Critical Care

Representation learning for the prediction of length of stay in infants with congenital heart diseases undergoing cardiac surgery.

AIMS: Congenital heart disease is the most common birth defect and significantly impacts health, mortality, and healthcare costs. Accurately estimating intensive care unit (ICU) and hospital stays after cardiac surgery could facilitate family planning and optimal healthcare resource allocation. Although some perioperative risk factors have been associated with prolonged stays, tools to estimate th...

Artificial Intelligence-Driven Electrocardiogram Screening for Asymptomatic Left Ventricular Systolic Dysfunction in the General Population.

BACKGROUND: Asymptomatic left ventricular systolic dysfunction (LVSD) is a well-established precursor of overt heart failure (HF), yet it often remains undiagnosed in the general population. Artificial intelligence-enabled electrocardiogram (ECG) analysis offers a scalable approach for early detection. OBJECTIVES: The purpose of this study was to evaluate the diagnostic performance of an artificia...

DeePedIMV: Deep learning model for early prediction of invasive mechanical ventilation in critically ill children.

BACKGROUND: Acute deterioration requiring invasive mechanical ventilation (IMV) is common in critically ill children and may arise from respiratory, c...

Development and Validation of a Machine Learning-Based Nomogram for Predicting Severe Pneumocystis jirovecii Pneumonia in Kidney Transplant Recipients.

BACKGROUND Pneumocystis jirovecii pneumonia (PJP) is a life-threatening opportunistic infection in kidney transplant recipients (KTRs). Early identifi...

Comparison of Left Ventricular Ejection Fraction Among 18F-FDG Dual-Gated PET/CT, Single-Gated PET/MRI, and Cardiac MRI.

Left ventricular ejection fraction (LVEF) is a critical parameter in the evaluation of cardiac function, and its measurement can guide treatment decis...

Interpretable Multi-Modality Consensus QSAR Framework: Integrating Machine and Deep Learning for Enhanced Multi-Endpoint Toxicity Assessment.

Accurate toxicity assessment is essential for chemical safety, but experimental testing is costly, slow, and ethically constrained, motivating the ado...

Current and future contributions of AI to pulmonary function test interpretation, diagnostic approaches, and predictions of disease progression.

INTRODUCTION: Artificial intelligence (AI) methods - including machine learning, deep learning, and explainable AI - are increasingly applied to pulmo...

Machine Learning Prediction of Progression to Dialysis in Patients With Polycystic Kidney Disease: Population-Based Retrospective Cohort Study.

BACKGROUND: Autosomal dominant polycystic kidney disease (ADPKD), characterized by progressive cyst growth and renal decline, is the leading genetic c...

Clinic-first sepsis recognition in the ICU: a proteomics-guided, parsimonious model with independent validation.

BACKGROUND: Sepsis recognition in the ICU remains variable and relies on consensus clinical criteria rather than biomarker-defined rules. Routine labo...

Explainable machine learning for postoperative respiratory failure prediction in open-heart surgery patients - a study based on the MIMIC-IV database.

BACKGROUND: Postoperative respiratory failure (PRF) is a severe complication after open-heart surgery, associated with increased mortality and prolong...

Machine learning in ARDS: an intensivist's guide to artificial intelligence applications.

Accurate, fair, and generalisable scaling of injury severity score-based AI with demographics in terms of mortality in patients with trauma: multi-centre, multi-national retrospective cohort study.

BACKGROUND: Accurate and equitable prediction of trauma-related in-hospital mortality is critical for guiding clinical decisions and optimising trauma...

Prediction of favorable outcomes of acute basilar artery occlusion using machine learning.

BACKGROUND: This study aims to develop an interpretable machine learning model using SHapley Additive exPlanations (SHAP) to predict favorable outcome...

Functional-based multi-omics early prediction of radiation pneumonitis in NSCLC using AI-generated perfusion and ventilation from planning CT.

ObjectiveThis study aims to develop a functional-based multi-omics model for early prediction of radiation pneumonitis (RP) by extracting radiomic and...

[Care for uncontrolled asthma in Germany - with focus on Magdeburg and Mannheim regions].

Description of treatment and prescription patterns among asthma patients in the regions of Magdeburg (MD) and Mannheim (MA) compared nationwide.We ana...

Bayesian networks for predicting clinical outcomes in COVID-19 patients: A retrospective study in a resource-limited setting.

BACKGROUND: The COVID-19 pandemic has highlighted the critical need for robust, interpretable predictive models to guide clinical decision-making for ...

Systematic review of Artificial Intelligence-based methods for glycemic control and risk prediction in intensive care units.

BACKGROUND: Achieving safe glycemic targets in intensive care remains difficult due to rapidly changing physiology, treatment effects, and measurement...

Evidence-based AI clinical decision support system for acute burn care and complex reconstruction.

BACKGROUND: Burn surgery requires rapid, evidence-based decision-making across acute resuscitation, operative management, and reconstruction. Despite ...

Infrared thermography in the detection of arthritis: a systematic review of diagnostic performance compared with joint ultrasound.

Early detection of arthritis in autoimmune rheumatic diseases (ARDs) is critical to prevent irreversible damage. Joint ultrasound (US) offers high sen...

[Expert consensus on the clinical application of robotic-assisted bronchoscopy].

Robotic-assisted bronchoscopy (RAB) is an emerging diagnostic and interventional technology which integrates thin-slice CT-based virtual airway recons...