Hematology

MS-YOLO: A Multi-Scale Model for Accurate and Efficient Blood Cell Detection

Complete blood cell detection holds significant value in clinical diagnostics. Conventional manual microscopy methods suffer from time inefficiency and diagnostic inaccuracies. Existing automated detection approaches remain constrained by high deployment costs and suboptimal accuracy. While deep learning has introduced powerful paradigms to this field, persistent challenges in detecting overlapp...

Aryl Organophosphate Esters and Hemostatic Disruption: Identifying Risk through Machine Learning and Experimental Validation.

Organophosphate esters (OPEs) have emerged as a significant environmental concern due to their widespread occurrence and potential human health risks. The presence of OPEs in human blood suggests direct interactions with hematological components, which may compromise hemostatic balance and lead to adverse health outcomes. Despite the critical role of hemostatic balance in maintaining blood stabili...

Predicting Blood Type: Assessing Model Performance with ROC Analysis

Introduction: Personal identification is a critical aspect of forensic sciences, security, and healthcare. While conventional biometrics systems suc...

Revolutionizing Blood Banks: AI-Driven Fingerprint-Blood Group Correlation for Enhanced Safety

Identification of a person is central in forensic science, security, and healthcare. Methods such as iris scanning and genomic profiling are more ac...

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 treating developmental hip dysplasia and hip dislocation...

Enhanced Early Detection of Colorectal Cancer via Blood Biomarker Combinations Identified Through Extracellular Vesicle Isolation and Artificial Intelligence Analysis.

Colorectal cancer (CRC) remains a major cause of cancer-related deaths worldwide, with early detection being crucial for improving survival rates. Des...

Revolutionizing breast cancer monitoring: emerging hematocrit-based metrics - a narrative review.

Breast cancer remains a leading global health concern, with significant strides made in early detection and treatment. However, effective long-term su...

Prediction of enhanced coagulation with varied pre-oxidations for seasonal variations of cyanobacteria-dominated algae-laden water quality.

Enhanced coagulation with pre-oxidation is a cost-effective approach for managing seasonal water quality pollution caused by algal outbreaks and death...

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) poses a substantial risk of morbidity and mortality,...

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 within the brain. Delayed and inaccurate diagnosis and...

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) by leveraging single-cell RNA sequencing (scRNA-seq...

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 blood cell analysis, with research focusing on high-p...

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 monitor it frequently enough to prevent serious co...

[Rapid identification and analysis of hemoglobin isoelectric focusing electrophoresis images based on deep learning].

Gel electrophoresis is used to separate and analyze macromolecules （such as DNA， RNA， and proteins） and their fragments， and highly reproducible and e...

Diagnostic Stewardship of Blood Cultures in the Pediatric ICU Using Machine Learning.

OBJECTIVE: The medical community recently experienced a severe shortage of blood culture media bottles. Rates of blood stream infection (BSI) among cr...

Machine Learning Model to Guide Empirical Antimicrobial Therapy in Febrile Neutropenic Patients With Hematologic Malignancies.

BACKGROUND/AIM: Optimal antimicrobial selection for patients with febrile neutropenia (FN) may differ depending on the underlying mechanisms. We aimed...

Machine-learning models to predict iron recovery after blood donation: a model development and external validation study.

BACKGROUND: Machine-learning models directly predicting iron biomarkers after blood donation could help to manage donation-associated iron deficiency ...

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 abnormal red blood cells (RBCs), including tear-drop ce...

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 learning and multi-objective optimization, aimed at off...

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 from photoplethysmography (PPG) data. An aging soc...