Hematology

Bioinformatic applications in blood group molecular typing.

The application of molecular laboratory methods to predict the expression of red blood cell antigens is well established in today's clinical transfusion practice. The advent of high-throughput molecular blood typing approaches - based on microarray, short-read, and long-read next generation sequencing platforms - has set the stage for widespread, high-resolution donor and patient typing, encompass...

Clinical decision support system for detecting right ventricular dysfunction in acute pulmonary embolism: Explainable a new Binary Rule Search (BRS) ensembles and robustness evaluation.

BACKGROUND: Right-ventricular dysfunction (RVD) in acute pulmonary embolism (PE) carries excess short-term mortality; fast, transparent risk stratification is needed. OBJECTIVE: To develop and evaluate an interpretable Binary Rule Search (BRS) framework for RVD detection on a fully binarized clinical dataset, emphasizing conservative robustness. METHODS: We analyzed a single-center cohort (N = 363...

A nomogram model integrating ultrasound-based multimodal radiomics features and clinical indexes for diagnosing significant hepatic fibrosis in AILD patients.

OBJECTIVE: To develop a prediction model combining radiomics features from 2D ultrasound (2D-US) and shear wave elastography (SWE) with clinical indic...

Artificial Intelligence in Hematology.

Artificial intelligence (AI) has emerged as a powerful resource in healthcare for diagnosis and management. However, successful AI deployment depends ...

Personalizing total quality management strategies for transfusion services: Integrating artificial intelligence, Big Data, and the SoHO framework.

Blood metabolic panels for identifying significant fibrosis and inflammation in patients with MASLD.

Metabolic dysfunction-associated steatotic liver disease (MASLD) remains a prevalent condition with limited diagnostic and therapeutic options. This s...

Predicting postoperative length of stay: a feature selection approach to predictive modeling in lumbar fusion surgery.

BACKGROUND: Predictive modeling has the potential to improve preoperative planning and resource allocation in lumbar fusion surgery. This study aimed ...

Enhancing Glomerular Hematuria Identification in Automated Urinalysis Using a Light Gradient Boosting Machine-Based Model: A Diagnostic Accuracy Study.

BACKGROUND: Glomerular hematuria (GH) is a key parameter assessed during urine analysis that is poorly identified using the automated UF-5000 system, ...

Artificial intelligence and machine learning in thrombosis and hemostasis: a scoping review of clinical and laboratory applications, challenges, and future directions.

This scoping review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guideli...

Cerebral blood flow and modern approaches for clinical assessment & monitoring: a view to the future.

Cerebral blood flow (CBF) is under homeostatic control via cerebral autoregulation, maintaining a constant blood supply to brain parenchyma by integra...

Investigation of droplet dynamics in the hypermonotectic succinonitrile-water system in a temperature gradient and microgravity conditions supported by deep learning computer vision.

We have investigated the liquid-liquid phase separation from a homogeneous liquid phase and the subsequent liquid droplet phase evolution upon cooling...

Explainable machine learning model for predicting early recurrence and distant metastasis after surgery in early-onset colorectal cancer.

OBJECTIVE: To develop explainable machine learning models for predicting the risk of early postoperative recurrence and distant metastasis in patients...

Evaluation of a Semi-Supervised AI Model (ASUS Blade) for Peripheral Blood Film Leukocyte Classification.

BACKGROUND: The peripheral blood film (PBF) analysis traditionally relies on manual microscopy (MM), a labour-intensive method with inter-observer var...

Sociodemographic Predictors of Child-Parent Agreement on Oral Health.

BACKGROUND: Oral health is vital for children's overall well-being. Parents play a critical role in shaping children's oral health through preventive ...

Comparative Performance of Machine Learning and Traditional Risk Scores in Predicting Adverse Events After Transcatheter Aortic Valve Replacement in Patients With Atrial Fibrillation.

Patients with atrial fibrillation (AF) following transcatheter aortic valve replacement (TAVR) remain at risk of ischemic stroke (IS) and bleeding. Ho...

Creating Solutions to the Challenges in Managing Heparin Infusions: Exploring Use of Technology Including Artificial Intelligence and the Role of Anticoagulation Stewardship.

Adverse events and medication-related errors are common with anticoagulants. Heparin infusions are frequently associated with administration errors or...

Sustainable silk fibroin scaffolds for bone repair: assessing their osteogenic potential via AI-enhanced synchrotron imaging workflow.

Effective bone regeneration requires scaffolds capable of guiding and supporting new mineralized matrix formation. In this study, silk fibroin constru...

Consensus machine learning identifies cell death gene signature for carotid artery stenosis diagnosis.

Carotid artery stenosis (CAS) is a major contributor to ischemic stroke, and molecular tools for its early detection remain limited. To address this n...

Development of Automated High-Throughput Digital Microscopy With Deep Learning for Enhanced Blood Smear Imaging.

A microscope is essential in scientific and medical research, enabling the magnification of specimens too small for the naked eye. The conventional me...

A Simple Laboratory-based Machine Learning Model Accurately Predicts Advanced Liver Fibrosis in Metabolic Dysfunction-associated Steatotic Liver Disease Patients.

BACKGROUND/AIM: Metabolic dysfunction-associated steatotic liver disease (MASLD) is often silent and progressive, affecting nearly one-fourth of the g...