AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Erythrocytes

Showing 11 to 20 of 91 articles

Clear Filters

Blood quality evaluation on-chip classification of cell morphology using a deep learning algorithm.

Lab on a chip
The quality of red blood cells (RBCs) in stored blood has a direct impact on the recovery of patients treated by blood transfusion, which directly reflects the quality of blood. The traditional means for blood quality evaluation involve the use of re...

An automated malaria cells detection from thin blood smear images using deep learning.

Tropical biomedicine
Timely and rapid diagnosis is crucial for faster and proper malaria treatment planning. Microscopic examination is the gold standard for malaria diagnosis, where hundreds of millions of blood films are examined annually. However, this method's effect...

Quantitative phase imaging of living red blood cells combining digital holographic microscopy and deep learning.

Journal of biophotonics
Digital holographic microscopy as a non-contacting, non-invasive, and highly accurate measurement technology, is becoming a valuable method for quantitatively investigating cells and tissues. Reconstruction of phases from a digital hologram is a key ...

Biophysical Profiling of Sickle Cell Disease Using Holographic Cytometry and Deep Learning.

International journal of molecular sciences
Sickle cell disease (SCD) is an inherited hematological disorder associated with high mortality rates, particularly in sub-Saharan Africa. SCD arises due to the polymerization of sickle hemoglobin, which reduces flexibility of red blood cells (RBCs),...

High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system.

Scientific reports
Accurate identification and classification of bone marrow (BM) nucleated cell morphology are crucial for the diagnosis of hematological diseases. However, the subjective and time-consuming nature of manual identification by pathologists hinders promp...

Diagnosis of diabetes mellitus using high frequency ultrasound and convolutional neural network.

Ultrasonics
The incidence of diabetes mellitus has been increasing, prompting the search for non-invasive diagnostic methods. Although current methods exist, these have certain limitations, such as low reliability and accuracy, difficulty in individual patient a...

Leveraging deep learning for detecting red blood cell morphological changes in blood films from children with severe malaria anaemia.

British journal of haematology
In sub-Saharan Africa, acute-onset severe malaria anaemia (SMA) is a critical challenge, particularly affecting children under five. The acute drop in haematocrit in SMA is thought to be driven by an increased phagocytotic pathological process in the...

Efficient deep learning-based approach for malaria detection using red blood cell smears.

Scientific reports
Malaria is an extremely malignant disease and is caused by the bites of infected female mosquitoes. This disease is not only infectious among humans, but among animals as well. Malaria causes mild symptoms like fever, headache, sweating and vomiting,...

Pursuing the elusive footsteps of malaria in peripheral blood smears utilizing artificial intelligence.

British journal of haematology
For over a century, the need to identify malaria in the peripheral blood has been the driving force behind the development of fundamental clinical microscopy techniques. In the study by Moysis et al., artificial intelligence-based model was utilized ...

Image cropping for malaria parasite detection on heterogeneous data.

Journal of microbiological methods
Malaria is a deadly disease of significant concern for the international community. It is an infectious disease caused by a Plasmodium spp. parasite and transmitted by the bite of an infected female Anopheles mosquito. The parasite multiplies in the ...