Hematology

Deep Learning-Based Model for Non-invasive Hemoglobin Estimation via Body Parts Images: A Retrospective Analysis and a Prospective Emergency Department Study.

Anemia is a significant global health issue, affecting over a billion people worldwide, according to the World Health Organization. Generally, the gold standard for diagnosing anemia relies on laboratory measurements of hemoglobin. To meet the need in clinical practice, physicians often rely on visual examination of specific areas, such as conjunctiva, to assess pallor. However, this method is sub...

An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping.

Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characterizing a patient-specific blood input function, traditionally reliant on invasive arterial blood sampling. This research introduces a novel approach empl...

Chemical analogue based drug design for cancer treatment targeting PI3K: integrating machine learning and molecular modeling.

Cancer is a generic term for a group of disorders defined by uncontrolled cell growth and the potential to invade or spread to other parts of the body...

The application of blood flow sound contrastive learning to predict arteriovenous graft stenosis of patients with hemodialysis.

End-stage kidney disease (ESKD) presents a significant public health challenge, with hemodialysis (HD) remaining one of the most prevalent kidney repl...

Machine learning-based identification of the risk factors for postoperative nausea and vomiting in adults.

Postoperative nausea and vomiting (PONV) is a common adverse effect of anesthesia. Identifying risk factors for PONV is crucial because it is associat...

Structured adaptive boosting trees for detection of multicellular aggregates in fluorescence intravital microscopy.

Fluorescence intravital microscopy captures large data sets of dynamic multicellular interactions within various organs such as the lungs, liver, and ...

Artificial intelligence reveals the predictions of hematological indexes in children with acute leukemia.

Childhood leukemia is a prevalent form of pediatric cancer, with acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML) being the primary...

Coagulation Risk Predicting in Anticoagulant-Free Continuous Renal Replacement Therapy.

INTRODUCTION: Continuous renal replacement therapy (CRRT) is a prolonged continuous extracorporeal blood purification therapy to replace impaired rena...

Comparative analysis of feature selection techniques for COVID-19 dataset.

In the context of early disease detection, machine learning (ML) has emerged as a vital tool. Feature selection (FS) algorithms play a crucial role in...

The synchronous upregulation of a specific protein cluster in the blood predicts both colorectal cancer risk and patient immune status.

BACKGROUND: Early detection and treatment of colorectal cancer (CRC) is crucial for improving patient survival rates. This study aims to identify sign...

Stress Classification and Vital Signs Forecasting for IoT-Health Monitoring.

Health monitoring embedded with intelligence is the demand of the day. In this era of a large population with the emergence of a variety of diseases, ...

Hierarchical Hybrid Networks for Automatic Pulmonary Blood Vessel Segmentation in Computed Tomography Images.

Pulmonary arterial hypertension (PAH) is considered the third most common cardiovascular disease after coronary heart disease and hypertension. The di...

Identification of Active Molecules against Thrombocytopenia through Machine Learning.

Thrombocytopenia, which is associated with thrombopoietin (TPO) deficiency, presents very limited treatment options and can lead to life-threatening c...

Machine Learning-Driven Biomarker Discovery for Skeletal Complications in Type 1 Gaucher Disease Patients.

Type 1 Gaucher disease (GD1) is a rare, autosomal recessive disorder caused by glucocerebrosidase deficiency. Skeletal manifestations represent one of...

Non-Contact Blood Pressure Estimation From Radar Signals by a Stacked Deformable Convolution Network.

This study introduces a contactless blood pressure monitoring approach that combines conventional radar signal processing with novel deep learning arc...

Establishing reflex test rules for platelet fluorescent counting method using machine learning models on Sysmex XN-series hematology analyzer.

INTRODUCTION: The platelet fluorescent counting (PLT-F) method is utilized as a reflex test method following the initial test of the platelet impedanc...

Could machine learning revolutionize how we treat immune thrombocytopenia?

The absence of reliable biomarkers in immune thrombocytopenia (ITP) complicates treatment choice, necessitating a trial-and-error approach. Machine le...

Machine learning-driven diagnosis of multiple sclerosis from whole blood transcriptomics.

Multiple sclerosis (MS) is a neurological disorder characterized by immune dysregulation. It begins with a first clinical manifestation, a clinically ...

Soul: An OCTA dataset based on Human Machine Collaborative Annotation Framework.

Branch retinal vein occlusion (BRVO) is the most prevalent retinal vascular disease that constitutes a threat to vision due to increased venous pressu...

Early predictive values of clinical assessments for ARDS mortality: a machine-learning approach.

Acute respiratory distress syndrome (ARDS) is a devastating critical care syndrome with significant morbidity and mortality. The objective of this stu...