Pulmonology

Advancements in wearable technology for heart failure patients.

PURPOSE OF REVIEW: While pharmacologic and device therapies have improved heart failure care in recent decades, age-adjusted rehospitalization rates remain largely stable. While the adoption of many implanted devices shown to reduce heart failure hospitalizations has been slow due to their invasive nature and limited insurance coverage, wearable devices such as smartwatches are noninvasive and inc...

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 develop and externally validate admission-based machine-learning models for predicting mechanical ventilation (MV), 30-day and 3-month mortality, and long-term all-cause and cause-specific mortality in hospitalized patients with F-ILD. STUDY DESIGN: ...

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 ...

Diagnostic Model Construction of Mitochondrial-Mitophagy Related Genes and Their Regulatory Network and Potential Drug Discovery in Childhood Allergic Asthma.

Childhood allergic asthma (CAA) is a chronic airway inflammatory disorder driven by allergens, and its underlying molecular mechanisms as well as prec...

Single-cell and machine learning-based neural regulation signature for prognosis prediction and immunotherapy response in lung adenocarcinoma.

OBJECTIVE: Lung adenocarcinoma (LUAD) molecular heterogeneity limits traditional prognostic models. Given the emerging role of neural regulation (NR) ...

Deep Learning for Differentiating Pulmonary Metastasis from Primary Lung Cancer Constructed on Frozen Sections for Intraoperative Diagnosis.

The surgical procedures for primary lung cancer and metastatic lung tumors differ. An intraoperative diagnosis is important for surgical procedure dec...

Transformative Role of Advanced Neural Computation in Clinical Image Diagnostics: A Review of Key Concepts and Applications.

Medical imaging plays a crucial role in modern diagnostic practices, but traditional techniques often face limitations in accuracy, efficiency, and sc...

Development and Validation of an Explainable Machine Learning Model to Assess the Prevalence Probability of Gastrointestinal Heat Retention Syndrome in Children: Cross-Sectional Study.

BACKGROUND: Health problems associated with energy metabolism imbalance have received increasing attention. Our team was the first to propose the conc...

An attention-infused deep convolutional paradigm for multi-label classification of thoracic pathologies in chest radiographs.

This research proposes an empirical benchmarking study of an attention-infused deep convolutional framework for multi-label thoracic pathology classif...

Optimizing lung cancer diagnosis using improved fungal growth optimizer-based medical image segmentation.

Medical image segmentation is one of the most important processes in computer-aided diagnosis. It plays a critical role in detecting and analyzing dis...

Endomyocardial Gremlin-1 is associated with structural remodeling and adverse clinical outcomes in non-ischemic cardiomyopathy.

BACKGROUND: Risk stratification in non-ischemic cardiomyopathies (NICM) remains challenging despite guideline-based phenotypic classification using mu...

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 ...

Correction: TB-DROP: deep learning-based drug resistance prediction of Mycobacterium tuberculosis utilizing whole genome mutations.

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...

ADC Target Profiling in NSCLC: Generalizable AI Separates TROP-2 and cMET Phenotypes.

PURPOSE: Antibody-drug conjugates (ADC) targeting trophoblast cell surface antigen 2 (TROP-2) and cMET are entering clinical trials in non-small cell ...

Integrative machine learning and CRISPR/Cas9 analysis reveals the role of APOE in lipid metabolism-associated kidney fibrosis.

Kidney fibrosis, characterized by excessive extracellular matrix deposition and progressive renal tissue remodeling, frequently culminates in End-stag...

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...

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...

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...