Pulmonology

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

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DLA-Net: dual lesion attention network for classification of pneumoconiosis using chest X-ray images.

Accurate and early detection of pneumoconiosis using chest X-rays (CXR) is important for preventing ...

Enhancing cancer prediction in challenging screen-detected incident lung nodules using time-series deep learning.

Lung cancer screening (LCS) using annual computed tomography (CT) scanning significantly reduces mor...

Computed tomography machine learning classifier correlates with mortality in interstitial lung disease.

BACKGROUND: A machine learning classifier system, Fibresolve, was designed and validated as an adjun...

Using blood routine indicators to establish a machine learning model for predicting liver fibrosis in patients with Schistosoma japonicum.

This study intends to use the basic information and blood routine of schistosomiasis patients to est...

Effect of emphysema on AI software and human reader performance in lung nodule detection from low-dose chest CT.

BACKGROUND: Emphysema influences the appearance of lung tissue in computed tomography (CT). We evalu...

Comprehensive clinical application analysis of artificial intelligence-enabled electrocardiograms for screening multiple valvular heart diseases.

BACKGROUND: Valvular heart disease (VHD) is becoming increasingly important to manage the risk of fu...

New Diagnostic Tools for Pulmonary Embolism Detection.

The presentation of pulmonary embolism (PE) varies from asymptomatic to life-threatening, and manage...

Attention pyramid pooling network for artificial diagnosis on pulmonary nodules.

The development of automated tools using advanced technologies like deep learning holds great promis...

Development of a machine learning model for predicting pneumothorax risk in coaxial core needle biopsy (≤3 cm).

PURPOSE: The aim is to devise a machine learning algorithm exploiting preoperative clinical data to ...

Machine-learning developed an iron, copper, and sulfur-metabolism associated signature predicts lung adenocarcinoma prognosis and therapy response.

BACKGROUND: Previous studies have largely neglected the role of sulfur metabolism in LUAD, and no st...

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data.

The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging c...

Feature fusion method for pulmonary tuberculosis patient detection based on cough sound.

Since the COVID-19, cough sounds have been widely used for screening purposes. Intelligent analysis ...

Investigating Machine Learning Techniques for Predicting Risk of Asthma Exacerbations: A Systematic Review.

Asthma, a common chronic respiratory disease among children and adults, affects more than 200 millio...

Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning.

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thora...

Screening and diagnosis of cardiovascular disease using artificial intelligence-enabled cardiac magnetic resonance imaging.

Cardiac magnetic resonance imaging (CMR) is the gold standard for cardiac function assessment and pl...

Prediction of tuberculosis clusters in the riverine municipalities of the Brazilian Amazon with machine learning.

OBJECTIVE: Tuberculosis (TB) is the second most deadly infectious disease globally, posing a signifi...

Deep Learning Features and Metabolic Tumor Volume Based on PET/CT to Construct Risk Stratification in Non-small Cell Lung Cancer.

RATIONALE AND OBJECTIVES: To build a risk stratification by incorporating PET/CT-based deep learning...

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