Critical Care

A Review of the Role of Artificial Intelligence in Patients on Extracorporeal Membrane Oxygenation: A Scoping Review Study.

BACKGROUND AND AIMS: ECMO is used to deliver cardiopulmonary support in extreme failure where standard therapies are unfruitful. Although ECMO is associated with high costs and significant risks, advances in technology and clinical management have improved its safety and patient outcomes. AI/ML may provide decision support through data analysis to alert clinicians to potential safety issues, detec...

Real-Time Dynamic Prediction of Mortality and Renal Replacement Therapy After Cardiac Surgery Using a Time-Series Deep Learning Model.

OBJECTIVE: Predicting postoperative deterioration following cardiac surgery remains challenging. Conventional risk scores rely on static variables and fail to capture evolving physiologic trajectories. We developed a time-series deep learning model (DLM) using serial ICU measurements to dynamically predict mortality and continuous renal replacement therapy (CRRT) after cardiac surgery. METHODS: Us...

The LoVe score: Development and validation of a bedside clinical index to identify patients at risk for prolonged invasive mechanical ventilation.

BACKGROUND: Early management decisions after intubation, such as humidification strategy or initiation of prevention bundles for ventilator-associated...

Deep Learning-Based Classification for Grading of Respiratory Distress Syndrome on Neonatal Chest Radiographs.

OBJECTIVE: To develop and validate a deep learning-based multi-class classification model for automated grading of respiratory distress syndrome (RDS)...

Risks of Major Adverse Kidney Events in Non-Hispanic Black Patients with Diabetes or Hypertension: A real-world cohort study.

INTRODUCTION: Chronic kidney disease (CKD) disproportionately burdens non-Hispanic Black (NHB) patients who experience a three- to four-fold higher ri...

Differential Diagnosis of Asthma and COPD: Established and Emerging Biomarkers and Technologies.

Asthma and chronic obstructive pulmonary disease (COPD) represent the two most prevalent chronic respiratory conditions worldwide, affecting hundreds ...

Early mortality prediction of prognosis in cardiac arrest patients using machine learning: Development, external validation, and explainability with SHAP.

BACKGROUND: Cardiac arrest (CA) is a major global health challenge, accounting for a significant proportion of deaths and healthcare resource utilizat...

Comparison of Machine Learning-Based Reporting with Surgeon Reporting of Postoperative Complication in Cardiothoracic Morbidity and Mortality Conferences.

BACKGROUND: Traditional morbidity and mortality (M&M) conferences incompletely capture postoperative complications, potentially limiting quality impro...

Completely free-breathing cardiac MRI using deep learning reconstruction reduces sedation and scan time in children.

BACKGROUND: Traditional cardiac magnetic resonance (CMR) imaging scan times are long and require breath-holds, often necessitating intubation and mech...

Improving respiratory and heart rate variation estimation from resting-state BOLD fMRI across the lifespan using a functionally informed, tissue-aware deep learning framework.

Accurate measurement of physiological signals such as respiration and cardiac activity is essential for modeling physiological confounds in BOLD-fMRI ...

Leveraging ECG foundation models in critical care for sinus rhythm and atrial fibrillation classification.

BACKGROUND: Recent advances in deep learning have led to the development of ECG foundation models (ECG-FMs) trained with self-supervised learning, whi...

Machine Learning Modeling for Predicting Mortality in Pediatric Patients Undergoing Elective Noncardiac Surgery: Comparison to a Regression Model.

BACKGROUND: Perioperative mortality in children is relatively rare; however, accurate preoperative risk stratification is critical, as it enables anti...

[Development and validation of an explainable machine learning-based model for predicting mortality risk in polytrauma patients].

Polytrauma is commonly defined as multisystem trauma involving at least two body regions with an Abbreviated Injury Scale (AIS) score ≥ 3, characteriz...

Dynamic F1-score-based voting strategies for multi-class classification: an adaptive ensemble approach for non-linear and imbalanced datasets.

Classification is a core machine learning task, and ensemble voting methods are widely used to improve predictive accuracy in domains such as medical ...

ICU Open-Access Databases for Artificial Intelligence in Sepsis: Balancing Innovation With Data Quality Challenges.

OBJECTIVES: To evaluate the suitability of three major open-access ICU databases (Medical Information Mart for Intensive Care IV [MIMIC-IV], eICU Coll...

Historians, Not Prophets: Sepsis Temperature Trajectories in the Machine Learning Age.

Physician-revised AI-generated drafts are associated with higher ratings of written explanations in end-of-life care in the intensive care unit: a scenario-based single-center cross-sectional study.

BACKGROUND: In end-of-life care (EOL) in the intensive care unit (ICU), intensivists are expected to provide medically appropriate and empathetic comm...

Autonomous Vascular Access Devices: Current Solutions and Future Challenges.

INTRODUCTION: Central venous cannulation is essential for life-saving interventions including resuscitation of critically ill patients, hemodynamic mo...

EXPRESS: Relation between Albumin-Corrected Anion Gap and In-Hospital Mortality in Patients with Traumatic Lung Injury: A Multicenter Retrospective Cohort Study and the Development of Machine Learning-Based Prediction Models.

BACKGROUND: The anion gap is primarily utilized as an indicator for evaluating acid-base imbalances in critically ill patients. However, its accuracy ...

Perception and accuracy of an AI-based contactless wellness screening system in dental setting; a cross-sectional study.

This study evaluates the reliability and participants' responses to a contactless artificial intelligence (AI) device powered by Remote Photoplethysmo...