Pulmonology

Machine learning and bioinformatics-based identification of mitophagy-related diagnostic biomarkers in bronchiolitis obliterans.

This study aimed to explore the molecular mechanisms associated with mitophagy in BO and identified mitophagy-associated BO diagnostic genes. Using Gene Expression Omnibus database data, differentially expressed genes in BO patients vs controls were analyzed via Gene Ontology enrichment. Algorithms like Boruta, least absolute shrinkage and selection operator, and Random Forest screened BO-specific...

Research on the Prediction of Coal Workers' Pneumoconiosis Based on Easily Detectable Clinical Data: Machine Learning Model Development and Validation Study.

BACKGROUND: Coal workers' pneumoconiosis (CWP) is the most prevalent occupational disease that causes irreversible lung damage. Early prediction of CWP is the key to blocking the irreversible process of pulmonary fibrosis. The prediction of CWP based on imaging data and biomarker detection is constrained due to high cost and poor convenience. OBJECTIVE: The study aimed to use easily detectable cli...

Surface Tracking-Assisted Multi-Cycle 4D MRI Motion Modeling for Lung Radiotherapy: A Preliminary Validation Study.

To validate a respiratory motion model that uses real-time electromagnetic (EM) surface tracking acquired concurrently with time-resolved multi-cycle ...

MS-HIENet: multi-scale hybrid implicit-explicit registration network.

Medical image registration serves as a cornerstone for precision diagnosis and treatment, particularly for dynamic organs like the lungs, where high d...

A lightweight residual dilated temporal transformer block for ECG classification on edge devices.

Wearable devices play a crucial role in healthcare by enabling continuous monitoring of vital physiological signals such as ECG, heart rate, respirati...

A hybrid LSTM-GRU framework for lung cancer classification using GWO-WOA algorithm for hyperparameter tuning and BPSO for feature selection.

Early identification of lung cancer using questionnaire-based data offers a low-cost, non-invasive pathway to assist clinical decision-making. However...

Development of Radiomics Models to Predict Progression-Free Survival and Early Polymetastatic Progression in Patients With Lung Oligometastases Treated on the Single-Arm Phase II Stereotactic Ablative Radiotherapy-5 Trial.

AIMS: Despite the increasing use of stereotactic ablative radiotherapy (SABR) for oligometastatic cancer, at present, accurate models to predict the t...

A deep learning and morphometric hybrid model for automated quantification of kidney interstitial fibrosis in trichrome-stained whole-slide image.

BACKGROUND: Interstitial fibrosis (IF) is the strongest predictor of chronic kidney disease progression. Visual estimation of IF from trichrome (TRI)-...

Artificial intelligence-enhanced ECG score for perioperative risk assessment in non-cardiac surgery.

AIMS: The role of electrocardiography (ECG) has been limited in the preoperative risk evaluation in noncardiac surgery due to its low prognostic value...

Street-level ambient fine particulate matter and tuberculosis: A machine learning and Bayesian spatiotemporal analysis.

Tuberculosis (TB) remains a leading cause of death from infectious diseases globally, yet evidence linking short-term ambient fine particulate matter ...

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)....

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...

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...