Author:
Xing Ying,Liu Xuekai,Dai Juhua,Ge Xiaoxing,Wang Qingchen,Hu Ziyu,Wu Zhicheng,Zeng Xuehui,Xu Dan,Qu Chenxue
Abstract
AbstractBackground and objectiveMorphological 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.MethodsA 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.ResultsFor 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.ConclusionAI 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.
Funder
Wu Jieping Medical Foundation
Publisher
Springer Science and Business Media LLC
Subject
Health Informatics,Health Policy,Computer Science Applications
Reference16 articles.
1. PalmerL BriggsC. McFaddenS et al, ICSH recommendations for the standardization of nomenclature and grading of peripheral blood cell morphological features. Int J Lab Hematol. 2015;37(3):287–303.
2. The Hematology and Osology Group. Chinese society of laboratory medicine, guideline for the report standardization of complete blood count test. Chin J Lab Med. 2020;43(6):619–27.
3. Ohsaka A. Artificial intelligence (AI) and hematological diseases: establishment of a peripheral blood convolutional neural network (CNN)-based digital morphology analysis system. Rinsho Ketsueki. 2020;61(5):564–9.
4. Fan H, Zhang F, Xi L, Li Z, Liu G, Xu Y. LeukocyteMask: An automated localization and segmentation method for leukocyte in blood smear images using deep neural networks. J Biophotonics. 2019;12: e201800488.
5. Kono K, Hayata R, Murakami S, et al. Quantitative distinction of the morphological characteristic of erythrocyte precursor cells with texture analysis using gray level co-occurrence matrix. J Clin Lab Anal. 2018;32:1–6.
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