Pulmonology

Latest AI and machine learning research in pulmonology for healthcare professionals.

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A systematic review and meta-analysis of artificial intelligence software for tuberculosis diagnosis using chest X-ray imaging.

BACKGROUND: Pulmonary tuberculosis (PTB) remains a global public health challenge, with 10.8 million...

A Deep Neural Network Framework for the Detection of Bacterial Diseases from Chest X-Ray Scans.

AIMS: This research aims to develop an advanced deep-learning framework for detecting respiratory di...

Quantitative computed tomography imaging classification of cement dust-exposed patients-based Kolmogorov-Arnold networks.

BACKGROUND: Occupational health assessment is critical for detecting respiratory issues caused by ha...

Interpretable niche-based cell‒cell communication inference using multi-view graph neural networks.

Cell‒cell communication (CCC) is a fundamental biological process for the harmonious functioning of ...

Advancing breast, lung and prostate cancer research with federated learning. A systematic review.

Federated learning (FL) is advancing cancer research by enabling privacy-preserving collaborative tr...

Immunogenic cell death biomarkers for sepsis diagnosis and mechanism via integrated bioinformatics.

Immunogenic cell death (ICD) has been implicated in sepsis, a condition with high mortality, through...

China Protocol for early screening, precise diagnosis, and individualized treatment of lung cancer.

Early screening, diagnosis, and treatment of lung cancer are pivotal in clinical practice since the ...

Screening and identification of novel protein markers of early-stage lung cancer and construction and application of screening models.

OBJECTIVE: Molecular biomarkers have the potential to improve the current state of early screening o...

Use video comprehension technology to diagnose ultrasound pneumothorax like a doctor would.

INTRODUCTION: Emergency rescue scenes and pre-hospital emergency stages commonly encounter trauma vi...

Novel machine learning models for the prediction of acute respiratory distress syndrome after liver transplantation.

Early prediction of acute respiratory distress syndrome (ARDS) after liver transplantation (LT) faci...

Artificial Intelligence for Risk Stratification of Acute Pulmonary Embolism: Perspectives on Clinical Needs, Expanding Toolkit, and Pathways Forward.

Despite a significant number of innovations for management of acute pulmonary embolism (PE) over the...

Development and validation of a machine learning model for real-time prediction of invasive mechanical ventilation weaning readiness.

PURPOSE: To develop and validate a bedside machine learning (ML) decision support tool for predictio...

Can intraoperative improvement of radial endobronchial ultrasound imaging enhance the diagnostic yield in peripheral pulmonary lesions?

BACKGROUND: Data regarding the diagnostic efficacy of radial endobronchial ultrasound (R-EBUS) findi...

Effectiveness of Digital Health Interventions for Chronic Obstructive Pulmonary Disease: Systematic Review and Meta-Analysis.

BACKGROUND: Chronic obstructive pulmonary disease (COPD), marked by dyspnea, cough, and sputum produ...

Lung auscultation - today and tomorrow- a narrative review.

INTRODUCTION: Lung auscultation is a fundamental diagnostic tool for respiratory conditions. Despite...

Deep learning-based framework for Mycobacterium tuberculosis bacterial growth detection for antimicrobial susceptibility testing.

Tuberculosis (TB) kills more people annually than any other pathogen. Resistance is an ever-increasi...

The predictive value of F-FDG PET/CT radiomics for pleural invasion in non-small cell lung cancer.

OBJECTIVE: This study aims to develop and validate a PET/CT radiomics fusion model for preoperative ...

Explainable machine learning and feature interpretation to predict survival outcomes in the treatment of lung cancer.

The treatment outcomes of lung cancer are highly variable, and machine learning (ML) models provide ...

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