Critical Care

Detecting and measuring respiratory events in horses during exercise with a microphone: deep learning vs. standard signal processing.

BACKGROUND: Monitoring respiration parameters such as respiratory rate could be beneficial to understand the impact of training on equine health and performance and ultimately improve equine welfare. In this work, we compare deep learning-based methods to an adapted signal processing method to automatically detect cyclic respiratory events and extract the dynamic respiratory rate from microphone r...

Real-Time and Non-Invasive Detection of Respiratory Viral Infections Using an Intelligent Odor Monitoring System (IOMS).

Real-time monitoring of infection-associated volatile organic compounds (VOCs) offers a non-invasive pathway for early respiratory infection detection. However, diagnostic precision remains challenged by complex VOC mixtures, low analyte abundance, and significant biological variability. This study introduces an Intelligent Odor Monitoring System (IOMS) guided by infection-associated biomarker ide...

Optimising ICU supply chains: Evidence-based strategies for efficiency, resilience and patient safety-a structured narrative review.

Inventory management in intensive care units (ICUs) plays a critical role in ensuring uninterrupted patient care, yet it remains an underprioritised c...

Correction: Interpretable machine learning-based real-time sepsis diagnosis.

CaReS-BiNet: A multi-scale deep learning framework for ECG arrhythmia classification.

Automated electrocardiogram (ECG) arrhythmia classification remains challenging due to morphological complexity, severe class imbalance, and poor mode...

Energy-efficient wireless network control via spatio-temporal deep learning and multi-agent reinforcement learning.

The rapid expansion of wireless data traffic is placing increasing strain on the energy consumption of current communication networks, intensifying th...

Detection, localization, and measurement of endotracheal tube positioning on adults' chest X-ray: developing a prediction model.

Accurate placement of the endotracheal tube (ETT) is critical for ensuring optimal care for patients requiring mechanical ventilation and preventing p...

Innovative screening for OSAS in patients with head and neck cancer using the apneal application: Protocol for a prospective single-centre study.

INTRODUCTION: OSAS is a common yet underdiagnosed condition, particularly among patients with head and neck cancers (HNC). Anatomical changes caused b...

Risk Factor Analysis and Nomogram for Predicting 28-day in-hospital Mortality in ICU Patients with Liver Necrosis.

INTRODUCTION: The mortality risk factors in intensive care unit (ICU) patients with liver necrosis (LN) remain unclear. This study aimed to develop a ...

A physics-informed multi-modal transformer framework for predictive maintenance and remaining useful life estimation of electrical submersible pumps.

Electrical Submersible Pumps (ESPs) are widely used in oil and gas production but are highly vulnerable to mechanical, electrical, hydraulic, and ther...

Association between dynamic digital radiography findings and post-extubation respiratory deterioration: A retrospective exploratory analysis of a prospectively collected ICU cohort.

INTRODUCTION: Dynamic Digital Radiography (DDR) is a novel bedside imaging modality that enables real-time visualization of pulmonary motion with mini...

Single-cell and multi-omics integration delineates the landscape of gene-wise intratumor heterogeneity and identifies prognostic biomarkers for immunotherapy in non-small cell lung cancer.

BACKGROUND: Gene-wise intratumor heterogeneity (ITH), defined as spatial variability in the expression of individual genes across tumor regions, remai...

EMG-based hand gesture recognition using multi-scale deep residual network with SE-module.

In the past few years, Hand Gesture Recognition (HGR) utilizing EMG data has gained significant attention for improving human-machine interaction. How...

Interpretable machine learning model for predicting kidney failure among CAKUT children in multicenter large-scale study.

Congenital anomalies of the kidney and urinary tract (CAKUT) are the leading cause of pediatric kidney failure, but predicting individual progression ...

Deep Learning in Dental Imaging: Advances, Challenges, and Future.

Artificial intelligence and deep learning have expanded dental imaging analysis by enabling automated detection, classification, localization, segment...

Online Sepsis Prediction Using Vital Signs and Multiscale Temporal-Aware Contrastive Learning: Model Development and Validation Study.

BACKGROUND: Real-time prediction of sepsis is a critical yet highly challenging task. Existing studies face 2 major limitations. First, they often rel...

Interpretable Machine Learning for Early Risk Stratification of Carbapenem Resistance Among ICU Patients with Sterile-Site Pseudomonas aeruginosa Isolates: Development and Internal Validation Using MIMIC-IV.

OBJECTIVES: We developed and internally validated an interpretable machine learning model to stratify carbapenem-resistance probability among ICU pati...

AutoRV: Automated Assessment of Right Ventricular Structure and Function Using Transesophageal Echocardiography in Mechanically Ventilated Patients.

OBJECTIVE: Right ventricle (RV) dysfunction has therapeutic implications for the management of mechanically ventilated patients in intensive care unit...

Comparative machine learning modeling of resting energy expenditure estimation in mechanically ventilated children after cardiac surgery.

OBJECTIVE: Nutrition status is vital for children's recovery following cardiac surgery, with substantial inter-individual variability in metabolic dem...

Reproducible bicarbonate thresholds predict critically ill patient mortality in an international personalized survival study.

Bicarbonate abnormalities are common in intensive care unit (ICU) patients, but prior mortality studies neglected critical illness pathophysiology. Th...