scholarly journals Developing and Preliminary Validating an Automatic Cell Classification System for Bone Marrow Smears: a Pilot Study

2020 ◽  
Vol 44 (10) ◽  
Author(s):  
Hong Jin ◽  
Xinyan Fu ◽  
Xinyi Cao ◽  
Mingxia Sun ◽  
Xiaofen Wang ◽  
...  
Keyword(s):  
Bone Marrow ◽  
Cell Count ◽  
Marrow Cell ◽  
Differential Count ◽  
Cell Classification ◽  
Initial Development ◽  
Differential Cell Count ◽  
Analysis Methods ◽  
Differential Cell ◽  
Cell Series

Abstract Bone marrow smear examination is an indispensable diagnostic tool in the evaluation of hematological diseases, but the process of manual differential count is labor extensive. In this study, we developed an automatic system with integrated scanning hardware and machine learning-based software to perform differential cell count on bone marrow smears to assist diagnosis. The initial development of the artificial neural network was based on 3000 marrow smear samples retrospectively archived from Sir Run Run Shaw Hospital affiliated to Zhejiang University School of Medicine between June 2016 and December 2018. The preliminary field validating test of the system was based on 124 marrow smears newly collected from the Second Affiliated Hospital of Harbin Medical University between April 2019 and November 2019. The study was performed in parallel of machine automatic recognition with conventional manual differential count by pathologists using the microscope. We selected representative 600,000 marrow cell images as training set of the algorithm, followed by random captured 30,867 cell images for validation. In validation, the overall accuracy of automatic cell classification was 90.1% (95% CI, 89.8–90.5%). In a preliminary field validating test, the reliability coefficient (ICC) of cell series proportion between the two analysis methods were high (ICC ≥ 0.883, P < 0.0001) and the results by the two analysis methods were consistent for granulocytes and erythrocytes. The system was effective in cell classification and differential cell count on marrow smears. It provides a useful digital tool in the screening and evaluation of various hematological disorders.

Morphogo: An Automatic Bone Marrow Cell Classification System on Digital Images Analyzed by Artificial Intelligence

2020 ◽  
Vol 64 (6) ◽  
pp. 588-596
Author(s):  
Xinyan Fu ◽  
May Fu ◽  
Qiang Li ◽  
Xiangui Peng ◽  
Ju Lu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Bone Marrow ◽  
Sensitivity And Specificity ◽  
Bone Marrow Aspirate ◽  
Marrow Cell ◽  
Clinical Applications ◽  
Differential Count ◽  
Cell Classification ◽  
Cell Percentage ◽  
Differential Cell

<b><i>Introduction:</i></b> The nucleated-cell differential count on the bone marrow aspirate smears is required for the clinical diagnosis of hematological malignancy. Manual bone marrow differential count is time consuming and lacks consistency. In this study, a novel artificial intelligence (AI)-based system was developed to perform cell automatic classification of bone marrow cells and determine its potential clinical applications. <b><i>Materials and Methods:</i></b> Bone marrow aspirate smears were collected from the Xinqiao Hospital of Army Medical University. First, an automated analysis system (<i>Morphogo</i>) scanned and generated whole digital images of bone marrow smears. Then, the nucleated marrow cells in the selected areas of the smears at a magnification of ×1,000 were analyzed by the software utilizing an AI-based platform. The cell classification results were further reviewed and confirmed independently by 2 experienced pathologists. The automatic cell classification performance of the system was evaluated using 3 categories: accuracy, sensitivity, and specificity. Correlation coefficients and linear regression equations between automatic cell classification by the AI-based system and concurrent manual differential count were calculated. <b><i>Results:</i></b> In 230 cases, the classification accuracy was above 85.7% for hematopoietic lineage cells. Averages of sensitivity and specificity of the system were found to be 69.4 and 97.2%, respectively. The differential cell percentage of the automated count based on 200–500 cell counts was correlated with differential cell percentage provided by the pathologists for granulocytes, erythrocytes, and lymphocytes (<i>r</i> ≥ 0.762, <i>p</i> &#x3c; 0.001). <b><i>Discussion/Conclusion:</i></b> This pilot study confirmed that the <i>Morphogo</i> system is a reliable tool for automatic bone marrow cell differential count analysis and has potential for clinical applications. Current ongoing large-scale multicenter validation studies will provide more information to further confirm the clinical utility of the system.

scholarly journals DIFFERENTIAL CELL COUNT AS DIAGNOSTIC AND PROGNOSTIC MARKER OF ACUTE MYOCARDIAL INFARCTION

2018 ◽  
Vol 24 (2) ◽  
pp. 175
Author(s):  
Sri Anita ◽  
Liong Boy Kurniawan ◽  
Darwati Muhadi
Keyword(s):  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Cell Count ◽  
Inflammatory Marker ◽  
St Elevation Myocardial Infarction ◽  
Chi Square ◽  
Cross Sectional ◽  
Differential Cell Count ◽  
Differential Cell ◽  
St Elevation

Myocardial infarction is a necrosis of myocardial cells due to lack of blood and oxygen supply caused by obstruction of coronary arteries, mostly due to atherosclerosis processes. Increased inflammatory marker level is associated with poor cardiovascular prognosis. This study was aimed to know whether leukocytes count, differential cell count and the Ratio of Neutrophils-Lymphocytes (RNL) could distinguish between types of Acute Myocardial Infarction (AMI) and to evaluate its correlation with mortality. This was a cross-sectional retrospective study using medical records patients which were diagnosed as AMI by clinicians in Cardiac Centre of the Dr. Wahidin Sudirohusodo Hospital during the period of April 1st, 2015 - May 31st, 2016. Statistical analysis used the Mann-Whitney and Chi-Square test, p<0.05 was considered as significant. The total subjects were 435 patients divided into 289 ST- Elevation Myocardial Infarction (STEMI) and 146 Non-ST-Elevation Myocardial Infarction (NSTEMI). There were significant differences in that mean of leukocytes, neutrophils, lymphocytes, monocytes, eosinophils counts and RNL between STEMI and NSTEMI (p <0.05). Significant differences were also found in leukocyte, neutrophils, lymphocytes, eosinophils, basophils and RNL mean between those who died and survived (p <0.05) and a significant correlation between increased leukocytes, neutrophils, basophils counts with mortality (p <0.05). In conclusion, the number of leukocytes and leukocyte count can be used as diagnostic markers of AMI between STEMI and NSTEMI, as well as prognostic markers among patients who died and survived. Routine blood sampling cohort studies in patients with AMI can avoid the bias of the results obtained. 

Cell function in the bovine mammary gland: a preliminary study on interdependence of healthy and infected udder quarters

2007 ◽  
Vol 74 (2) ◽  
pp. 174-179 ◽  
Author(s):  
Roswitha Merle ◽  
Anke Schröder ◽  
Jörn Hamann
Keyword(s):  
Cell Count ◽  
Cell Function ◽  
Cell Activity ◽  
Polymorphonuclear Leucocytes ◽  
Differential Cell Count ◽  
Immune Reactions ◽  
Cell Counts ◽  
Differential Cell ◽  
Group A ◽  
Group B

Udder defence mechanisms are not completely explained by current mastitis research. The anatomical construction of the udder implies that infection of one udder quarter does not influence the immune status of neighbouring quarters. To test this hypothesis, we compared the immune reactions of individual udder quarters in response to microbial attacks. In the course of immune reactions, polymorphonuclear leucocytes (PMN) release oxygen radicals, which can be determined by chemiluminescence (CL). Milk from 140 udder quarters of 36 cows was analysed for somatic cell count (SCC), differential cell count, viability and CL activity. Quarters with an SCC <100000 cells/ml and free of pathogens were defined as uninfected, all other quarters were categorized as infected. Three groups of cows were classified cytologically: group A (healthy, 11 animals, SCC limit <100000 cells/ml); group B (moderate mastitis, 8 cows, SCC [ges ]100000 and <400000 cells/ml in at least one quarter); and group C (severe mastitis, 17 cows, SCC [ges ]400000 cells/ml in at least one quarter). Infected and uninfected quarters in groups B and C were analysed separately. Viability of PMN leucocytes was significantly (P=0·0012) lower in group A (72·6%) than in healthy quarters of group C (84·0%). Lowering the SCC limit of healthy quarters to <50000 cells/ml (group A: all quarters within the udder) revealed striking differences between samples of groups B and C: in addition to varying differential cell counts and viabilities, CL activity of group B<50 (2929 CL units/million PMN) was markedly lower than that of the other groups (5616 in group A<50 and 6445 CL units/million PMN in group C<50). These results allow the conclusion that the infection of one udder quarter influences the cell activity of neighbouring quarters. When the SCC threshold for healthy quarters was reduced to 50000 cells/ml, greater differences in cell activities were detected between healthy udders and healthy quarters of infected udders.

scholarly journals The advantage of PCR for MTB in comparison to ADA in diagnosing tubercular pleural effusion

2018 ◽  
Vol 5 (1) ◽  
pp. 131
Author(s):  
Abhishek Agarwal ◽  
Ahbab Hussain ◽  
Rajendra Prasad ◽  
Anand Verma ◽  
Amitabh Banka ◽  
...  
Keyword(s):  
Pleural Effusion ◽  
Cell Count ◽  
Pleural Fluid ◽  
Extrapulmonary Tuberculosis ◽  
Total Cell ◽  
Differential Cell Count ◽  
Tuberculosis Patients ◽  
Lung Consolidation ◽  
Differential Cell ◽  
Important Health

Background: Tuberculosis continues to be an important health problem globally. The bacteriological confirmation of diagnosis in extrapulmonary tuberculosis patients is more difficult because most of the cases of extrapulmonary tuberculosis are paucibacillary in nature. In this study we have compared the pleural fluid ADA levels with PCR for MTB in pleural fluid to confirm the diagnosis of tuberculosis in the pleural fluid.Methods: The study was done over two years and a total of 106 patients with a clinico-radiological diagnosis of pleural effusion were enrolled for the study. The pleural fluid was aspirated and examined for total cell count, differential cell count, protein, sugar, ADA and PCR for MTB.A CT Thorax was done in all the 106 patients of pleural effusion and underlying consolidation along with pleural effusion was found in 60 patients.Results: The pleural fluid was exudative in nature in all the patients. 90 patients (84.9%) had lymphocyte predominant pleural effusion while 16 patients (15.1%) had neutrophil predominant pleural effusion. The overall sensitivity of ADA in all the cases of pleural effusion was 85.2% while the overall sensitivity of PCR for MTB in all the cases of pleural effusion was 51.1%. However, in the 60 patients of pleural effusion with underlying lung consolidation, the overall sensitivity of ADA was 69.1% while the overall sensitivity of PCR for MTB was 92.8% for diagnosing tubercular pleural effusion.Conclusions: PCR for MTB is a useful test along with ADA for diagnosing tubercular pleural effusion. PCR for MTB is especially useful in the diagnosis of tubercular pleural effusion in patients with underlying lung consolidation.  

scholarly journals Assessment of subclinical mastitis diagnostic accuracy by differential cell count in individual cow milk

2018 ◽  
Vol 18 (1) ◽  
pp. 460-465 ◽  
Author(s):  
Alfonso Zecconi ◽  
Diego Vairani ◽  
Micaela Cipolla ◽  
Nicoletta Rizzi ◽  
Lucio Zanini
Keyword(s):  
Diagnostic Accuracy ◽  
Cell Count ◽  
Subclinical Mastitis ◽  
Cow Milk ◽  
Differential Cell Count ◽  
Differential Cell

Normal values for bronchoalveolar lavage (BAL) differential cell count and lymphocyte subpopulations

2021 ◽  
Author(s):  
Daniel Soriano ◽  
Sebastian Fähndrich ◽  
Thomas Köhler ◽  
Wolfram Meschede ◽  
Joachim Müller-Quernheim ◽  
...  
Keyword(s):  
Bronchoalveolar Lavage ◽  
Cell Count ◽  
Normal Values ◽  
Lymphocyte Subpopulations ◽  
Differential Cell Count ◽  
Differential Cell
Sign in / Sign up

Export Citation Format

Share Document