Pulmonology

Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules.

Percutaneous transthoracic puncture of small pulmonary nodules is technically challenging. We developed a novel electromagnetic navigation puncture system for the puncture of sub-centimeter lung nodules by combining multiple deep learning models with electromagnetic and spatial localization technologies. We compared the performance of DL-EMNS and conventional CT-guided methods in percutaneous lung...

Machine learning-driven prediction of medical expenses in triple-vessel PCI patients using feature selection.

Revascularization therapies, such as percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG), alleviate symptoms and treat myocardial ischemia. Patients with multivessel disease, particularly those undergoing 3-vessel PCI, are more susceptible to procedural complications, which can increase healthcare costs. Developing efficient strategies for resource allocation has be...

Assessing the Clinical and Functional Status of COPD Patients Using Speech Analysis During and After Exacerbation.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) affects breathing, speech production, and coughing. We evaluated a machine learning analysis ...

An effective COVID-19 classification in X-ray images using a new deep learning framework.

BackgroundThe global concern regarding the diagnosis of lung-related diseases has intensified due to the rapid transmission of coronavirus disease 201...

A machine learning framework for short-term prediction of chronic obstructive pulmonary disease exacerbations using personal air quality monitors and lifestyle data.

Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous disease with a variety of symptoms including, persistent coughing and mucus production...

Using natural language processing to identify emergency department patients with incidental lung nodules requiring follow-up.

OBJECTIVES: For emergency department (ED) patients, lung cancer may be detected early through incidental lung nodules (ILNs) discovered on chest CTs. ...

Task-oriented robotic rehabilitation for back mobility and functioning in a post-intensive care unit obese patient: A case report.

BackgroundIntensive care unit (ICU) acquired weakness is a detrimental condition characterized by muscle weakness, difficulty in weaning from mechanic...

Association between automatic AI-based quantification of airway-occlusive mucus plugs and all-cause mortality in patients with COPD.

In this cohort study involving 9399 current and former smokers from the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease study, we assess...

Classification of NSCLC subtypes using lung microbiome from resected tissue based on machine learning methods.

Classification of adenocarcinoma (AC) and squamous cell carcinoma (SCC) poses significant challenges for cytopathologists, often necessitating clinica...

Development of a clinical prediction model for benign and malignant pulmonary nodules with a CTR ≥ 50% utilizing artificial intelligence-driven radiomics analysis.

OBJECTIVE: In clinical practice, diagnosing the benignity and malignancy of solid-component-predominant pulmonary nodules is challenging, especially w...

Automatic machine learning accurately predicts the efficacy of immunotherapy for patients with inoperable advanced non-small cell lung cancer using a computed tomography-based radiomics model.

PURPOSE: Patients with advanced non-small cell lung cancer (NSCLC) have varying responses to immunotherapy, but there are no reliable, accepted biomar...

Generative Adversarial Networks With Radiomics Supervision for Lung Lesion Generation.

Data-driven methods for lesion generation are quickly emerging due to the need for realistic imaging targets for image quality assessment and virtual ...

Interpretable machine learning models for COPD ease of breathing estimation.

Chronic obstructive pulmonary disease (COPD) is a leading cause of death worldwide and greatly reduces the quality of life. Utilizing remote monitorin...

Advancing presurgical non-invasive spread through air spaces prediction in clinical stage IA lung adenocarcinoma using artificial intelligence and CT signatures.

BACKGROUND: To accurately identify spread through air spaces (STAS) in clinical stage IA lung adenocarcinoma, our study developed a non-invasive and i...

Risk Prediction of Liver Injury in Pediatric Tuberculosis Treatment: Development of an Automated Machine Learning Model.

PURPOSE: Drug-induced liver injury (DILI) is one of the most common and serious adverse drug reactions related to first-line anti-tuberculosis drugs i...

Golgi protein 73: charting new territories in diagnosing significant fibrosis in MASLD: a prospective cross-sectional study.

OBJECTIVES: To explore the correlation between serum Golgi protein 73 (GP73) levels and the degree of fibrosis in Metabolic dysfunction associated ste...

Sex-based variations in breath-holding: oxygen storage and diving response among non-divers.

Breath-hold diving performances are typically better in men than in women. However, it is still being determined if there are differences in the physi...

MCF-DTI: Multi-Scale Convolutional Local-Global Feature Fusion for Drug-Target Interaction Prediction.

Predicting drug-target interactions (DTIs) is a crucial step in the development of new drugs and drug repurposing. In this paper, we propose a novel d...

Assessing ChatGPT's accuracy and reliability in asthma general knowledge: implications for artificial intelligence use in public health education.

BACKGROUND: Integrating Artificial Intelligence (AI) into public health education represents a pivotal advancement in medical knowledge dissemination,...

Comparative diagnostic accuracy of ChatGPT-4 and machine learning in differentiating spinal tuberculosis and spinal tumors.

BACKGROUND: In clinical practice, distinguishing between spinal tuberculosis (STB) and spinal tumors (ST) poses a significant diagnostic challenge. Th...