title: [Clinical application of automated digital image analysis for morphology review of peripheral blood leukocyte].
abstract: OBJECTIVE
  To explore the clinical application of automated digital image analysis in leukocyte morphology examination when review criteria of hematology analyzer are triggered.
  
  
  METHODS
  The reference range of leukocyte differentiation by automated digital image analysis was established by analyzing 304 healthy blood samples from Peking University First Hospital. Six hundred and ninty-seven blood samples from Peking University First Hospital were randomly collected from November 2013 to April 2014, complete blood cells were counted on hematology analyzer, blood smears were made and stained at the same time. Blood smears were detected by automated digital image analyzer and the results were checked (reclassification) by a staff with abundant morphology experience. The same smear was examined manually by microscope. The results by manual microscopic differentiation were used as"golden standard", and diagnostic efficiency of abnormal specimens by automated digital image analysis was calculated, including sensitivity, specificity and accuracy. The difference of abnormal leukocytes detected by two different methods was analyzed in 30 samples of hematological and infectious diseases.
  
  
  RESULTS
  Specificity of identifying abnormalities of white blood cells by automated digital image analysis was more than 90% except monocyte. Sensitivity of neutrophil toxic abnormities (including Döhle body, toxic granulate and vacuolization) was 100%; sensitivity of blast cells, immature granulates and atypical lymphocytes were 91.7%, 60% to 81.5% and 61.5%, respectively. Sensitivity of leukocyte differential count was 91.8% for neutrophils, 88.5% for lymphocytes, 69.1% for monocytes, 78.9% for eosinophils and 36.3 for basophils. The positive rate of recognizing abnormal cells (blast, immature granulocyte and atypical lymphocyte) by manual microscopic method was 46.7%, 53.3% and 10%, respectively. The positive rate of automated digital image analysis was 43.3%, 60% and 10%, respectively. There was no statistic significance between two methods (χ(2) = 0.067, 0.271, 0.000, all P>0.05). Automated digital image analysis could be used to morphology examination with abnormal leukocytes and optimize review criteria of hematology analyzer.
  
  
  CONCLUSIONS
  Sensitivity and specificity of recognizing abnormal blood leukocytes by automated digital image analysis are satisfactory, which can be used as a tool of leukocyte morphology review when review criteria of hematology analyzer are triggered.
authors.affiliation: Clinical Laboratory, Peking University First Hospital, Beijing 100034, China.
authors.forename: Ying
authors.initials: Y
authors.lastname: Xing
authors.affiliation: Department of Medicine Laboratory, Shandong Medical College, Jinan 250000, China.
authors.forename: Xiaohua
authors.initials: X
authors.lastname: Yan
authors.forename: Chengwei
authors.initials: C
authors.lastname: Pu
authors.forename: Ke
authors.initials: K
authors.lastname: Shang
authors.forename: Ning
authors.initials: N
authors.lastname: Dong
authors.forename: Run
authors.initials: R
authors.lastname: Wang
authors.forename: Jianzhong
authors.initials: J
authors.lastname: Wang
mesh_terms: D057205:Automation, Laboratory; D002454:Cell Differentiation; D006801:Humans; D007958:Leukocyte Count; D007962:Leukocytes; D008853:Microscopy; D012016:Reference Values; D013234:Stem Cells