AIMC Topic: Leukocyte Count

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Machine learning classification of inflammatory bowel disease activity using white blood cell subsets.

BMJ open gastroenterology
OBJECTIVE: The lack of a rapid, validated, consistent test for tracking disease activity in patients with inflammatory bowel disease (IBD) is currently a major challenge. Currently used biomarkers have notable disadvantages, such as the slow processi...

Machine learning based analysis of leucocyte cell population data by Sysmex XN series hematology analyzer for the diagnosis of bacteremia.

Scientific reports
In clinical practice, early recognition of bacteremia leads to prognostic improvement. Recently, cell population data (CPD) from the Sysmex XN-series hematology analyzer has attracted attention as a new method for the early diagnosis of bacteremia, b...

Leukocyte analysis: Current status and future direction.

Clinica chimica acta; international journal of clinical chemistry
Leukocytes (white blood cells, WBCs) are a vital component of the human immune system, responsible for resisting foreign pathogens, repairing damaged tissues, and regulating immune responses. Abnormal changes in their number and function serve as cli...

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...

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 Efficient Acute Lymphoblastic Leukemia Screen Framework Based on Multi-Modal Deep Neural Network.

International journal of laboratory hematology
BACKGROUND: Acute lymphoblastic leukemia (ALL) is a leading cause of death among pediatric malignancies. Early diagnosis of ALL is crucial for minimizing misdiagnosis, improving survival rates, and ensuring the implementation of precise treatment pla...

Prediction of the risk of mortality in older patients with coronavirus disease 2019 using blood markers and machine learning.

Frontiers in immunology
INTRODUCTION: The mortality rate among older people infected with severe acute respiratory syndrome coronavirus 2 is alarmingly high. This study aimed to explore the predictive value of a novel model for assessing the risk of death in this vulnerable...

Using random forest and biomarkers for differentiating COVID-19 and Mycoplasma pneumoniae infections.

Scientific reports
The COVID-19 pandemic has underscored the critical need for precise diagnostic methods to distinguish between similar respiratory infections, such as COVID-19 and Mycoplasma pneumoniae (MP). Identifying key biomarkers and utilizing machine learning t...

Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality.

Scientific reports
Understanding the combined effects of risk factors on all-cause mortality is crucial for implementing effective risk stratification and designing targeted interventions, but such combined effects are understudied. We aim to use survival-tree based ma...