AIMC Topic: Erythrocyte Count

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Assessment of anemia recovery using peripheral blood smears by deep semi-supervised learning.

Annals of hematology Feb 21, 2025
Monitoring anemia recovery is crucial for clinical intervention. Morphological assessment of red blood cells (RBCs) with peripheral blood smears (PBSs) provides additional information beyond routine blood tests. However, the PBS test is labor-intensi...
Erythrocytes Erythrocyte Count Mice Supervised Machine Learning Humans Anemia Animals Male Deep Learning
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Differential diagnosis of iron deficiency anemia from aplastic anemia using machine learning and explainable Artificial Intelligence utilizing blood attributes.

Scientific reports Jan 2, 2025
As per world health organization, Anemia is a most prevalent blood disorder all over the world. Reduced number of Red Blood Cells or decrease in the number of healthy red blood cells is considered as Anemia. This condition also leads to the decrease ...
Artificial Intelligence Machine Learning Female Humans Erythrocyte Count Anemia, Aplastic Hematologic Tests Anemia, Iron-Deficiency Adult Erythrocyte Indices Male Diagnosis, Differential
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Assessing red blood cell deformability from microscopy images using deep learning.

Lab on a chip Dec 21, 2021
Red blood cells (RBCs) must be highly deformable to transit through the microvasculature to deliver oxygen to tissues. The loss of RBC deformability resulting from pathology, natural aging, or storage in blood bags can impede the proper function of t...
Deep Learning Microscopy Erythrocytes Humans Erythrocyte Count Erythrocyte Deformability
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Quantitative analysis of blood cells from microscopic images using convolutional neural network.

Medical & biological engineering & computing Jan 1, 2021
Blood cell count provides relevant clinical information about different kinds of disorders. Any deviation in the number of blood cells implies the presence of infection, inflammation, edema, bleeding, and other blood-related issues. Current microscop...
Blood Cells Erythrocyte Count Neural Networks, Computer Image Processing, Computer-Assisted Microscopy
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Evaluation of machine learning-driven automated Kleihauer-Betke counting: A method comparison study.

International journal of laboratory hematology Nov 4, 2020
INTRODUCTION: The Kleihauer-Betke (KB) test is the diagnostic standard for the quantification of fetomaternal hemorrhage (FMH). Manual analysis of KB slides suffers from inter-observer and inter-laboratory variability and low efficiency. Flow cytomet...
Flow Cytometry Erythrocyte Count Fetomaternal Transfusion Machine Learning Pregnancy Female Humans
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Multilevel approach to male fertility by machine learning highlights a hidden link between haematological and spermatogenetic cells.

Andrology Jun 21, 2020
BACKGROUND: Male infertility represents a complex clinical condition requiring an accurate multilevel assessment, in which machine learning technology, combining large data series in non-linear and highly interactive ways, could be innovatively appli...
Humans Machine Learning Algorithms Databases, Factual Retrospective Studies Lymphocyte Count Spermatogenesis Erythrocyte Count Longitudinal Studies Female Semen Analysis Adult Hematopoiesis Infertility, Male Male Environment
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Purification of viable peripheral blood mononuclear cells for biobanking using a robotized liquid handling workstation.

Journal of translational medicine Nov 12, 2019
BACKGROUND: The purification of peripheral blood mononuclear cells (PBMCs) by means of density gradient (1.07 g/mL) centrifugation is one of the most commonly used methods in diagnostics and research laboratories as well as in biobanks. Here, we eval...
Cell Separation Preservation, Biological Erythrocyte Count Specimen Handling Biological Specimen Banks Erythrocytes Automation Aged Aged, 80 and over Adult Humans Female Male Reproducibility of Results Middle Aged Cell Survival Leukocytes, Mononuclear Robotics Lymphocytes
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Predicting Long-Term Mortality after Acute Coronary Syndrome Using Machine Learning Techniques and Hematological Markers.

Disease markers Jan 30, 2019
INTRODUCTION: Hematological indices including red cell distribution width and neutrophil to lymphocyte ratio are proven to be associated with outcomes of acute coronary syndrome. The usefulness of machine learning techniques in predicting mortality a...
Male Middle Aged Hospital Mortality Acute Coronary Syndrome Lymphocyte Count Erythrocyte Count Female Aged Survival Rate Humans Machine Learning Biomarkers
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Machine learning-based detection and quantification of red blood cells in Cholistani cattle: A pilot study.

Research in veterinary science Jun 1, 2025
This study presents the first account of using machine learning to detect and count normal and abnormal red blood cells (RBCs), including tear-drop cells and schistocytes, in Cholistani cattle from Pakistan. A Support Vector Machine (SVM) model was a...
Machine Learning Support Vector Machine Pakistan Erythrocytes Animals Erythrocyte Count Pilot Projects Cattle
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Automatic detection and characterization of quantitative phase images of thalassemic red blood cells using a mask region-based convolutional neural network.

Journal of biomedical optics Nov 1, 2020
SIGNIFICANCE: Label-free quantitative phase imaging is a promising technique for the automatic detection of abnormal red blood cells (RBCs) in real time. Although deep-learning techniques can accurately detect abnormal RBCs from quantitative phase im...
Erythrocyte Count Holography Neural Networks, Computer Erythrocytes
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