在印度北部的一家三级医疗中心对带有 ShonitTM 的自动血液显微镜系统 AI100 进行评估

Upinder Singh, Shreyam Acharya, Tushar Sehgal
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摘要

背景:对外周血片进行显微镜检查是一项劳动密集型、主观且耗时的工作。它还需要训练有素的技术人员。随着技术的进步,人们开发出了可对红细胞和白细胞进行分类的自动形态分析系统。我们的目的是研究带有 ShonitTM 的自动显微系统 AI100 检查外周血片的能力。研究方法该研究是在新德里全印度医学科学研究所进行的一项前瞻性研究。我们比较了带 ShonitTM 的自动形态分析系统 AI100 和人工显微镜检查的黄金标准,以确定血液中的形态异常。结果:共研究了 108 个病例。其中 21 例因染色和涂片效果不佳而被排除。男女比例为 7:5,年龄中位数为 31.1 岁。中性粒细胞、淋巴细胞、单核细胞、嗜酸性粒细胞和嗜碱性粒细胞的 AI100 与人工显微镜检查的白细胞百分比之间的皮尔逊相关性(r)分别为 0.92、0.81、0.34、0.94 和 0.25。AI100 与人工显微镜检查在小红细胞、大红细胞、泪滴细胞、靶细胞、棘细胞和棘细胞方面的一致性分别为 77%、86%、100%、100%、95% 和 97%。在血小板计数、团块状血小板和巨血小板方面,AI100 与人工显微镜检查的一致性分别为 89%、100% 和 89%。结论带有 ShonitTM 的自动细胞分析系统 AI100 能够对外周血涂片中的红细胞、白细胞和血小板进行形态学分类。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Evaluation of Automated Blood Microscopy System AI100 with ShonitTM in a Tertiary Care Center in Northern India
Background: The microscopic examination of peripheral blood film is labour-intensive, subjective and time-consuming. It also requires trained technical staff. Technological advancements have been made to develop automated morphological analytical systems for the classification of both red blood cells and white blood cells. We aimed to investigate the ability of the automated microscopy system AI100 with the ShonitTM to examine peripheral blood films. Methods: The study was a prospective study done at the All India Institute of Medical Sciences, New Delhi. We compared the automated morphological analysis system AI100 with ShonitTM with the gold standard of manual microscopy to identify morphological abnormalities in the blood. Results: A total of 108 cases were studied. Twenty-one cases were excluded due to suboptimal staining and smearing. The male-to-female ratio was 7:5, and the median age was 31.1 Years. The Pearson correlation (r) between % of WBCs between AI100 and manual microscopy was 0.92,0.81, 0.34, 0.94 and 0.25 for neutrophils, lymphocytes, monocytes, eosinophils and basophils, respectively. The concordance of AI100 and manual microscopy for microcytes, macrocytes, tear drop cells, target cells, acanthocytes, and echinocytes was 77%, 86%, 100%, 100%,95% and 97%, respectively. The concordance of AI100 and manual microscopy for platelet count, clumps, and giant platelets was 89%,100% and 89%. Conclusions: The automated cell analysis system AI100 with ShonitTM is capable of morphological classification of RBC, WBC and platelet in peripheral blood smears.
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