biostudies-literatureUnknown28(7)Cheng HThe presence of the <i>RUNX1</i>::<i>RUNX1T1</i> fusion gene in acute myeloid leukemia (AML) is associated with distinct morphological characteristics of myeloblasts. This study aims to assess the capability of artificial intelligence (AI) in identifying genetic abnormalities based on cell morphology. This multicenter trial included 205 patients diagnosed with AML, of which 75 were AML with <i>RUNX1</i>::<i>RUNX1T1</i>. A dataset comprising 65,039 images of myeloblasts was compiled for training, testing, and validating an AI model. The model demonstrated proficiency in adapting to varied clinical scenarios by applying two different threshold values. Under the threshold of 0.59, the testing and validation cohorts demonstrated sensitivities of 92.86% and 95.65%, with corresponding accuraciesiScience112998https://www.ebi.ac.uk/biostudies/studies/S-EPMC12274885biostudies-literaturePredicting <i>RUNX1</i>::<i>RUNX1T1</i> genetic abnormalities in acute myeloid leukemia from bone marrow smears: Can artificial intelligence do better?PMC12274885Yang JJin XXiao YDing JXu HTang GWang JYang YCao XZhang YGuo FCheng HLou JfalsePredicting <i>RUNX1</i>::<i>RUNX1T1</i> genetic abnormalities in acute myeloid leukemia from bone marrow smears: Can artificial intelligence do better?The presence of the <i>RUNX1</i>::<i>RUNX1T1</i> fusion gene in acute myeloid leukemia (AML) is associated with distinct morphological characteristics of myeloblasts. This study aims to assess the capability of artificial intelligence (AI) in identifying genetic abnormalities based on cell morphology. This multicenter trial included 205 patients diagnosed with AML, of which 75 were AML with <i>RUNX1</i>::<i>RUNX1T1</i>. A dataset comprising 65,039 images of myeloblasts was compiled for training, testing, and validating an AI model. The model demonstrated proficiency in adapting to varied clinical scenarios by applying two different threshold values. Under the threshold of 0.59, the testing and validation cohorts demonstrated sensitivities of 92.86% and 95.65%, with corresponding accuracies2025-01-01T00:00:00Z2025 Jul2025-08-23T03:06:21.76Z2025-08-23T03:06:21.76ZS-EPMC122748854068778310.1016/j.isci.2025.112998