Pulmonology

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

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Artificial intelligence and machine learning in respiratory medicine.

: The application of artificial intelligence (AI) and machine learning (ML) in medicine and in parti...

Identifying Lung Cancer Risk Factors in the Elderly Using Deep Neural Networks: Quantitative Analysis of Web-Based Survey Data.

BACKGROUND: Lung cancer is one of the most dangerous malignant tumors, with the fastest-growing morb...

Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning.

Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate progno...

Estimating an individual's oxygen uptake during cycling exercise with a recurrent neural network trained from easy-to-obtain inputs: A pilot study.

Measurement of oxygen uptake during exercise ([Formula: see text]) is currently non-accessible to mo...

Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study.

OBJECTIVES: Pneumothorax is the most common and potentially life-threatening complication arising fr...

External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules.

BACKGROUND: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in c...

Comparison of Artificial Intelligence-Based Fully Automatic Chest CT Emphysema Quantification to Pulmonary Function Testing.

The purpose of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm...

Deep Learning from Incomplete Data: Detecting Imminent Risk of Hospital-acquired Pneumonia in ICU Patients.

Hospital acquired pneumonia (HAP) is the second most common nosocomial infection in the ICU and cost...

Towards Reliable ARDS Clinical Decision Support: ARDS Patient Analytics with Free-text and Structured EMR Data.

In this work, we utilize a combination of free-text and structured data to build Acute Respiratory D...

CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.

PURPOSE: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is a...

Feature-shared adaptive-boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images.

PURPOSE: In clinical practice, invasiveness is an important reference indicator for differentiating ...

LungRegNet: An unsupervised deformable image registration method for 4D-CT lung.

PURPOSE: To develop an accurate and fast deformable image registration (DIR) method for four-dimensi...

Imaging research in fibrotic lung disease; applying deep learning to unsolved problems.

Over the past decade, there has been a groundswell of research interest in computer-based methods fo...

Exercise cardiac power and the risk of heart failure in men: A population-based follow-up study.

BACKGROUND: Little is known about exercise cardiac power (ECP), defined as the ratio of directly mea...

Automatic Lung Nodule Detection Combined With Gaze Information Improves Radiologists' Screening Performance.

Early diagnosis of lung cancer via computed tomography can significantly reduce the morbidity and mo...

Detecting Respiratory Pathologies Using Convolutional Neural Networks and Variational Autoencoders for Unbalancing Data.

The aim of this paper was the detection of pathologies through respiratory sounds. The ICBHI (Intern...

Second-Generation Sequencing with Deep Reinforcement Learning for Lung Infection Detection.

Recently, deep reinforcement learning, associated with medical big data generated and collected from...

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