Pulmonology

Multimodal data integration to determine viral and innate immune kinetics in human airway epithelium.

Understanding the mechanisms that govern viral spread in human airway epithelium (HAE) remains a major challenge, particularly with regard to identifying and quantifying key factors such as cell-type-specific infectivity, viral transmission paths, and the innate immune dynamics. Although mathematical models and experimental advances have provided valuable insights into respiratory infections, reve...

External validation of cough-based algorithms for pulmonary tuberculosis screening from the CODA TB DREAM challenge using cough data from Peru.

The COugh Diagnostic Algorithm for Tuberculosis (CODA TB) DREAM Challenge recently evaluated the performance of artificial intelligence (AI) algorithms for tuberculosis (TB) screening using cough sounds. Eleven AI models were developed using a dataset of 733,756 cough sounds collected from 2143 adults from seven countries. This study evaluates the CODA Challenge AI models with an external independ...

Multicenter machine learning model for assessing the impact of malignancy on in-hospital mortality in heart failure patients: a clinical decision support system with interpretable artificial intelligence.

Heart failure (HF) and malignancy represent two major global health burdens that frequently coexist and lead to poor clinical outcomes. However, the s...

National trends and comparative outcomes of insulin versus non-insulin therapy in acute ischemic stroke patients treated with mechanical thrombectomy: a retrospective cohort study using the national inpatient sample.

Background diabetes mellitus is prevalent among patients with acute ischemic stroke (AIS). The prognostic significance of long-term insulin treatment ...

Deep learning-based motion correction: cardiac motion artifact and image quality improvements on chest CT.

PURPOSE: Since the clinical application of computed tomography (CT), cardiac and respiratory motion artifacts have caused decreased image quality and ...

Computational models for the classification of antibody specificity using heavy chain features.

BACKGROUND: Antibodies play a critical role in immune defense, with their antigen specificity primarily governed by the unique sequences of their heav...

Pulmonary nodule growth prediction with anisotropic reaction-diffusion.

BACKGROUND AND OBJECTIVE: Accurate prediction of pulmonary nodule growth is critical for early malignancy assessment and timely lung cancer diagnosis....

A Dynamic Machine Learning Approach to Complement Nurse-Led Clinics in Identifying High-Risk Patients with Intermittent Claudication.

OBJECTIVE: To develop and validate dynamic machine-learning models to predict 6-month adverse outcomes in intermittent claudication (IC), enabling ris...

Pre-analytical standardization and multi-biofluid harmonization for liquid biopsy biomarkers in lung cancer: From discovery to clinical laboratory implementation.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection being critical for improving survival outcomes. Liqu...

PneumoMLPred: Innovating Pneumonia Management with Guideline Adherence Review and Explainable Machine Learning-based Mortality Prediction - A single-centre retrospective study.

INTRODUCTION: Community-acquired pneumonia (CAP) is one of the leading causes of death globally. Changing antibiotic resistance profiles, paucity of r...

[Position paper of the German Society of Cardiology-quality criteria for performing catheter ablation of atrial fibrillation: executive summary].

The updated German Society of Cardiology (DGK) position paper on catheter ablation of atrial fibrillation (AF) [1] presents the current evidence, tech...

Improving Diagnosis of Severe Paediatric Lymphobronchial Tuberculosis Using Segmentation and Deep Learning on Chest X-Rays.

BACKGROUND: Diagnosing intrathoracic tuberculosis (TB) in children under five years of age remains challenging. Clinical symptoms often overlap with t...

Application of an AI-Based Pediatric Early Warning Score in the Pediatric Emergency Department: Cross-Sectional Study.

BACKGROUND: Pediatric emergency departments see a high volume of patients. Given that children often cannot describe their condition and there is a sh...

Hybrid fractional groupers and moray eels driven deep learning for pneumonia detection using multi-modal data in federated learning.

Pneumonia is a severe lung infection triggered by various viral pathogens. Detecting and diagnosing pneumonia using clinical images is challenging bec...

AI-guided integration of aging-related functional assessment to predict nonresponse to cardiac rehabilitation in heart failure.

BACKGROUND: Cardiac rehabilitation (CR) improves functional capacity and outcomes in patients with heart failure (HF). However, a clinically significa...

Evaluation of a deep learning model applied to chest X-rays of patients with suspected pneumonia presenting to the emergency department designed to predict admission risk: a retrospective training and prospective non-intervention validation.

OBJECTIVES: To develop, train and test a deep learning model Image-based PROgnostication applied to Chest X Rays (IPRO-X) tool that predicts the inpat...

A real-time early warning system to anticipate respiratory disease outbreaks using transfer learning.

Respiratory disease outbreaks burden American healthcare systems with over one million hospitalizations annually, yet current surveillance systems lag...

Viral pneumonia detection during the COVID-19 pandemic using deep learning and DCGAN-based data augmentation.

Deep learning has shown strong potential for automated chest X-ray analysis in the diagnosis of respiratory diseases. However, accurate detection of v...

Use of machine learning and voice for multiclass classification of Parkinson's disease, chronic obstructive pulmonary disease, and healthy controls.

Parkinson's disease (PD) and chronic obstructive pulmonary disease (COPD) are prevalent conditions with substantial impact on quality of life and heal...

Real-time prediction of cardiorespiratory deterioration during paediatric critical care transport using interpretable machine learning.

Interhospital transport of critically ill children carries inherent risks, including unexpected respiratory and cardiovascular deterioration. Early wa...