Hematology

Development of Machine-learning Model to Predict Anticoagulant Use and Type in Geriatric Traumatic Brain Injury Using Coagulation Parameters.

This study aimed to investigate the patterns of anticoagulation therapy and coagulation parameters and to develop a prediction model to predict the type of anticoagulation therapy in geriatric patients with traumatic brain injury. A retrospective analysis was performed using the nationwide neurotrauma database of Japan. Elderly patients (≥65 years) with traumatic brain injury. Patients were divide...

Breath Analyzer for Real-Time Exercise Fat Burning Prediction: Oral and Alveolar Breath Insights with CNN.

The increasing prevalence of obesity and metabolic disorders has created a significant demand for personalized devices that can effectively monitor fat metabolism. In this study, we developed an advanced breath analyzer system designed to provide real-time monitoring of exercise-induced fat burning by analyzing volatile organic compounds (VOCs) present in both oral and alveolar breath. Acetone in ...

Robust and interpretable deep learning system for prognostic stratification of extranodal natural killer/T-cell lymphoma.

PURPOSE: Extranodal natural killer/T-cell lymphoma (ENKTCL) is an hematologic malignancy with prognostic heterogeneity. We aimed to develop and valida...

Robotic-assisted versus conventional hip arthroplasty: a comparative analysis of perioperative blood management and early outcomes.

PURPOSE: This study aimed to evaluate the impact of Robotic-Assisted Total Hip Arthroplasty (RATHA) versus Conventional Total Hip Arthroplasty (CTHA) ...

Blood loss in one-stage bilateral total knee arthroplasty: cruciate-retaining vs. posterior stabilized. A propensity score-matched analysis.

INTRODUCTION: Although single-stage bilateral total knee arthroplasty (BTKA) presents several advantages, higher perioperative blood loss is a potenti...

Accurate prediction of bleeding risk after coronary artery bypass grafting with dual antiplatelet therapy: A machine learning model vs. the PRECISE-DAPT score.

BACKGROUND: Dual antiplatelet therapy (DAPT) after coronary artery bypass grafting (CABG), although might be protective for ischemic events, can lead ...

Early detection of high blood pressure from natural speech sounds with graph diffusion network.

This study presents an innovative approach to cuffless blood pressure prediction by integrating speech and demographic features. With a focus on non-i...

Application of machine learning methods for predicting esophageal variceal bleeding in patients with cirrhosis.

OBJECTIVE: To develop and compare machine learning models based on CT morphology features, serum biomarkers, and basic physical conditions to predict ...

Predicting Bleeding Related Events in Robotic-Assisted Partial Nephrectomy for Angiomyolipoma: Simplifying Risk Assessment with Tumor Diameter and Depth, A Retrospective Study.

BACKGROUND: Evaluating risk factors for bleeding events in robot-assisted partial nephrectomy (RAPN) for renal angiomyolipoma (RAML) is essential for ...

Programmable ultrasound-mediated swarms manipulation of bacteria-red blood cell microrobots for tumor-specific thrombosis and robust photothermal therapy.

Despite the excellent advantages of biomicrorobots, such as autonomous navigation and targeting actuation, effective penetration and retention to deep...

Pinning down the accuracy of physics-informed neural networks under laminar and turbulent-like aortic blood flow conditions.

BACKGROUND: Physics-informed neural networks (PINNs) are increasingly being used to model cardiovascular blood flow. The accuracy of PINNs is dependen...

Very low doses of rituximab in autoimmune hemolytic anemia-an open-label, phase II pilot trial.

INTRODUCTION: Although rituximab is approved for several autoimmune diseases, no formal dose finding studies have been conducted. The amount of CD20+ ...

A noninvasive blood glucose detection method with strong time adaptability based on fuzzy operator decision fusion and dynamic spectroscopy characteristics of PPG signals.

PPG signals are a new means of non-invasive detection of blood glucose, but there are still shortcomings of poor time adaptability and low prediction ...

An explainable and supervised machine learning model for prediction of red blood cell transfusion in patients during hip fracture surgery.

AIM: The study aimed to develop a predictive model with machine learning (ML) algorithm, to predict and manage the need for red blood cell (RBC) trans...

Deep learning detected histological differences between invasive and non-invasive areas of early esophageal cancer.

The depth of invasion plays a critical role in predicting the prognosis of early esophageal cancer, but the reasons behind invasion and the changes oc...

Actuation-Mediated Compression of a Mechanoresponsive Hydrogel by Soft Robotics to Control Release of Therapeutic Proteins.

Therapeutic proteins, the fastest growing class of pharmaceuticals, are subject to rapid proteolytic degradation in vivo, rendering them inactive. Sop...

A potential predictive model based on machine learning and CPD parameters in elderly patients with aplastic anemia and myelodysplastic neoplasms.

BACKGROUND: Aplastic anemia (AA) and myelodysplastic neoplasms (MDS) have similar peripheral blood manifestations and are clinically characterized by ...

Predicting blood transfusion demand in intensive care patients after surgery by comparative analysis of temporally extended data selection.

BACKGROUND: Blood transfusion (BT) is a critical aspect of medical care for surgical patients in the Intensive Care Unit (ICU). Timely and accurate id...

Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context.

BACKGROUND: Anemia during pregnancy is a significant public health concern, particularly in resource-limited settings. Machine learning (ML) offers pr...

Standardized evaluation of diabetic retinopathy using artificial intelligence and its association with metabolic dysfunction-associated steatotic liver disease in Japan: A cross-sectional study.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is common in patients with obesity and diabetes and can lead to serious complications...