Critical Care

Machine Learning Prediction of Pediatric In-Hospital Survival Before Extracorporeal Membrane Oxygenation Cannulation.

Identifying suitable candidates for extracorporeal membrane oxygenation (ECMO) is still challenging. Our aim is to leverage machine learning (ML) to predict survival and identify critical variables influencing outcomes in pediatric patients requiring venovenous ECMO (VV-ECMO). This retrospective study used the Extracorporeal Life Support Organization (ELSO) registry to develop conventional ML algo...

Artificial intelligence assisted telemedicine, clinical decision support for anesthesia and critical care in intensive care units: a scoping review.

BACKGROUND: Artificial intelligence (AI) has been increasingly used in care delivery in intensive care units (ICUs) and anesthesia-critical care practice through telemedicine, tele-ICU systems, and remote patient monitoring, and is expected to support real-time clinical decision-making. METHODS: This scoping review followed PRISMA-ScR guidelines to map the existing evidence of AI in critical care ...

Dosimetry of Inhaled Aerosols: From Exposure to Target Tissue Dose in Risk Assessment and Drug Delivery.

Inhaled aerosol dosimetry is a critical discipline bridging exposure to biological effect in both therapeutic and toxicological contexts. This review ...

Admission-Based Machine-Learning Models for Predicting Mechanical Ventilation and Mortality in Fibrotic Interstitial Lung Disease: A Multicenter Cohort Study with External Validation.

BACKGROUND: Admission-based risk stratification tools are limited for hospitalized patients with fibrotic interstitial lung disease (F-ILD). AIMS: To ...

Clinician Perspectives on Ambient AI Scribes in the Intensive Care Unit: Qualitative Interview Study.

BACKGROUND: In intensive care unit (ICU) settings, structured team-based communication, such as multidisciplinary rounds, handoffs, and goals-of-care ...

A Validated, Modifiable Proteomic Score from the EXSCEL Trial Predicts Cardiovascular Events in Diabetes.

Adults with type 2 diabetes mellitus (T2DM) are at increased risk for stroke, myocardial infarction, and cardiovascular death, yet individual risk is ...

Exploring Ambient Artificial Intelligence to Enhance Learning and Feedback During Operating Room-to-Intensive Care Unit Handoffs: Co-Design and Simulation Study.

BACKGROUND: Operating room (OR)-to-intensive care unit (ICU) handoffs are among the most complex and high-risk communication events in perioperative c...

Artificial Intelligence in Hemodialysis: Current Clinical Applications and Future Perspectives.

INTRODUCTION: Timely adjustment of intervention strategies based on multidimensional hemodialysis data is essential for improving patients' quality of...

Lung Recruitment Maneuvers During Invasive Mechanical Ventilation: Single-Center Retrospective PICU Cohort Study, 2016-2023.

OBJECTIVES: Recruitment maneuvers (RMs) during invasive mechanical ventilation (IMV) for acute hypoxemic respiratory failure are not characterized in ...

Lung ultrasound feature tracking to quantify regional lung strain in mechanically ventilated pigs.

BACKGROUND: In patients requiring respiratory support, clinicians rely on physical exam, radiologic, laboratory, and ventilator-derived measures for t...

AI In Leukemia Diagnostics: Complementing the Pathologist's Role.

Artificial intelligence (AI) is reshaping every stage of leukemia diagnostics, from digital morphology and multiparameter flow cytometry to next-gener...

Developing a machine learning model to predict renal function decline within three years using one-year longitudinal data in patients with type 2 diabetes.

AIMS: The increasing prevalence of type 2 diabetes mellitus (T2DM) has led to an increase in diabetic kidney disease (DKD), which is presently a major...

Definitions and Severity/Risk Prediction Tools for Bronchopulmonary Dysplasia: A Scoping Review.

BACKGROUND: Bronchopulmonary dysplasia (BPD) remains a major complication of extreme prematurity, but diagnosis and severity classification have becom...

A multi-fidelity poroelastic finite element and machine learning framework for characterizing respiratory mechanics in porcine lungs.

Accurate and rapid characterization of lung mechanics remains a central challenge in respiratory disease management. Physics-informed poroelastic fini...

Machine learning phenotypes and heterogeneous albumin effects in cirrhotic AKI: a causal inference study.

Current guidelines recommend albumin infusion as a first-line treatment for acute kidney injury (AKI) in patients with cirrhosis. However, recent larg...

Behaviour artificial intelligence technology to support the decision-making process of continuation or withdrawal of life-sustaining therapy.

BACKGROUND: Withholding and withdrawing life-sustaining therapy (LST) is common in European ICUs but significant variations exist. Behaviour artificia...

Multi-view constrained semi-supervised vertebra detection for 3D ultrasound spine volume.

BACKGROUND: Scoliosis is a spinal disorder characterized by a three-dimensional (3D) deformity of the vertebral column. 3D ultrasound imaging has been...

Machine learning-based detection of missed inspiratory efforts using esophageal pressure during noisy pressure support ventilation.

BACKGROUND: Missed inspiratory efforts represent one of the most frequent and clinically relevant forms of patient-ventilator asynchrony during assist...

Predictive analytics and risk stratification models in internal medicine: from risk scores to real-time machine learning.

Internal medicine involves high-stakes, time-sensitive decisions (such as triaging acute illnesses, escalating care, providing thromboprophylaxis, pla...

Artificial intelligence for child health: current capabilities and the next frontier.

Artificial intelligence (AI) is reshaping paediatric healthcare, offering new capabilities across diagnosis, monitoring and treatment personalisation....