AIMC Topic: Leukocytes
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Domain knowledge-infused pre-trained deep learning models for efficient white blood cell classification.
Scientific reports
May 4, 2025
White blood cell (WBC) classification is a crucial step in assessing a patient's health and validating medical treatment in the medical domain. Hence, efficient computer vision solutions to the classification of WBC will be an effective aid to medica...
Multiscale deformed attention networks for white blood cell detection.
Scientific reports
Apr 26, 2025
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
Apr 10, 2025
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...
Identifying periphery biomarkers of first-episode drug-naïve patients with schizophrenia using machine-learning-based strategies.
Progress in neuro-psychopharmacology & biological psychiatry
Feb 25, 2025
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
Feb 5, 2025
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
Jan 23, 2025
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
Jan 13, 2025
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
Nov 18, 2024
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
Oct 6, 2024
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...
Association of retinal image-based, deep learning cardiac BioAge with telomere length and cardiovascular biomarkers.
Optometry and vision science : official publication of the American Academy of Optometry
Jun 28, 2024
SIGNIFICANCE: Our retinal image-based deep learning (DL) cardiac biological age (BioAge) model could facilitate fast, accurate, noninvasive screening for cardiovascular disease (CVD) in novel community settings and thus improve outcome with those wit...
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