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@article{180192,
        author = {Shiva Das and Md. Salman Rahi and Raja Kumar and Adil Anwer},
        title = {Automated Analysis of White Blood Cells: Technological Advances and Diagnostic Accuracy},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {12},
        number = {1},
        pages = {235-240},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=180192},
        abstract = {White blood cells (WBCs) are central to the body’s immune response and serve as key biomarkers in diagnosing infections, hematologic malignancies, and inflammatory diseases. Traditional manual methods for WBC analysis, while clinically valuable, are time-consuming, subjective, and limited in throughput. Recent technological advances have revolutionized WBC analysis by introducing automated systems that enhance speed, reproducibility, and diagnostic accuracy. This review explores the evolution of WBC analysis, from manual microscopy to sophisticated automated hematology analyzers, flow cytometry, digital imaging, and artificial intelligence (AI)-driven platforms. Modern 5-part differential analyzers, digital smear systems (e.g., CellaVision), and machine learning algorithms have demonstrated significant improvements in leukocyte classification and anomaly detection. Clinical applications in infections, leukemias, and marrow disorders highlight the diagnostic value of these innovations. Despite clear advantages in efficiency and consistency, challenges such as cost, over-flagging, and limited interpretability in abnormal cases remain. Ongoing research in AI and integration with digital pathology offers promising solutions. Automation is reshaping the future of hematology, supporting precision diagnostics and data-driven healthcare.},
        keywords = {WBCs, hematologic malignancies, inflammatory diseases, flow cytometry, digital imaging, digital pathology, leukocyte},
        month = {May},
        }