Erythrocytes, Abnormal

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Schistocyte detection in artificial intelligence age.

International journal of laboratory hematology Mar 12, 2024
Schistocytes are fragmented red blood cells produced as a result of mechanical damage to erythrocytes, usually due to microangiopathic thrombotic diseases or mechanical factors. The early laboratory detection of schistocytes has a critical impact on ...
Machine Learning Erythrocytes, Abnormal Humans Artificial Intelligence
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Combining imaging flow cytometry and machine learning for high-throughput schistocyte quantification: A SVM classifier development and external validation cohort.

EBioMedicine Aug 17, 2022
BACKGROUND: Schistocyte counts are a cornerstone of the diagnosis of thrombotic microangiopathy syndrome (TMA). Their manual quantification is complex and alternative automated methods suffer from pitfalls that limit their use. We report a method com...
Flow Cytometry Erythrocytes, Abnormal Support Vector Machine Humans Artificial Intelligence Machine Learning
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Integrating deep learning with microfluidics for biophysical classification of sickle red blood cells adhered to laminin.

PLoS computational biology Nov 29, 2021
Sickle cell disease, a genetic disorder affecting a sizeable global demographic, manifests in sickle red blood cells (sRBCs) with altered shape and biomechanics. sRBCs show heightened adhesive interactions with inflamed endothelium, triggering painfu...
Anemia, Sickle Cell High-Throughput Screening Assays Microfluidics Lab-On-A-Chip Devices Laminin Image Interpretation, Computer-Assisted Biophysical Phenomena Diagnosis, Computer-Assisted Deep Learning In Vitro Techniques Computational Biology Humans Neural Networks, Computer Hemoglobin, Sickle Erythrocytes, Abnormal Erythrocyte Deformability Protein Multimerization
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Rapid diagnosis of hereditary haemolytic anaemias using automated rheoscopy and supervised machine learning.

British journal of haematology Jul 5, 2020
(1)
Erythrocyte Deformability Anemia, Hemolytic, Congenital Anemia, Hemolytic, Congenital Nonspherocytic Hydrops Fetalis Erythrocytes, Abnormal Blood Viscosity Humans Pyruvate Kinase Reticulocytes Single-Cell Analysis Pyruvate Metabolism, Inborn Errors Image Processing, Computer-Assisted Supervised Machine Learning Datasets as Topic Automation
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Healthy and unhealthy red blood cell detection in human blood smears using neural networks.

Micron (Oxford, England : 1993) Feb 1, 2016
One of the most common diseases that affect human red blood cells (RBCs) is anaemia. To diagnose anaemia, the following methods are typically employed: an identification process that is based on measuring the level of haemoglobin and the classificati...
Sensitivity and Specificity Neural Networks, Computer Erythrocytes, Abnormal Image Processing, Computer-Assisted Algorithms Microscopy Humans Erythrocytes
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Applications of deep learning to the assessment of red blood cell deformability.

Biorheology Jan 1, 2021
BACKGROUND: Measurement of abnormal Red Blood Cell (RBC) deformability is a main indicator of Sickle Cell Anemia (SCA) and requires standardized quantification methods. Ektacytometry is commonly used to estimate the fraction of Sickled Cells (SCs) by...
(2)
Deep Learning Erythrocyte Deformability Anemia, Sickle Cell Humans Erythrocytes Erythrocytes, Abnormal
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