AIMC Topic: Leukocytes

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Multiscale deformed attention networks for white blood cell detection.

Scientific reports
White blood cell (WBC) detection is pivotal in medical diagnostics, crucial for diagnosing infections, inflammations, and certain cancers. Traditional WBC detection methods are labor-intensive and time-consuming. Convolutional Neural Networks (CNNs) ...

Clinical subtypes identification and feature recognition of sepsis leukocyte trajectories based on machine learning.

Scientific reports
Sepsis is a highly variable condition, and tracking leukocyte patterns may offer insights for tailored treatment and prognosis. We used the MIMIC-IV database to analyze patients diagnosed with Sepsis-3 within 24 h of ICU admission. Latent class mixed...

High-throughput microfluidics for precise separation and focusing of circulating tumor cells with optimized triangular microchannel design.

Talanta
The precise separation and focusing of circulating tumor cells (CTCs) from blood cells are crucial for advancing cancer diagnosis, optimizing therapeutic strategies, and fostering progress in cellular research. Inertial microfluidics offers an effici...

Comparative Analysis of the Performance of Automated Digital Cell Morphology Analyzers for Leukocyte Differentiation in Hematologic Malignancies: Mindray MC-80 Versus West Medical Vision Hema.

International journal of laboratory hematology
INTRODUCTION: The use of artificial intelligence in hematology laboratories has improved the diagnostic evaluation of peripheral blood cells. The aim of this study is to compare the performance of two automated digital cell morphology analyzers, the ...

Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies.

Progress in neuro-psychopharmacology & biological psychiatry
Schizophrenia is a complex mental disorder. Accurate diagnosis and classification of schizophrenia has always been a major challenge in clinic due to the lack of biomarkers. Therefore, identifying molecular biomarkers, particularly in the peripheral ...

Two-stage CNN-based framework for leukocytes classification.

Computers in biology and medicine
Leukocytes are pivotal markers in health, crucial for diagnosing diseases like malaria and viral infections. Peripheral blood smear tests provide pathologists with vital insights into various medical conditions. Manual leukocyte counting is challengi...

Performance of the automated digital cell image analyzer UIMD PBIA in white blood cell classification: a comparative study with sysmex DI-60.

Clinical chemistry and laboratory medicine
OBJECTIVES: This study aimed to evaluate the performance of PBIA (UIMD, Seoul, Republic of Korea), an automated digital morphology analyzer using deep learning, for white blood cell (WBC) classification in peripheral blood smears and compare it with ...

An Integrated Microfluidic Microwave Array Sensor with Machine Learning for Enrichment and Detection of Mixed Biological Solution.

Biosensors
In this work, an integrated microfluidic microwave array sensor is proposed for the enrichment and detection of mixed biological solution. In individuals with urinary tract infections or intestinal health issues, the levels of white blood cells (WBCs...

Diagnosis and typing of leukemia using a single peripheral blood cell through deep learning.

Cancer science
Leukemia is highly heterogeneous, meaning that different types of leukemia require different treatments and have different prognoses. Current clinical diagnostic and typing tests are complex and time-consuming. In particular, all of these tests rely ...

Comparing Human-Level and Machine Learning Model Performance in White Blood Cell Morphology Assessment.

European journal of haematology
INTRODUCTION: There is an increasing research focus on the role of machine learning in the haematology laboratory, particularly in blood cell morphologic assessment. Human-level performance is an important baseline and goal for machine learning. This...