Pulmonology

Reconstruction of Heart-related Imaging from Lung Electrical Impedance Tomography Using Semi-Siamese U-Net.

INTRODUCTION: Electrical Impedance Tomography (EIT) is widely used for bedside ventilation monitoring but is limited in reconstructing cardiac-related signals due to the dominance of lung impedance changes. This study aims to reconstruct heart-related impedance imaging from lung EIT using a novel semi-Siamese U-Net architecture.

Unveiling etiology and mortality risks in community-acquired pneumonia: A machine learning approach.

Community-acquired pneumonia (CAP) is associated with high mortality, and accurate diagnosis and risk prediction are essential for improving patient outcomes. Traditional diagnostic methods have limitations, prompting the use of machine learning (ML) to enhance diagnostic precision and treatment strategies. This study aims to develop ML models to predict CAP etiology and mortality using clinical d...

Predicting and Diagnosing Pneumonia in Patients Undergoing Elective Cardiac Surgery via Machine Learning Analysis of Exhaled Volatile Carbonyl Compounds.

OBJECTIVE: Pneumonia remains one of the most common post-operative complications after elective cardiac surgery. Early intervention could lead to impr...

Multimodal Generative Artificial Intelligence Model for Creating Radiology Reports for Chest Radiographs in Patients Undergoing Tuberculosis Screening.

Chest radiographs play a crucial role in tuberculosis screening in high-prevalence regions, although widespread radiographic screening requires exper...

Case study on force compliant robot arm controller for nasopharyngeal swab insertion.

The nasopharyngeal (NP) swab sample test, commonly used to detect COVID-19 and other respiratory illnesses, involves moving a swab through the nasal c...

Integrating bioinformatics and machine learning to investigate the mechanisms by which three major respiratory infectious diseases exacerbate heart failure.

Heart failure (HF) is a severe cardiovascular disease often worsened by respiratory infections like influenza, COVID-19, and community-acquired pneumo...

Predictive modelling of air pollution affecting human tuberculosis risk on Mainland China.

In this study, we investigated the correlation between air pollution indicators and pulmonary tuberculosis (TB) incidence and mortality rates across p...

Clinical value of the 70-kVp ultra-low-dose CT pulmonary angiography with deep learning image reconstruction.

OBJECTIVE: This study aims to assess the feasibility of "double-low," low radiation dosage and low contrast media dosage, CT pulmonary angiography (CT...

Enhancing gas concentration prediction and ventilation efficiency in deep coal mines: a hybrid DL-Koopman and Fuzzy-PID framework.

With the increasing depth of coal mining operations, traditional ventilation systems are becoming insufficient to address the growing safety and opera...

Intelligent diagnosis model for chest X-ray images diseases based on convolutional neural network.

To address misdiagnosis caused by feature coupling in multi-label medical image classification, this study introduces a chest X-ray pathology reasonin...

Machine learning-based integration identifies plasma cells-related gene signature ST6GAL1 in idiopathic pulmonary fibrosis.

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a rare, progressive, and fibrotic disease with poor prognosis that lacks treatment options. As a ma...

Integrating CT radiomics and clinical features using machine learning to predict post-COVID pulmonary fibrosis.

BACKGROUND: The lack of reliable biomarkers for the early detection and risk stratification of post-COVID-19 pulmonary fibrosis (PCPF) underscores the...

Understanding the causal effects and heterogeneity between metabolic syndrome and lung function: a nationwide prospective cohort study in China.

BACKGROUND: Metabolic syndrome (MetS) has been widely recognized as a risk factor for lung function. However, the evidence regarding the causal effect...

Developing an innovative lung cancer detection model for accurate diagnosis in AI healthcare systems.

Accurate Lung cancer (LC) identification is a big medical problem in the AI-based healthcare systems. Various deep learning-based methods have been pr...

Development and validation of a transformer model-based early warning score for real-time prediction of adverse outcomes in the emergency department.

This study aimed to develop and validate a transformer-based early warning score (TEWS) system for predicting adverse events (AEs) in the emergency de...

Machine learning model and hemoglobin to red cell distribution width ratio evaluates all-cause mortality in pulmonary embolism.

The ratio of hemoglobin (Hb) to red blood cell distribution width (RDW), known as HRR, functions as an innovative indicator related to prognosis. Howe...

AI-driven genetic algorithm-optimized lung segmentation for precision in early lung cancer diagnosis.

Lung cancer remains the leading cause of cancer-related mortality worldwide, necessitating accurate and efficient diagnostic tools to improve patient ...

Identification of exosome-related genes in NSCLC via integrated bioinformatics and machine learning analysis.

Exosomes are crucial in the development of non-small cell lung cancer (NSCLC), yet exosome-associated genes in NSCLC remain insufficiently explored. T...

Unveiling diagnostic biomarkers and therapeutic targets in lung adenocarcinoma using bioinformatics and experimental validation.

Lung adenocarcinoma (LUAD) is a major challenge in oncology due to its complex molecular structure and generally poor prognosis. The aim of this study...

A deep learning-based computed tomography reading system for the diagnosis of lung cancer associated with cystic airspaces.

To propose a deep learning model and explore its performance in the auxiliary diagnosis of lung cancer associated with cystic airspaces (LCCA) in comp...