Pulmonology

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 regarding the integrity of the multiple physiological systems involved in the energetic supply. Longitudinal analysis of the oxygen uptake (V̇o) by wearable sensors in realistic settings might permit development of a practical tool for the study of the longitudinal aerobic system dynamics (i.e., V̇...

Automated Spirometry Quality Assurance: Supervised Learning From Multiple Experts.

Forced spirometry testing is gradually becoming available across different healthcare tiers including primary care. It has been demonstrated in earlier work that commercially available spirometers are not fully able to assure the quality of individual spirometry manoeuvres. Thus, a need to expand the availability of high-quality spirometry assessment beyond specialist pulmonary centres has arisen....

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 machine with sigmoid activation function (S-ELM), ex...

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 resources and controlling pollution. Artificial intelligen...

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 dyspnea by compressing healthy lung parenchyma and ...

Deep monocular 3D reconstruction for assisted navigation in bronchoscopy.

PURPOSE: In bronchoschopy, computer vision systems for navigation assistance are an attractive low-cost solution to guide the endoscopist to target pe...

Enhancing Comparative Effectiveness Research With Automated Pediatric Pneumonia Detection in a Multi-Institutional Clinical Repository: A PHIS+ Pilot Study.

BACKGROUND: Community-acquired pneumonia is a leading cause of pediatric morbidity. Administrative data are often used to conduct comparative effectiv...

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 enables noninvasive estimation of the OEF based on t...

Pulmonary nodule classification with deep residual networks.

UNLABELLED: PURPOSE  : Lung cancer has the highest death rate among all cancers in the USA. In this work we focus on improving the ability of computer...

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 mortality rate. Patients with lung nodules greater th...

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 inflammation, which may lead to lung cancer. The ac...

Feasibility and safety of robot-assisted thoracic surgery for lung lobectomy in patients with non-small cell lung cancer: a systematic review and meta-analysis.

BACKGROUND: The aim of this study is to evaluate the feasibility and safety of robot-assisted thoracic surgery (RATS) lobectomy versus video-assisted ...

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) and alveolar (ARMS) subtypes of rhabdomysarcoma (RMS...

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 the lives of people by limiting their activities ...

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 tuberculosis (TB) on chest radiographs. Materials and Met...

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 scans in the near future, which radiologists will ...

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 activity for the detection of lung disorders usin...

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 sleep apnea because patients with sleep apnea exh...

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 structured algorithms in lung nodule CT image diagno...

Comparison of Machine Learning Approaches for Prediction of Advanced Liver Fibrosis in Chronic Hepatitis C Patients.

BACKGROUND/AIM: Using machine learning approaches as non-invasive methods have been used recently as an alternative method in staging chronic liver di...