Application of artificial intelligence assists bone marrow cytomorphology analysis in the diagnosis and treatment of acute myeloid leukemia
10.3760/cma.j.cn114452-20220928-00563
- VernacularTitle:人工智能辅助骨髓细胞形态学分析在急性髓系白血病诊疗中的应用
- Author:
Jigang XIAO
1
;
Huijun WANG
;
Wenyu CAI
;
Shuying CHEN
;
Ge SONG
;
Xulin LU
;
Chenxi LIU
;
Zhigang WANG
;
Chao FANG
;
Yanan CHEN
;
Zhijian XIAO
Author Information
1. 中国医学科学院血液病医院（中国医学科学院血液学研究所），实验血液学国家重点实验室，国家血液系统疾病临床医学研究中心，细胞生态海河实验室，天津 300020
- Keywords:
Artificial intelligence;
Bone marrow smear;
Cytomorphology;
Acute myeloid leukemia
- From:
Chinese Journal of Laboratory Medicine
2023;46(3):274-279
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To investigate the value of artificial intelligence (AI) cytomorphologic analysis system in the cytomorphological diagnosis and therapeutic evaluation of acute myeloid leukemia (AML).Methods:Bone 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.Results:The 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. Conclusion:AI cytomorphologic analysis system has high accuracy, sensitivity and specificity for blast cell recognition in AML morphological diagnosis and therapeutic evaluation.
