ReviewHuman genetics2025
Clinical validation of artificial intelligence-assisted karyotyping on peripheral blood in a cytogenetic diagnostic laboratory.
Review in Human genetics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
13 authors.
Funding
Abstract
G-banded chromosome analysis, also known as G-banded karyotyping, remains a fundamental and irreplaceable diagnostic modality in clinical genetic testing. G-banded karyotypes provide whole genome visualization through chromosome banding patterns at the single-cell resolution for the diagnosis of chromosomal disorders. However, this method is labor-intensive and requires specialized expertise to manually analyze and karyotype metaphase spreads. In recent years, artificial intelligence (AI) algorithms have been utilized to automate karyotyping and assist with chromosome analysis. Despite this progress, there is a scarcity of studies evaluating the utility of artificial intelligence-assisted (AI-assisted) karyotyping analysis in cytogenetics diagnostic laboratories. This study highlights promising applications of AI-assisted karyotyping analysis in a cytogenetics diagnostic laboratory through a combination of a literature review, our data, and experience from a retrospective cohort study. This study also discusses important considerations of the use of AI-assisted karyotyping analysis in a cytogenetic diagnostic laboratory and outlines a two-stage framework for its implementation into clinical workflows. This approach aims to utilize the accuracy and efficiency of AI-assisted karyotyping analysis, potentially benefiting personalized patient care and contributing to advancements in the health system.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.