AI Medical Compendium Topic

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Erythrocytes

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Applications of deep learning to the assessment of red blood cell deformability.

Biorheology
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...

Machine learning for predicting preoperative red blood cell demand.

Transfusion medicine (Oxford, England)
BACKGROUND: The paucity of accurate quantitative standards for determining the quantity of red blood cells (RBCs) needed for perioperative patients and the predominant application of the "preoperative hemoglobin + surgery type" empirical decision-mak...

Digital holographic deep learning of red blood cells for field-portable, rapid COVID-19 screening.

Optics letters
Rapid screening of red blood cells for active infection of COVID-19 is presented using a compact and field-portable, 3D-printed shearing digital holographic microscope. Video holograms of thin blood smears are recorded, individual red blood cells are...

Combining microfluidics with machine learning algorithms for RBC classification in rare hereditary hemolytic anemia.

Scientific reports
Combining microfluidics technology with machine learning represents an innovative approach to conduct massive quantitative cell behavior study and implement smart decision-making systems in support of clinical diagnostics. The spleen plays a key-role...

Automatic identification of malaria and other red blood cell inclusions using convolutional neural networks.

Computers in biology and medicine
Malaria is a serious disease responsible for thousands of deaths each year. Many efforts have been made to aid in the diagnosis of malaria using machine learning techniques, but to date, the presence of other elements that may interfere with the reco...

Single Cell Analysis of Stored Red Blood Cells Using Ultra-High Throughput Holographic Cytometry.

Cells
Holographic cytometry is introduced as an ultra-high throughput implementation of quantitative phase imaging of single cells flowing through parallel microfluidic channels. Here, the approach was applied for characterizing the morphology of individua...

Imaging flow cytometry data analysis using convolutional neural network for quantitative investigation of phagocytosis.

Biotechnology and bioengineering
Macrophages play an important role in the adaptive immune system. Their ability to neutralize cellular targets through Fc receptor-mediated phagocytosis has relied upon immunotherapy that has become of particular interest for the treatment of cancer ...

Assessing red blood cell deformability from microscopy images using deep learning.

Lab on a chip
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...

Increasing a microscope's effective field of view via overlapped imaging and machine learning.

Optics express
This work demonstrates a multi-lens microscopic imaging system that overlaps multiple independent fields of view on a single sensor for high-efficiency automated specimen analysis. Automatic detection, classification and counting of various morpholog...

Deep Learning-Based Phenotypic Assessment of Red Cell Storage Lesions for Safe Transfusions.

IEEE journal of biomedical and health informatics
This study presents a novel approach to automatically perform instant phenotypic assessment of red blood cell (RBC) storage lesion in phase images obtained by digital holographic microscopy. The proposed model combines a generative adversarial networ...