Critical Care

Near-Real-Time Multi-Parametric Quantitative MRI using Parallel Non-Cartesian 6D Spatial-Temporal Dictionary Learning Neural Networks.

OBJECTIVE: Multi-parametric quantitative MRI (qMRI) enables precise targeting during image-guided interventions such as deep brain stimulation. To address the demand for higher temporal resolution in multi-parametric qMRI of the brain, we propose an online pipeline for near-real-time quantitative MRI. METHODS: The acquisition utilizes an alternating dual-flip-angle blipped multi-gradient-echo sequ...

Risk prediction models for incident heart failure: a systematic review and meta-analysis.

BACKGROUND: Early identification of patients at risk of heart failure (HF) provides opportunities for preventative management. Though models have been developed to predict HF incidence, their validation remains unclear. Our objective was to summarise the performance, as observed in validation studies, of risk prediction models for incident HF. METHODS: In addition to articles from three previous s...

The role of triglyceride-glucose (TyG) index in predicting mortality risk in critically ill patients with sepsis: a machine learning model in a cohort study.

AI-Driven Chemometrics for Multi-omics Data Integration: Advances, Challenges, and Future Directions.

The convergence of artificial intelligence and chemometrics has revolutionized multi-omics data integration, enabling unprecedented insights into comp...

Development of a calibrated logistic regression clinical risk score for predicting postoperative sepsis following retrograde intrarenal surgery.

Effective elimination of respiratory misregistration-induced attenuation correction errors in PET/CT via deep learning trained on data-driven gated PET from strictly respiratory-phase-matched PET/CT cases.

Pre-Imaging Clinical Factors Associated With Cardiac MR Image Quality Using Large Language Model-Enabled Data Extraction.

BACKGROUND: Poor cardiac MR image quality can prompt repeat examinations and hinder clinical decision-making. PURPOSE: To evaluate whether pre-imaging...

A motor thalamic site in humans that suppresses involuntary breathing without awareness.

Breathing is generated by brainstem respiratory networks but can be controlled and modulated by forebrain activity. The recent clinical adoption of th...

AttnEffNet-B4: an attention-augmented EfficientNet-B4 framework with fourier transformation for robust multi-disease diagnosis.

Early and precise disease detection in healthcare plays a vital role for better treatment results, healthier life expectancy, and good quality of care...

ECMO PAL VV: using deep neural networks for survival prognostication in venovenous extracorporeal membrane oxygenation.

Development of a real-world, therapeutic drug monitoring-informed model to predict teicoplanin daily dose in pediatric intensive care unit patients with bacterial infections.

INTRODUCTION: Teicoplanin is commonly used to treat Gram-positive bacterial infections in the intensive care unit (ICU). However, evidence to support ...

DeepCRI: Real-time EEG-based Prognostication after Cardiac Arrest.

Accurate prediction of neurological outcome after cardiac arrest is essential for guiding intensive care decisions. Electroencephalography (EEG) suppo...

Cross-sectional research: Application of an Artificial Intelligence-Based Pediatric Early Warning Score in the Pediatric Emergency Department.

BACKGROUND: There are a large number of pediatric emergency patients. Due to the fact that the children cannot describe their own conditions, there is...

Multimodal cardiovascular risk profiling using self-supervised learning of polysomnography.

STUDY OBJECTIVES: Polysomnography (PSG) provides a comprehensive assessment of brain, cardiac, and respiratory activity during sleep. While it is wide...

Construction and interpretability evaluation of a prediction model for radiographic pneumonia in children at outpatient and emergency departments.

BACKGROUND: Radiographic confirmation is crucial for pediatric pneumonia diagnosis, but chest X-ray overuse in outpatient and emergency settings raise...

Natural bioactive as modulators of airway inflammation and remodeling: mechanistic insights.

Airway diseases such as asthma, COPD, pulmonary fibrosis, and other inflammatory airway conditions remain a significant threat to global health. Tradi...

Targeted Surface-Enhanced Raman Scattering for Highly Accurate Identification of Bacterial Species and Finding Spectral Signatures with Explainable Artificial Intelligence.

The analysis of complex Raman spectra from biological samples has traditionally relied on conventional chemometric-based methods, the performance of w...

Early detection of heart failure using trend similarity metrics combined with hybrid deep learning based on real-world health monitoring data.

Heart failure (HF) is a global health crisis, with over 18 million annual deaths attributed to cardiovascular diseases. Early detection is critical to...

Predicting Postoperative Outcomes in Pediatric Ureteroscopy Using Machine Learning and Explainable AI-EAU Endourology Vision-AI Study.

AIM OF THE STUDY: This study aimed to evaluate the performance of machine learning (ML) algorithms integrated with explainable artificial intelligence...

Integrated multi-omics and machine learning identify Casp8 and Pycard as central regulators of pyroptosis and necroptosis in spinal cord injury.

Programmed cell death pathways exacerbate secondary damage after spinal cord injury, yet their shared regulators and tractable therapeutic points rema...