Artificial Intelligence Enables the Label-Free Identification of Chronic Myeloid Leukemia Cells with Mitochondrial Morphological Alterations

Long-term tyrosine kinase inhibitor (TKI) treatment for patients with chronic myeloid leukemia (CML) causes various adverse events. Achieving a deep molecular response (DMR) is necessary for discontinuing TKIs and attaining treatment-free remission. Thus, early diagnosis is crucial as a lower DMR achievement rate has been reported in high-risk patients. Therefore, we attempted to identify CML cells using a novel technology that combines artificial intelligence (AI) with flow cytometry and investigated the basis for AI- mediated identification. Our findings indicate that BCR-ABL1 -transduced cells and leukocytes from patients with CML showed significantly fragmented mitochondria and decreased mitochondrial membrane potential. Additionally, BCR-ABL1 enhanced the phosphorylation of Drp1 via the mitogen-activated protein kinase pathway, inducing mitochondrial fragmentation. Finally, the AI identified cell line models and patient leukocytes that showed mitochondrial morphological changes. Our study suggested that this AI- based technology enables the highly sensitive detection of BCR-ABL1 -positive cells and early diagnosis of CML. ### Competing Interest Statement K.S. and K.Y. are employees of Sysmex. S.O. is the founder and shareholder of ThinkCyte K.K., a company engaged in the development of machine vision cytometry. Y.K. is an employee of and has shares of stock options from ThinkCyte K.K. S.O. and Y.K. have filed patent applications related to the label-free GC method. Other authors declare no competing financial interests.