Hematology

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 objective of this study was to leverage data from the National Health and Nutritional Examination Survey (NHANES) to develop an interpretable predictive machine learning (ML) model for HL. In accordance with the established inclusion and exclusion criteria, a total of 2814 participants were randoml...

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 generated by artificial intelligence (AI) chatbots.

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 transforming quality control (QC) in transfusion medicine,...

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 become increasingly important. "SuperAgers," individual...

Developing hybrid mechanistic and data-driven personalized prediction models for platelet dynamics

Hematotoxicity, drug-induced damage to the blood-forming system, is a frequent side effect of cytotoxic chemotherapy and poses a significant challen...

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 in diagnosis have substantial impact on survival. ...

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 reconstruction surgery in polytrauma patients, focusi...

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) remains a significant clinical challenge. This study ai...

Machine learning-based analysis on factors influencing blood heavy metal concentrations in the Korean CHildren's ENvironmental health Study (Ko-CHENS).

Heavy metal concentration in pregnant women affects neurocognitive and behavioral development of their infants and children. The majority of existing ...

[Artificial intelligence-based automated assessment of coronary flow reserve from angiography and the impact of different vasodilators].

To explore the feasibility of a coronary angiography-based method developed with artificial intelligence which was able to automatically and quickly ...

Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia

Aplastic anemia is a rare, life-threatening hematologic disorder characterized by pancytopenia and bone marrow failure. ICU admission in these patie...

DanceTogether! Identity-Preserving Multi-Person Interactive Video Generation

Controllable video generation (CVG) has advanced rapidly, yet current systems falter when more than one actor must move, interact, and exchange posi...

Anatomy-Guided Multitask Learning for MRI-Based Classification of Placenta Accreta Spectrum and its Subtypes

Placenta Accreta Spectrum Disorders (PAS) pose significant risks during pregnancy, frequently leading to postpartum hemorrhage during cesarean deliv...

Identification of molecular subtypes and a prognostic signature based on machine learning and purine metabolism-related genes in breast cancer.

Breast cancer (BC), one of the most prevalent malignant tumors worldwide, lacks efficacious diagnostic biomarkers and therapeutic targets. This study ...

Machine Learning Derived Blood Input for Dynamic PET Images of Rat Heart

Dynamic FDG PET imaging study of n = 52 rats including 26 control Wistar-Kyoto (WKY) rats and 26 experimental spontaneously hypertensive rats (SHR) ...

Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography for Low-Risk Patients Screened With Artificial Intelligence: Protocol for the CarDIA-AI Randomized Controlled Trial.

BACKGROUND: Invasive coronary angiography (ICA) is the gold standard in the diagnosis of coronary artery disease (CAD). Being invasive, it carries rar...

Deep transfer learning approach for automated cell death classification reveals novel ferroptosis-inducing agents in subsets of B-ALL.

Ferroptosis is a recently described type of regulated necrotic cell death whose induction has anti-cancer therapeutic potential, especially in hematol...

An AI-based automatic leukemia classification system utilizing dimensional Archimedes optimization.

Leukemia is a common type of blood cancer marked by the abnormal and uncontrolled proliferation and expansion of white blood cells. This anomaly impac...

Real-World fNIRS-Based Brain-Computer Interfaces: Benchmarking Deep Learning and Classical Models in Interactive Gaming

Brain-Computer Interfaces enable direct communication between the brain and external systems, with functional Near-Infrared Spectroscopy emerging as...

A Federated Learning Model for the Prediction of Blood Transfusion in Intensive Care Units.

Accurate prediction of blood transfusion requirements is crucial for patient outcomes and resource management in clinical settings. We developed a mac...