Application of artificial intelligence assists bone marrow cytomorphology analysis in the diagnosis and treatment of acute myeloid leukemia
Xiao Jigang, Wang Huijun, Cai Wenyu, Chen Shuying, Song Ge, Lu Xulin, Liu Chenxi, Wang Zhigang, Fang Chao, Chen Yanan, Xiao Zhijian
Abstract
ObjectiveTo investigate the value of artificial intelligence (AI) cytomorphologic analysis system in the cytomorphological diagnosis and therapeutic evaluation of acute myeloid leukemia (AML).
MethodsBone marrow smear samples were collected from 150 patients with newly diagnosed and treated acute myeloid leukemia who were inpatients and outpatients at the Department of Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College from June 1, 2021 to July 31, 2022 for retrospective analysis. Among them, there were 50 patients in the newly diagnosed group, including 28 males and 22 females, with the onset age of 43.5(32.3,58.8)years. There were 100 patients in the post-treatment group, including 36 males and 64 females, with the onset age of 34.5(23.0,47.0)years. The results from cytomorphology expert were used as the gold standard and the Python 3.6.7 was used for analysis to evaluate the accuracy, sensitivity, and specificity of the AI cytomorphologic analysis system for blast cell recognition in AML diagnosis and treatment.
ResultsThe proportion of blasts in AI analysis of 50 samples in the newly diagnosed group was≥20%, which met the diagnostic criteria of AML. AI analysis of blasts had an accuracy of 90.3%, sensitivity of 85.5%, and specificity of 98.0%. The correlation coefficient between AI and the proportion of blasts analyzed by experts was positively correlated(r=0.882, P<0.001). Meanwhile, in the post-treatment group, the sensitivity and specificity of AI analysis of blasts were 89.7% and 99.2%, respectively. The correlation coefficient between AI and the proportion of blasts analyzed by experts was positively correlated(r=0.957, P<0.001). According to AI analysis data, there are 8 samples in this group whose AI efficacy evaluation results on AML are inconsistent with expert analysis.
ConclusionAI cytomorphologic analysis system has high accuracy, sensitivity and specificity for blast cell recognition in AML morphological diagnosis and therapeutic evaluation.
Key words:
Artificial intelligence; Bone marrow smear; Cytomorphology; Acute myeloid leukemia
Contributor Information
Xiao Jigang
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China
Wang Huijun
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China
Cai Wenyu
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China
Chen Shuying
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China
Song Ge
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China
Lu Xulin
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China
Liu Chenxi
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China
Wang Zhigang
Tianjin DeepCyto L.L.C, Tianjin 300073, China
Fang Chao
Tianjin DeepCyto L.L.C, Tianjin 300073, China
Chen Yanan
Tianjin DeepCyto L.L.C, Tianjin 300073, China
Xiao Zhijian
State Key Laboratory of Experimental Hematology, National Clinical Research Center for Blood Diseases, Haihe Laboratory of Cell Ecosystem, Institute of Hematology and Blood Diseases Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin 300020, China
