Pulmonology

Smart Breathomics: future prospects for non-invasive respiratory disease detection using biomarkers in exhaled breath condensate.

Smart Breathomics is redefining the field of non-invasive diagnostics in respiratory diseases through the analysis of exhaled breath condensate (EBC). Chronic respiratory diseases (CRDs) represent a significant global health burden and are becoming increasingly expensive to treat, which necessitates timely and precise diagnosis. Smart breathomics addresses the challenges faced in EBC analysis by p...

Artificial Intelligence in Tuberculosis Imaging: A Global Bibliometric Analysis of Research Trends and Collaborations.

INTRODUCTION: Tuberculosis (TB), a leading infectious cause of death, remains a global health challenge. Imaging is central to diagnosis and screening, while artificial intelligence (AI) is increasingly applied to chest X-rays (CXR) and computed tomography (CT). However, no bibliometric study has comprehensively mapped publication trends, collaborations, modalities, technological evolution, and em...

Integrated machine learning and density functional theory to design C₃N₅ piezo-catalysts toward precise control of selective reactive oxygen species and efficient water purification.

The precise modulation of reactive oxygen species (ROS) generation pathways is crucial for enhancing the selectivity and efficiency of piezo-catalytic...

Artificial intelligence to improve the detection and risk stratification of acute pulmonary embolism (AID-PE): protocol for a pragmatic quasi-experimental comparator study.

INTRODUCTION: Pulmonary embolism (PE) is a potentially fatal condition requiring timely diagnosis and treatment. CT pulmonary angiography (CTPA) is th...

Start4All protocol for a Bayesian cost-effectiveness model of tuberculosis screening and diagnosis in seven high burden low-income and middle-income countries.

INTRODUCTION: High costs of screening and diagnostic tests remain a major barrier to timely tuberculosis (TB) identification in resource-limited setti...

Large-scale evaluation of multimodal large language models for pneumothorax detection.

PURPOSE: Pneumothorax requires rapid recognition and accurate interpretation of chest X-rays (CXRs), particularly in acute settings where delays can h...

Early Maturation of Heart Rate Variability in Very Preterm Infants Depends on Neonatal Factors and Is Associated With Neurodevelopmental Risk.

AIM: This study investigated central autonomic network maturation deviations using a previously defined machine learning model set to estimate a funct...

Diagnostic model and identification of IGFBP2 as an aging-related biomarker and therapeutic target for systemic sclerosis-associated interstitial lung disease through integrated bioinformatics and machine learning approaches.

BACKGROUND: Cellular senescence is a critical contributor to the pathogenesis of systemic sclerosis-associated interstitial lung disease (SSc-ILD). Ho...

Global research trends in robotic-assisted thoracoscopic surgery: a multidimensional analysis from 2000 to 2025.

OBJECTIVE: Robotic-assisted thoracoscopic surgery (RATS) has transformed thoracic surgery, yet fragmented research patterns obscure critical developme...

Ancestry-Associated Performance Variability of Open-Source AI Models for EGFR Prediction in Lung Cancer.

IMPORTANCE: Artificial intelligence (AI) models are emerging as rapid, low-cost tools for predicting targetable genomic alterations directly from rout...

Artificial intelligence for lung cancer: a systematic review of head‑to‑head CT, FDG PET/CT, and multimodal models across screening, staging, and prognosis.

BACKGROUND: Artificial intelligence (AI) has shown increasing potential in lung cancer imaging, particularly in detection, staging, prognosis, and rec...

[Application of a novel robot-assisted navigation system in CT-guided percutaneous lung biopsy].

Objective: To compare the clinical efficacy and safety of robot-assisted navigation systems with those of the conventional puncture localization metho...

Performance of adult-trained artificial intelligence models in paediatric imaging-a scoping review.

OBJECTIVES: This scoping review aims to evaluate the performance of artificial intelligence (AI) models designed for adults when applied to paediatric...

Leveraging natural language processing and machine learning to identify chronic conditions from primary care electronic medical records.

Primary care electronic medical records (EMRs) contain rich data that can support proactive identification of chronic health conditions. However, leve...

Machine learning-based on model for explain risk of 24-hour death in critically ill patients in the prehospital setting: A retrospective cohort study.

This study aimed to develop and validate a machine learning-based model for predicting 24-hour mortality in critically ill patients using prehospital ...

A rapid diagnostic approach for COPD utilizing multimodal serum spectra integrated with machine learning algorithms.

Chronic Obstructive Pulmonary Disease (COPD) is a major global respiratory illness causing death and disability. Traditional methods lack consistent s...

A deep learning model based on ultrasound imaging to differentiate malignant from benign pleural effusion: a multicenter cohort study.

Noninvasive Staging of Hepatic Fibrosis in Patients with Autoimmune Liver Disease Using Deep Learning.

RATIONALE AND OBJECTIVES: Accurate staging of hepatic fibrosis is essential for guiding immunosuppressive and antifibrotic therapies. However, percuta...

Advances in nanostructured chemiresistive sensors for formaldehyde detection.

Formaldehyde (HCHO) is deleterious both as an indoor volatile organic compound, contributing to respiratory and carcinogenic risks, and as an adultera...

A Large Language Model Approach to Functional Status Scale Assessment.

OBJECTIVES: To develop a fine-tuned version of the generative pretrained transformer (GPT)-4o artificial intelligence (AI) model able to estimate Func...