Hematology

Predicting blood pressure without a cuff using a unique multi-modal wearable device and machine learning algorithm.

Blood pressure is a critical risk factor for cardiovascular diseases (CVDs), yet most adults do not ...

Corrigendum to "Towards fast and reliable estimations of 3D pressure, velocity and wall shear stress in aortic blood flow: CFD-based machine learning approach" [Comput. Biol. Med. 191 (2025) 110137].

Machine learning assisted media optimization for enhanced insulin production in Pseudomonas fluorescens cell factory and scale-up studies.

Diabetes mellitus, a chronic metabolic disorder, is characterized by high blood glucose levels. Exte...

Spotlights on novel strategic innovations on the artificial intelligence and deep learning driven quality control focuses in transfusion medicine, to optimize blood component safety and efficacy and minimize the potential pitfalls.

Artificial intelligence (AI) combined with human intelligent, and machine learning (ML) are transfor...

EB-YOLO:An efficient and lightweight blood cell detector based on the YOLO algorithm.

Blood cell detection is an important part of medical diagnosis. Object detection is trending for blo...

GeneDX-PBMC: An adversarial autoencoder framework for unlocking Alzheimer's disease biomarkers using blood single-cell RNA sequencing data.

OBJECTIVE: To identify blood-based biomarkers and therapeutic targets for Alzheimer's disease (AD) b...

MEF-Net: Multi-scale and edge feature fusion network for intracranial hemorrhage segmentation in CT images.

Intracranial Hemorrhage (ICH) refers to cerebral bleeding resulting from ruptured blood vessels with...

Leveraging artificial intelligence chatbots for anemia prevention: A comparative study of ChatGPT-3.5, copilot, and Gemini outputs against Google Search results.

AIM: This study evaluated the understandability, actionability, and readability of text on anemia ge...

Towards fast and reliable estimations of 3D pressure, velocity and wall shear stress in aortic blood flow: CFD-based machine learning approach.

In this work, we developed deep neural networks for the fast and comprehensive estimation of the mos...

Evaluation of blood- and urine-derived biomarkers for machine learning prediction models of osteoarthritis in elderly patients: A feasibility study.

BACKGROUND: Osteoarthritis (OA) is a common degenerative joint disease, particularly affecting indiv...

Establishment of a machine learning-based prediction framework to assess trade-offs in decisions that affect post-HCT outcomes.

In this study, we propose a conceptual framework of decision support tools, built upon machine learn...

Machine learning-based detection and quantification of red blood cells in Cholistani cattle: A pilot study.

This study presents the first account of using machine learning to detect and count normal and abnor...

Real-Time Precision Tracking System in Periprosthetic Acetabular Osteotomy With Osteotome Chisel Elastic Deformation Consideration.

The periprosthetic acetabular osteotomy (PAO) is a commonly used technique in orthopedics for treati...

Machine learning-based prediction of hearing loss: Findings of the US NHANES from 2003 to 2018.

The prevalence of hearing loss (HL) has emerged as an escalating public health concern globally. The...

Machine learning models based on a national-scale cohort accurately identify patients at high risk of deep vein thrombosis following primary total hip arthroplasty.

BACKGROUND: The occurrence of deep venous thrombosis (DVT) following total hip arthroplasty (THA) po...

A Physics-Integrated Deep Learning Approach for Patient-Specific Non-Newtonian Blood Viscosity Assessment using PPG.

BACKGROUND AND OBJECTIVE: The aim of this study is to extract a patient-specific viscosity equation ...

Biomedical Big Data and Artificial Intelligence in Blood.

Machine learning based prediction of cognitive metrics using major biomarkers in SuperAgers.

As populations age, understanding cognitive decline and age-related diseases like dementia has becom...

A machine learning and centrifugal microfluidics platform for bedside prediction of sepsis.

Sepsis is a life-threatening organ dysfunction due to a dysfunctional response to infection. Delays ...

Interpretable machine learning for predicting optimal surgical timing in polytrauma patients with TBI and fractures to reduce postoperative infection risk.

This retrospective study leverages machine learning to determine the optimal timing for fracture rec...

A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remai...