Detecting Abnormalities in Blood Cells by Using Different Modern Techniques: A Comprehensive Review
DOI:
https://doi.org/10.52783/jns.v14.2739Keywords:
Red Blood Cells, White Blood Cells, Platelets, Convolutional neural networks (CNN), Thalassemia, Thrombocytopenia, Hemophilia, HematologistsAbstract
Blood is the life maintaining fluid that flows through the body's blood vessels Arteries, Veins and Capillaries. Blood cell abnormalities, encompassing alterations in the size, shape, count, color, texture or functionality of red blood cells (RBCs), white blood cells (WBCs), platelets and Plasma are critical indicators of various hematological and systemic diseases. Accurate and early detection of these abnormalities plays a significant role for timely diagnosis, prognosis, and treatment of conditions such as Anemia, Leukemia, Thalassemia, Thrombocytopenia, Hemophilia, Polycythemia vera, Plasma cell myeloma and other infections. In recent years, advancements in modern diagnostic techniques have significantly enhanced our ability to identify and characterize these abnormalities with high Accuracy, Sensitivity and Precision. This review highlights contemporary methodologies employed in detecting blood cell abnormalities, emphasizing their principles, applications and clinical relevance. Fully Automated complete blood count (CBC) analyzers, Artificial intelligence and machine learning techniques and Single cell sequencing technology have revolutionized routine hematology by providing rapid and quantitative as well as qualitative insights into blood cell parameters without much necessity of Hematologists. Modern blood cell analysis techniques have significantly advanced, combining automation, precision, and novel technologies to enhance diagnostic capabilities. Automated hematology analyzers employ methods like flow cytometry, impedance technology, and optical scattering for efficient cell counting and classification. Advanced tools, such as microfluidics, Raman spectroscopy, and single-cell RNA sequencing, enable detailed molecular and cellular profiling. Emerging approaches like imaging flow cytometry, laser-induced breakdown spectroscopy, and mass cytometry further improve the detection of rare and abnormal cells. Integrating artificial intelligence and machine learning has streamlined blood smear analysis and enhanced anomaly detection. These innovations provide faster, more accurate results, paving the way for improved diagnostics and personalized medicine.
The Primary purpose of this Literature Review is to assist readers in understanding the whole body of available different modern techniques in determination of abnormal blood cells.
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