Pathologist review of peripheral blood smears containing plasma cells and plasmacytoid lymphocytes

2020 ◽  
Author(s):  
Hibbah Nabeel ◽  
Tahmeena Ahmed ◽  
Lisa Senzel
Keyword(s):  
Peripheral Blood ◽  
Plasma Cells ◽  
Blood Smears ◽  
Peripheral Blood Smears

Brightfield and fluorescence in-channel staining of thin blood smears generated in a pumpless microfluidic

2021 ◽  
Author(s):  
Kokou S. Dogbevi ◽  
Paul Gordon ◽  
Kimberly L. Branan ◽  
Bryan Khai D. Ngo ◽  
Kevin B. Kiefer ◽  
...  
Keyword(s):  
Peripheral Blood ◽  
Blood Smears ◽  
Peripheral Blood Smears ◽  
Thin Blood Smears

Effective staining of peripheral blood smears which enhances the contrast of intracellular components and biomarkers is essential for the accurate characterization, diagnosis, and monitoring of various diseases such as malaria.

scholarly journals Histoplasma capsulatum in peripheral blood smears

2002 ◽  
Vol 116 (3) ◽  
pp. 503-503 ◽  
Author(s):  
Glen A. Kennedy ◽  
Jennifer L. Curnow ◽  
Julie Gooch ◽  
Bronwyn Williams ◽  
Peter Wood ◽  
...  
Keyword(s):  
Peripheral Blood ◽  
Histoplasma Capsulatum ◽  
Blood Smears ◽  
Peripheral Blood Smears

Performance of the CellaVision DC-1 digital cell imaging analyser for differential counting and morphological classification of blood cell

2021 ◽  
pp. jclinpath-2021-207863
Author(s):  
Lisa N van der Vorm ◽  
Henriët A Hendriks ◽  
Simone M Smits
Keyword(s):  
Blood Cell ◽  
Peripheral Blood ◽  
Cell Imaging ◽  
Small Satellite ◽  
Morphological Classification ◽  
Clinical Sensitivity ◽  
Blood Smears ◽  
Low Volume ◽  
Peripheral Blood Smears

AimsRecently, a new automated digital cell imaging analyser (Sysmex CellaVision DC-1), intended for use in low-volume and small satellite laboratories, has become available. The purpose of this study was to compare the performance of the DC-1 with the Sysmex DI-60 system and the gold standard, manual microscopy.MethodsWhite blood cell (WBC) differential counts in 100 normal and 100 abnormal peripheral blood smears were compared between the DC-1, the DI-60 and manual microscopy to establish accuracy, within-run imprecision, clinical sensitivity and specificity. Moreover, the agreement between precharacterisation and postcharacterisation of red blood cell (RBC) morphological abnormalities was determined for the DC-1.ResultsWBC preclassification and postclassification results of the DC-1 showed good correlation compared with DI-60 results and manual microscopy. In addition, the within-run SD of the DC-1 was below 1 for all five major WBC classes, indicating good reproducibility. Clinical sensitivity and specificity were, respectively, 96.7%/95.9% compared with the DI-60% and 96.6%/95.3% compared with manual microscopy. The overall agreement on RBC morphology between the precharacterisation and postcharacterisation results ranged from 49% (poikilocytosis) to 100% (hypochromasia, microcytosis and macrocytosis).ConclusionsThe DC-1 has proven to be an accurate digital cell imaging system for differential counting and morphological classification of WBCs and RBCs in peripheral blood smears. It is a compact and easily operated instrument that can offer low-volume and small satellite laboratories the possibilities of readily available blood cell analysis that can be stored and retrieved for consultation with remote locations.

scholarly journals Machine Learning (ML) Can Successfully Support Microscopic Differential Counts of Peripheral Blood Smears in a High Throughput Hematology Laboratory

Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 45-46
Author(s):  
Christian Pohlkamp ◽  
Kapil Jhalani ◽  
Niroshan Nadarajah ◽  
Inseok Heo ◽  
William Wetton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Single Cell ◽  
High Throughput ◽  
Peripheral Blood ◽  
Cell Types ◽  
Cell Type ◽  
Hematological Neoplasms ◽  
Blood Smears ◽  
Peripheral Blood Smears ◽  
Very High

Background: Cytomorphology is the gold standard for quick assessment of peripheral blood and bone marrow samples in hematological neoplasms. It is a broadly-accepted method for orchestrating more specific diagnostics including immunophenotyping or genetics. Inter-/intra-observer-reproducibility of single cell classification is only 75 to 90%. Only a limited number of cells (100 - 500 cells/smear) is read in a time-consuming procedure. Machine learning (ML) is more reliable where human skills are limited, i.e. in handling large amounts of data or images. We here tested ML to differentiate peripheral blood leukocytes in a high throughput hematology laboratory. Aim: To establish an ML-based cell classifier capable of identifying healthy and pathologic cells in digitalized peripheral blood smear scans at an accuracy competitive with or outperforming human expert level. Methods: We selected >2,600 smears out of our unique archive of > 250,000 peripheral blood smears from hematological neoplasms. Depending on quality, we scanned up to 1,000 single cell images per smear. For image acquisition, a Metafer Scanning System (Zeiss Axio Imager.Z2 microscope, automatic slide feeder and automatic oiling device) from MetaSystems (Altlussheim, GER) was used. Areas of interest were defined by pre-scan in 10x magnification followed by high resolution scan in 40x to generate cell images for analysis. Average capture times for 300/500 cells were 3:43/4:37 min We set up a supervised ML-learning model using colour images (144x144 pixels) as input, outputting predicted probabilities of 21 predefined classes. We used ImageNet-pretrained Xception as our base model. We trained, evaluated and deployed the model using Amazon SageMaker on a subset of 82,974 images randomly selected from 514,183 cells captured and labelled for this study. 20 different cell types and one garbage class were classified. We included cell type categories referring to the critical importance of detecting rare leukemia subtypes (e.g. APL). Numbers of images from respective 21 classes ranged from 1,830 to 14,909 (median: 2,945). Minority classes were up-sampledto handle imbalances. Each picture was labelled by highly skilled technicians (median years practicing in this laboratory: 5) and two independent hematologists (median years at microscope: 20). Results: On a separate test set of 8,297 cells, our classifier was able to predict any of the five cell types occurring in the peripheral blood of healthy individuals (PMN, lymphocytes, monocytes, eosinophils, basophils) at very high median accuracy (97.0%) Median prediction accuracy of 15 rare or pathological cell types was 91.3%. For six critical pathological cell forms (myeloblasts, atypical/bilobulated promyelocytes in APL/APLv, hairy cells, lymphoma cells,plasma cells), median accuracy was 93.4% (sensitivity 93.8%). We saw a very high "T98 accuracy" for these cell types (98.5%) which is the accuracy of cell type predictions with prediction probability >0.98 (achieved in 2231/2417 cases), implicating that critical cells predicted with probability <0.98 should be flagged for human expert validation with priority. For all 21 classes median accuracy was 91.7%. Accuracy was lower for cells representing consecutive steps of maturation, e.g. promyelo-/myelo-/metamyelocytes, reproducing inconsistencies from the human-built phenotypic classification system (s.Fig.). Conclusions: We demonstrate an automated workflow using automatic microscopic cell capturing and ML-driven cell differentiation in samples of hematologic patients. Reproducibility, accuracy, sensitivity and specificity are above 90%, for many cell types above 98%. By flagging suspicious cells for humanvalidation, this tool can support even experienced hematology professionals, especially in detecting rare cell types. Given an appropriate scanning speed, it clearly outperforms human investigators in terms of examination time and number of differentiated cells. An ML-based intelligence can make its skills accessible to hematology laboratories on site or after upload of scanned cell images, independent of time/location. A cloud-based infrastructure is available. A prospective head to head challenge between ML-based classifier and human experts comparing sensitivity and accuracy for detection of all cell classes in peripheral blood will be tested to proof suitability for routine use (NCT 4466059). Figure Disclosures Heo: AWS: Current Employment. Wetton:AWS: Current Employment. Drescher:MetaSystems: Current Employment. Hänselmann:MetaSystems: Current Employment. Lörch:MetaSystems: Current equity holder in private company.

Development of a method for automatic analysis of peripheral blood smears

2020 ◽  
Author(s):  
M.E. Volobueva ◽  
A. V. Alexeevski ◽  
E. V. Sheval ◽  
D. D. Penzar
Keyword(s):  
Peripheral Blood ◽  
Automatic Analysis ◽  
Blood Smears ◽  
Peripheral Blood Smears

scholarly journals Measurement of ovalocyte frequency in peripheral blood smears in defining ovalocytosis in Papua New Guinea

1998 ◽  
Vol 3 (10) ◽  
pp. 809-817 ◽  
Author(s):  
Peter Sapak ◽  
Adrian Sleigh ◽  
Gail Williams ◽  
Wilfred Peter ◽  
Meza Ginny ◽  
...  
Keyword(s):  
Papua New Guinea ◽  
Peripheral Blood ◽  
New Guinea ◽  
Blood Smears ◽  
Peripheral Blood Smears

scholarly journals Anticoagulant induced artefacts in peripheral blood smears

2008 ◽  
Vol 24 (2) ◽  
pp. 43-48 ◽  
Author(s):  
Aparna Narasimha ◽  
Harendra Kumar ◽  
C. S. B. R. Prasad
Keyword(s):  
Peripheral Blood ◽  
Blood Smears ◽  
Peripheral Blood Smears
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