AIMC Topic: Erythrocytes
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Image cropping for malaria parasite detection on heterogeneous data.
Journal of microbiological methods
Aug 20, 2024
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 ...
Pursuing the elusive footsteps of malaria in peripheral blood smears utilizing artificial intelligence.
British journal of haematology
Jul 8, 2024
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 ...
Leveraging deep learning for detecting red blood cell morphological changes in blood films from children with severe malaria anaemia.
British journal of haematology
Jun 18, 2024
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
Jun 10, 2024
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,...
Diagnosis of diabetes mellitus using high frequency ultrasound and convolutional neural network.
Ultrasonics
Sep 21, 2023
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...
High-accuracy morphological identification of bone marrow cells using deep learning-based Morphogo system.
Scientific reports
Aug 17, 2023
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...
Biophysical Profiling of Sickle Cell Disease Using Holographic Cytometry and Deep Learning.
International journal of molecular sciences
Jul 25, 2023
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),...
Quantitative phase imaging of living red blood cells combining digital holographic microscopy and deep learning.
Journal of biophotonics
Jun 23, 2023
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 ...
Blood quality evaluation on-chip classification of cell morphology using a deep learning algorithm.
Lab on a chip
Apr 12, 2023
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...
Classification of red cell dynamics with convolutional and recurrent neural networks: a sickle cell disease case study.
Scientific reports
Jan 13, 2023
The fraction of red blood cells adopting a specific motion under low shear flow is a promising inexpensive marker for monitoring the clinical status of patients with sickle cell disease. Its high-throughput measurement relies on the video analysis of...
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