Cheng Hui, Ding Jing, Wang Juan, Xiao Yujun, Jin Xinyi, Zhang Yan, Yang Yuanyuan, Xu Huangmeng, Cao Xinyi, Guo Fangyu, Yang Jianmin, Lou Jiatao, Tang Gusheng
Department of Hematology, Changhai Hospital, Naval Medical University, Shanghai, China.
Department of Laboratory Medicine, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
iScience. 2025 Jun 25;28(7):112998. doi: 10.1016/j.isci.2025.112998. eCollection 2025 Jul 18.
The presence of the :: 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 ::. 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 accuracies of 87.04% and 71.88%. Conversely, by setting the threshold at 0.88, specificities of 92.31% and 92.68% were achieved, along with accuracies of 88.89% and 90.63%. Regardless of the threshold, this AI model effectively distinguished :: genetic alterations based on cell morphology.
急性髓系白血病（AML）中::融合基因的存在与成髓细胞的独特形态特征相关。本研究旨在评估人工智能（AI）基于细胞形态识别基因异常的能力。这项多中心试验纳入了205例诊断为AML的患者，其中75例为伴有::的AML。编制了一个包含65039张成髓细胞图像的数据集，用于训练、测试和验证一个AI模型。该模型通过应用两个不同的阈值，表现出适应不同临床场景的能力。在0.59的阈值下，测试和验证队列的敏感性分别为92.86%和95.65%，相应的准确率分别为87.04%和71.88%。相反，将阈值设定为0.88时，特异性分别达到92.31%和92.68%，准确率分别为88.89%和90.63%。无论阈值如何，这个AI模型都能基于细胞形态有效区分::基因改变。
