S, Holmes Julia, Taghiyev Agshin F
Department of Pathology, Wake Forest University School of Medicine, Winston-Salem, NC 27157, USA.
Applied Spectral Imaging, Carlsbad, CA 92009, USA.
Genes (Basel). 2025 May 31;16(6):685. doi: 10.3390/genes16060685.
Artificial intelligence (AI) has entered the medical subspecialty of cytogenetics with the recent introduction of AI-guided karyotyping into the clinical laboratory. Karyotyping is an essential component of the cytogenetic analysis process; however, it is both labor-intensive and time-consuming. The introduction of AI algorithms into karyotyping software streamlines this process to provide accurate and abundant auto-karyotyped images for laboratory professionals to review and, also, alters the paradigm for chromosome analysis. Herein, we provide an overview of the AI-guided karyotyping products currently available for clinical use, discuss their utilization in the cytogenetics laboratory, and highlight changes AI-guided karyotyping has brought for early users. Finally, we reflect on our own laboratory observations and experience to discuss issues and practices that may need to adapt to best utilize this promising new technology.
随着人工智能（AI）引导的核型分析技术近期被引入临床实验室，人工智能已进入细胞遗传学这一医学亚专业领域。核型分析是细胞遗传学分析过程的重要组成部分；然而，它既耗费人力又耗时。将人工智能算法引入核型分析软件简化了这一过程，可为实验室专业人员提供准确且丰富的自动核型分析图像以供审核，同时也改变了染色体分析的模式。在此，我们概述了目前可用于临床的人工智能引导的核型分析产品，讨论它们在细胞遗传学实验室中的应用，并强调人工智能引导的核型分析给早期使用者带来的变化。最后，我们根据自己实验室的观察和经验，讨论可能需要调整以最佳利用这项有前景的新技术的问题和实践。
