Pulmonology

Identifying factors associated with vaping cessation in young adults: A machine learning and XAI approach.

The public health impact of vaping in the United States reflects a complex balance of potential benefits and emerging risks, as e‑cigarettes may reduce exposure to toxic combustion byproducts and support adult smoking cessation, yet growing evidence links vaping to respiratory and cardiovascular harm and youth uptake remains concerning, with 38.4% of adolescent users in 2024 reporting habitual use...

AIBAN-AI Bronchial Detection and Airway Navigation System.

BACKGROUND: Accurate navigation within the lungs is critical for bronchoscopy procedures but remains challenging due to the complex airway anatomy. Artificial intelligence (AI) integrating real-time landmark detection and navigation guidance may enhance precision and safety. METHODS: We developed an AI Bronchial detection and Airway Navigation system (AIBAN) for bronchoscopy, combining airway lume...

Neutrophil extracellular traps-related biomarkers in idiopathic pulmonary arterial hypertension: A machine learning-based identification and experimental validation study.

Neutrophil extracellular traps (NETs) are increasingly recognized as critical mediators in vascular inflammation and remodeling, yet their molecular m...

Development and validation of a risk stratification model for sarcopenia in patients with chronic lung disease: a cross-sectional study based on CHARLS data.

OBJECTIVE: The aim of this study was to develop a machine learning-based stratification model to identify high-risk individuals for sarcopenia among p...

Supervised Learning for Collagen Segmentation in Bright-Field Histology: A Comparative Evaluation of U-Net and MLP.

PURPOSE: Quantifying collagen in histological slides is essential for diagnosing and monitoring fibrosis. However, the combination of PicroSirius Red ...

Cost-effectiveness of latent tuberculosis infection screening using interferon-gamma release assays among migrants to Japan: a Markov modelling study incorporating social vulnerability and access barriers.

OBJECTIVE: This study aims to evaluate the cost-effectiveness of latent tuberculosis infection (LTBI) screening strategies using interferon-gamma rele...

Understanding LUng Cancer risk factors and their Impact Assessment (LUCIA): protocol for multicentre observational cohort study.

INTRODUCTION: Lung cancer (LC) is the leading cause of cancer-related mortality worldwide, primarily due to diagnosis at advanced stages. Although low...

Classification of spinal tuberculous infection, pyogenic infection and spinal metastasis from magnetic resonance imaging using machine learning.

BACKGROUND: Differentiating between spinal tuberculosis, pyogenic (bacterial) spondylitis and spinal metastasis remains a major diagnostic challenge b...

Development and evaluation of an ontology for non-invasive respiratory support in acute care.

Managing patients with respiratory failure increasingly involves non-invasive respiratory support (NIRS) strategies to support respiration, often prev...

Comprehensive Multi-Omics Profiling of Tertiary Lymphoid Structures Reveals Immunogenetic Landscapes and Prognostic Subtypes in Lung Adenocarcinoma.

Tertiary lymphoid structures (TLSs) are key components of the tumor immune microenvironment and show prognostic relevance in many cancers. However, th...

From Genomic Instability to Epigenetic Signatures: The Evolving Landscape of Circulating Cell-free DNA in Metabolic Dysfunction-Associated Steatotic Liver Disease.

Metabolic dysfunction-associated steatotic liver disease (MASLD) represents a leading global health burden, yet its diagnosis and staging rely heavily...

Identification of Mitochondrial Dysfunction Genes as Diagnostic Biomarkers for Ischemic Stroke by Integrated Bioinformatics Analysis and Machine Learning.

Current diagnostics for ischemic stroke (IS) lack timeliness and accessibility, highlighting the need for novel molecular diagnostic models. Three gen...

Engineering biomarker representations of vital signs data enhances deep learning mortality prediction.

OBJECTIVES: We evaluated bidirectional long short-term memory models for predicting inpatient mortality using different approaches to processing vital...

Optimized explainable AI and digital twin for patient flow improvement in ICU during respiratory epidemics.

BACKGROUND: Respiratory epidemics often place substantial pressure on intensive care units (ICU), which are continuously challenged to managing acute ...

Hybrid deep learning model for multimodal vocal and lung signal analysis in health monitoring.

Non-invasive health monitoring has recently gained a lot of consideration in the modern healthcare system, because it has the potential to diagnose di...

Artificial intelligence on chest X-ray for tuberculosis screening in Tanzania: a multicentre evaluation.

Tanzania has adopted artificial intelligence (AI)-assisted chest X-ray screening for tuberculosis (TB), including the use of CAD4TB version 6, which i...

The association of systemic inflammatory indices with all-cause mortality risks in patients with COPD: A cohort study based on machine learning.

This study aimed to comprehensively assess the prognostic value of routinely obtained blood-based systemic inflammatory indices in predicting all-caus...

Imaging of Small Cell Lung Cancer: An Updated Overview of Current and Emerging Applications.

Small cell lung cancer (SCLC) is an aggressive pulmonary neuroendocrine carcinoma characterized by rapid progression and early metastasis. Despite rec...

AlveolEye: Rapid and precise lung morphometry guided by computer vision.

Rigorous and reproducible evaluation of lung tissue under different conditions is necessary to interpret development, injury, and pharmacologic interv...

Data-driven outcome prediction and rational resource allocation in severe traumatic brain injury: a multicenter retrospective study integrating survival analysis and machine learning ensembles.

INTRODUCTION: Severe traumatic brain injury (sTBI), is a leading cause of death and disability among young and middle-aged populations worldwide. OBJE...