Abstract：
Morphological identification of peripheral leukocytes is a complex and time-consuming task, having especially high requirements for personnel expertise. This study is to investigate the role of artificial intelligence (AI) in assisting the manual leukocyte differentiation of peripheral blood.
A total of 102 blood samples that triggered the review rules of hematology analyzers were enrolled. The peripheral blood smears were prepared and analyzed by Mindray MC-100i digital morphology analyzers. Two hundreds leukocytes were located and their cell images were collected. Two senior technologists labeled all cells to form standard answers. Afterward, the digital morphology analyzer unitized AI to pre-classify all cells. Ten junior and intermediate technologists were selected to review the cells with the AI pre-classification, yielding the AI-assisted classifications. Then the cell images were shuffled and re-classified without AI. The accuracy, sensitivity and specificity of the leukocyte differentiation with or without AI assistance were analyzed and compared. The time required for classification by each person was recorded.
For junior technologists, the accuracy of normal and abnormal leukocyte differentiation increased by 4.79% and 15.16% with the assistance of AI. And for intermediate technologists, the accuracy increased by 7.40% and 14.54% for normal and abnormal leukocyte differentiation, respectively. The sensitivity and specificity also significantly increased with the help of AI. In addition, the average time for each individual to classify each blood smear was shortened by 215 s with AI.
AI can assist laboratory technologists in the morphological differentiation of leukocytes. In particular, it can improve the sensitivity of abnormal leukocyte differentiation and lower the risk of missing detection of abnormal WBCs.
A total of 102 blood samples that triggered the review rules of hematology analyzers were enrolled. The peripheral blood smears were prepared and analyzed by Mindray MC-100i digital morphology analyzers. Two hundreds leukocytes were located and their cell images were collected. Two senior technologists labeled all cells to form standard answers. Afterward, the digital morphology analyzer unitized AI to pre-classify all cells. Ten junior and intermediate technologists were selected to review the cells with the AI pre-classification, yielding the AI-assisted classifications. Then the cell images were shuffled and re-classified without AI. The accuracy, sensitivity and specificity of the leukocyte differentiation with or without AI assistance were analyzed and compared. The time required for classification by each person was recorded.
For junior technologists, the accuracy of normal and abnormal leukocyte differentiation increased by 4.79% and 15.16% with the assistance of AI. And for intermediate technologists, the accuracy increased by 7.40% and 14.54% for normal and abnormal leukocyte differentiation, respectively. The sensitivity and specificity also significantly increased with the help of AI. In addition, the average time for each individual to classify each blood smear was shortened by 215 s with AI.
AI can assist laboratory technologists in the morphological differentiation of leukocytes. In particular, it can improve the sensitivity of abnormal leukocyte differentiation and lower the risk of missing detection of abnormal WBCs.
摘要：
外周白细胞的形态学鉴定是一项复杂而耗时的任务，对人员专业知识要求特别高。本研究旨在探讨人工智能（AI）在辅助外周血手动白细胞分化中的作用。
共纳入102个触发血液学分析仪审查规则的血液样本。外周血涂片的制备和分析采用MindrayMC-100i数字形态分析仪。定位了两百个白细胞并收集了它们的细胞图像。两名高级技术人员标记所有细胞以形成标准答案。之后,数字形态分析仪联合AI对所有细胞进行预分类。选择了10名初级和中级技术人员用AI预分类来审查细胞，产生人工智能辅助分类。然后将细胞图像洗牌并在没有AI的情况下重新分类。准确性,分析和比较有或没有AI辅助的白细胞分化的敏感性和特异性。记录每个人分类所需的时间。
对于初级技术专家，在AI的辅助下，正常和异常白细胞分化的准确性分别提高了4.79％和15.16％。对于中级技术人员来说，对于正常和异常的白细胞分化，准确率分别提高了7.40%和14.54%，分别。在AI的帮助下，敏感性和特异性也显着增加。此外,使用AI，每个人对每个血液涂片进行分类的平均时间缩短了215s。
AI可以帮助实验室技术人员进行白细胞的形态分化。特别是,它可以提高白细胞异常分化的敏感性，降低白细胞异常漏检的风险。
共纳入102个触发血液学分析仪审查规则的血液样本。外周血涂片的制备和分析采用MindrayMC-100i数字形态分析仪。定位了两百个白细胞并收集了它们的细胞图像。两名高级技术人员标记所有细胞以形成标准答案。之后,数字形态分析仪联合AI对所有细胞进行预分类。选择了10名初级和中级技术人员用AI预分类来审查细胞，产生人工智能辅助分类。然后将细胞图像洗牌并在没有AI的情况下重新分类。准确性,分析和比较有或没有AI辅助的白细胞分化的敏感性和特异性。记录每个人分类所需的时间。
对于初级技术专家，在AI的辅助下，正常和异常白细胞分化的准确性分别提高了4.79％和15.16％。对于中级技术人员来说，对于正常和异常的白细胞分化，准确率分别提高了7.40%和14.54%，分别。在AI的帮助下，敏感性和特异性也显着增加。此外,使用AI，每个人对每个血液涂片进行分类的平均时间缩短了215s。
AI可以帮助实验室技术人员进行白细胞的形态分化。特别是,它可以提高白细胞异常分化的敏感性，降低白细胞异常漏检的风险。
