2024
DOI: 10.21203/rs.3.rs-4019004/v1
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Predicting RUNX1::RUNX1T1 genetic abnormalities in acute myeloid leukemia from bone marrow smears: Can artificial intelligence do better?

Hui Cheng,
Jing Ding,
Juan Wang
et al.

Abstract: Background: The presence of the RUNX1::RUNX1T1 fusion gene in patients diagnosed with acute myeloid leukemia (AML) subtype is often indicated by distinctive morphological features in myeloblasts from bone marrow (BM) smears. This study aims to evaluate the capacity of artificial intelligence (AI) to identify specific genetic abnormalities based solely on morphological characteristics. The intent is to investigate a non-invasive, cost-effective, and efficient preliminary screening method prior to the applicatio… Show more

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