Critical Care

Deep learning-based identification and maturation assessment of the zygomaticomaxillary suture in cone-beam computed tomography images.

OBJECTIVE: This study addresses maxillary deficiency management by developing a deep learning-based system for zygomaticomaxillary suture assessment. As the maturation status serves as a pivotal indicator for optimal maxillary protraction timing, its accurate evaluation is crucial for treatment success. The proposed cone-beam computed tomography-based system overcomes current limitations of subjec...

Deep learning-based labor relations prediction system with multi-source data fusion and early warning mechanisms.

Predicting workplace conflicts before they escalate into formal disputes or collective action represents a persistent challenge in organizational management, one that traditional statistical methods and expert judgment systems address inadequately due to labor relations' complex, multi-factorial nature. We present a deep learning-based early warning system that integrates heterogeneous organizatio...

A test-time clinically adaptive framework for detecting multiple fundus diseases harnessing ophthalmic foundation models.

Fundus diseases are leading causes of global vision impairment, often presenting with complex comorbidities that challenge conventional artificial int...

Feasibility of Implicit Neural Representation Learned Motion Compensation for 3D Stack-of-Spirals Free-Breathing Cardiac Quantitative Susceptibility Mapping.

PURPOSE: Differential blood oxygenation between the right and left heart (ΔSO2) is an indicator of cardiovascular function currently assessed in clini...

Emerging Trends and Innovations in Radiologic Diagnosis of Thoracic Diseases.

Over the past decade, Investigative Radiology has published numerous studies that have fundamentally advanced the field of thoracic imaging. This revi...

Exploring factors and models to predict post-dialysis volume overload status in maintenance hemodialysis patients based on pre-dialysis parameters.

OBJECTIVE: This study explored factors and models to predict post-dialysis volume overload status in maintenance hemodialysis patients (MHD) based on ...

Exploring the Ethical Dimensions of AI-Driven Patient Monitoring in Critical Care Nursing: A Systematic Review.

BACKGROUND: Artificial intelligence (AI) is increasingly applied in intensive care units (ICUs) to enhance monitoring, prediction and decision-making....

Simultaneous synthesis of perfusion and ventilation images from CT using a dual-decoder residual attention network for lung disease diagnosis.

BACKGROUND: Deep learning algorithms can synthesize pulmonary functional images from CT images. However, previous studies have only been able to predi...

Context-Aware Sentence Classification of Radiology Reports Using Synthetic Data: Development and Validation Study.

BACKGROUND: Automated structuring of radiology reports is essential for data utilization and the development of medical artificial intelligence models...

Predicting mortality and short-term outcomes of continuous kidney replacement therapies in neonates and infants.

BACKGROUND: Continuous kidney replacement therapy (CKRT) has emerged as a valuable treatment option in critically ill neonates and infants with acute ...

Explainable Machine Learning for Coma Outcome Prediction Based on Structural and Functional Brain MRI.

OBJECTIVES: Advanced MRI is recommended for the clinical evaluation of patients with coma. However, the implementation of these guidelines has been hi...

AI-enhanced therapeutic drug monitoring for vancomycin and β-lactam antibiotics in critical care: from population PK to bedside algorithms.

INTRODUCTION: Although sepsis and other severe infections in intensive care are common and deadly, obtaining safe and effective exposure for vancomyci...

Fcer1g and St3gal1: Macrophage-associated angiogenesis biomarkers and therapeutic targets in sepsis-induced acute lung injury.

BACKGROUND: Acute lung injury (ALI) involves the release of growth factors and inflammatory mediators from damaged pulmonary tissues, fostering endoth...

Machine learning models to detect opioid misuse in emergency department patients at triage.

OBJECTIVE: Emergency department (ED) encounters represent valuable opportunities to initiate evidence-based treatments for patients with opioid misuse...

Integrating Clinical Modeling and Machine Learning for Risk Assessment of Paracetamol and Other Nonsteroidal Anti-Inflammatory Drug Hypersensitivity in Children.

BACKGROUND: Nonsteroidal anti-inflammatory drug (NSAID) hypersensitivity is a common cause of drug-related reactions in children. Pre-test risk strati...

Machine learning-enabled early risk stratification of β-Iactam-induced electrolyte imbalances.

AIMS: To estimate the incidence of β-lactam/β-lactamase inhibitor - associated electrolyte imbalances and develop an internally validated, interpretab...

Early hemodynamic phenotyping in sepsis using transthoracic echocardiography: A proof-of-concept study in a north African ICU.

BACKGROUND: Cardiovascular dysfunction in sepsis is heterogeneous and contributes to poor outcomes. Hemodynamic phenotyping may delineate pathophysiol...

Machine learning analysis of s-EASIX for predicting 30-day mortality in sepsis patients from MIMIC-IV.

Endothelial dysfunction is an important risk factor for the progression of sepsis. The simplified endothelial activation and stress index (s-EASIX) se...

NeuroMDAVIS: Visualization of Single-Cell Multi-Omics Data under Deep Learning Framework.

Single-cell technologies have favored extensive advancements in cell-type discovery, cell state identification, development of lineage tracing, and di...

Explainable AI for mortality prediction: a comparative study using the MIMIC-III dataset.

OBJECTIVES: Predicting mortality is vital for tailoring treatments, improving care and reducing costs. Machine learning (ML) has shown strong potentia...