Modification of the Algorithm Used by Automated Hematology Analyzer XN-3000 Improves Specificity in the Detection of Schistocytes.

BACKGROUND: This study investigated the feasibility and accuracy of an automated hematology analyzer in the detection of schistocytes. METHODS: In total, 1,026 peripheral blood samples were collected. Schistocytes were morphologically diagnosed by manual examination of digital microscopic red blood cell images captured by a Sysmex DI-60. Automated diagnoses were performed using a Sysmex XN-3000. RESULT: The accuracy of automated diagnosis using the XN-3000 with the default algorithm "fragments?" was determined through comparison with the findings of morphological examination. The comparison showed a sensitivity of 100% and a specificity of 41.6% for automated diagnosis. To improve the low specificity, a two-step analysis was performed. Use of the algorithm "fragments?" in XN-3000 followed by an off-line analysis using the cell parameter %FRC (percent fragmented red blood cells) yielded a sensitivity of 86.5% and a specificity of 70.3%. CONCLUSIONS: Our study indicated that combined use of the %FRC parameter with the default algorithm of the Sysmex XN-3000 automated hematology analyzer can improve the low specificity of the default algorithm in rapid screening for schistocytes.

Peripheral granular lymphocytopenia and dysmorphic leukocytosis as simple prognostic markers in COVID-19.

INTRODUCTION: Developing prognostic markers can be useful for clinical decision-making. Peripheral blood (PB) examination is simple and basic that can be performed in any facility. We aimed to investigate whether PB examination can predict prognosis in coronavirus disease (COVID-19). METHODS: Complete blood count (CBC) and PB cell morphology were examined in 38 healthy controls (HCs) and 40 patients with COVID-19. Patients with COVID-19, including 26 mild and 14 severe cases, were hospitalized in Juntendo University Hospital (Tokyo, Japan) between April 1 and August 6, 2020. PB examinations were performed using Sysmex XN-3000 automated hematology analyzer and Sysmex DI-60 employing the convolutional neural network-based automatic image-recognition system. RESULTS: Compared with mild cases, severe cases showed a significantly higher incidence of anemia, lymphopenia, and leukocytosis (P < .001). Granular lymphocyte counts were normal or higher in mild cases and persistently decreased in fatal cases. Temporary increase in granular lymphocytes was associated with survival of patients with severe infection. Red cell distribution width was significantly higher in severe cases than in mild cases (P < .001). Neutrophil dysplasia was consistently observed in COVID-19 cases, but not in HCs. Levels of giant neutrophils and toxic granulation/Döhle bodies were increased in severe cases. CONCLUSION: Basic PB examination can be useful to predict the prognosis of COVID-19, by detecting SARS-CoV-2 infection-induced multi-lineage changes in blood cell counts and morphological anomalies. These changes were dynamically correlated with disease severity and may be associated with disruption of hematopoiesis and the immunological system due to bone marrow stress in severe infection.

Automated diagnostic support system with deep learning algorithms for distinction of Philadelphia chromosome-negative myeloproliferative neoplasms using peripheral blood specimen.

Philadelphia chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs) such as polycythemia vera (PV), essential thrombocythemia (ET), and primary myelofibrosis are characterized by abnormal proliferation of mature bone marrow cell lineages. Since various non-hematologic disorders can also cause leukocytosis, thrombocytosis and polycythemia, the detection of abnormal peripheral blood cells is essential for the diagnostic screening of Ph-negative MPNs. We sought to develop an automated diagnostic support system of Ph-negative MPNs. Our strategy was to combine the complete blood cell count and research parameters obtained by an automated hematology analyzer (Sysmex XN-9000) with morphological parameters that were extracted using a convolutional neural network deep learning system equipped with an Extreme Gradient Boosting (XGBoost)-based decision-making algorithm. The developed system showed promising performance in the differentiation of PV, ET, and MF with high accuracy when compared with those of the human diagnoses, namely: > 90% sensitivity and > 90% specificity. The calculated area under the curve of the ROC curves were 0.990, 0.967, and 0.974 for PV, ET, MF, respectively. This study is a step toward establishing a universal automated diagnostic system for all types of hematology disorders.