Pulmonology

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

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Fully automatic detection of lung nodules in CT images using a hybrid feature set.

PURPOSE: The aim of this study was to develop a novel technique for lung nodule detection using an o...

What Is High Enough? Elevated NT-pro-BNP in Decompensated Paroxysmal Supraventricular Tachycardia.

Since the late 1980s, elevated atrial natriuretic peptide (ANP) was considered the cause of brisk di...

Extracting aerobic system dynamics during unsupervised activities of daily living using wearable sensor machine learning models.

Physical activity levels are related through algorithms to the energetic demand, with no information...

Automated Spirometry Quality Assurance: Supervised Learning From Multiple Experts.

Forced spirometry testing is gradually becoming available across different healthcare tiers includin...

Extreme learning machines: a new approach for modeling dissolved oxygen (DO) concentration with and without water quality variables as predictors.

In this paper, several extreme learning machine (ELM) models, including standard extreme learning ma...

Prediction of dissolved oxygen concentration in hypoxic river systems using support vector machine: a case study of Wen-Rui Tang River, China.

Accurate quantification of dissolved oxygen (DO) is critically important for managing water resource...

Robot-assisted thoracoscopic lobectomy as treatment of a giant bulla.

BACKGROUND: A bulla is a marked enlarged space within the parenchyma of the lung. Bullae may cause d...

Deep monocular 3D reconstruction for assisted navigation in bronchoscopy.

PURPOSE: In bronchoschopy, computer vision systems for navigation assistance are an attractive low-c...

Oxygen extraction fraction mapping at 3 Tesla using an artificial neural network: A feasibility study.

PURPOSE: The oxygen extraction fraction (OEF) is an important biomarker for tissue-viability. MRI en...

Pulmonary nodule classification with deep residual networks.

UNLABELLED: PURPOSE  : Lung cancer has the highest death rate among all cancers in the USA. In this ...

Computed tomography (CT)-compatible remote center of motion needle steering robot: Fusing CT images and electromagnetic sensor data.

Lung cancer is the most common cause of cancer-related death, and early detection can reduce the mor...

Epithelial-mesenchymal transition effect of fine particulate matter from the Yangtze River Delta region in China on human bronchial epithelial cells.

Epidemiological studies have demonstrated that fine particulate matter (PM) exposure causes airway i...

Transfer learning on fused multiparametric MR images for classifying histopathological subtypes of rhabdomyosarcoma.

This paper presents a deep-learning-based CADx for the differential diagnosis of embryonal (ERMS) an...

Early Detection of Peak Demand Days of Chronic Respiratory Diseases Emergency Department Visits Using Artificial Neural Networks.

Chronic respiratory diseases, mainly asthma and chronic obstructive pulmonary disease (COPD), affect...

Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks.

Purpose To evaluate the efficacy of deep convolutional neural networks (DCNNs) for detecting tubercu...

Towards automatic pulmonary nodule management in lung cancer screening with deep learning.

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT ...

Feature selection using ant colony optimization with tandem-run recruitment to diagnose bronchitis from CT scan images.

BACKGROUND AND OBJECTIVES: Computer-aided diagnosis (CAD) plays a vital role in the routine clinical...

Adaptive neuro-fuzzy inference system for breath phase detection and breath cycle segmentation.

BACKGROUND: The monitoring of the respiratory rate is vital in several medical conditions, including...

Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis.

This study aimed to analyze the ability of extracting automatically generated features using deep st...

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