Pulmonology

Identification of differentially expressed genes associated with asthma in children based on the bioanalysis of the regulatory network.

Asthma, the most common chronic respiratory tract disease in children, is characterized by allergy, recurring airway obstruction and bronchospasm. The aim of the present study was to screen critical differentially expressed genes (DEGs) involved in asthma in children. Gene expression in different tissues was compared between asthmatic children and healthy control subjects in order to identify DEGs...

The utilisation of convolutional neural networks in detecting pulmonary nodules: a review.

Lung cancer is one of the leading causes of cancer-related fatality in the world. Patients display few or even no signs or symptoms in the early stages, resulting in up to 75% of patients diagnosed in the later stages of the disease. Consequently, there has been a call for lung cancer screening amongst at-risk populations. The early detection of malignant pulmonary nodules in CT is one of the sugg...

Adaptive multinomial regression with overlapping groups for multi-class classification of lung cancer.

Multi-class classification has attracted much attention in cancer diagnosis and treatment and many machine learning methods have emerged for addressin...

Peripheral bronchial identification on chest CT using unsupervised machine learning.

PURPOSE: To automatically identify small- to medium-diameter bronchial segments distributed throughout the lungs.

A Nasal Brush-based Classifier of Asthma Identified by Machine Learning Analysis of Nasal RNA Sequence Data.

Asthma is a common, under-diagnosed disease affecting all ages. We sought to identify a nasal brush-based classifier of mild/moderate asthma. 190 subj...

The comparison of pleural fluid TNF-α levels in tuberculous and nontuberculous pleural effusion.

BACKGROUND: Tuberculous pleural effusion is the manifestation of Mycobacterium tuberculosis infection in pleura. With existing means, it is difficult ...

Inhalable Fucoidan Microparticles Combining Two Antitubercular Drugs with Potential Application in Pulmonary Tuberculosis Therapy.

The pulmonary delivery of antitubercular drugs is a promising approach to treat lung tuberculosis. This strategy not only allows targeting the infecte...

Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs.

Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common...

Modeling asynchronous event sequences with RNNs.

Sequences of events have often been modeled with computational techniques, but typical preprocessing steps and problem settings do not explicitly addr...

Profiling Lung Cancer Patients Using Electronic Health Records.

If Electronic Health Records contain a large amount of information about the patient's condition and response to treatment, which can potentially revo...

Prediction of radiographic abnormalities by the use of bag-of-features and convolutional neural networks.

This study evaluated the feasibility of bag-of-features (BOF) and convolutional neural networks (CNN) for computer-aided detection in distinguishing n...

Chronic obstructive lung disease "expert system": validation of a predictive tool for assisting diagnosis.

PURPOSE: The purposes of this study were development and validation of an expert system (ES) aimed at supporting the diagnosis of chronic obstructive ...

Classification of Sputum Sounds Using Artificial Neural Network and Wavelet Transform.

Sputum sounds are biological signals used to evaluate the condition of sputum deposition in a respiratory system. To improve the efficiency of intensi...

Screening for active pulmonary tuberculosis: Development and applicability of artificial neural network models.

Tuberculosis (TB) remains a significant public health challenge, motivated by the diversity of healthcare epidemiological settings, as other factors. ...

Segmentation of histological images and fibrosis identification with a convolutional neural network.

Segmentation of histological images is one of the most crucial tasks for many biomedical analyses involving quantification of certain tissue types, su...

Deep learning for staging liver fibrosis on CT: a pilot study.

OBJECTIVES: To investigate whether liver fibrosis can be staged by deep learning techniques based on CT images.

Harnessing the evolutionary information on oxygen binding proteins through Support Vector Machines based modules.

OBJECTIVES: The arrival of free oxygen on the globe, aerobic life is becoming possible. However, it has become very clear that the oxygen binding prot...

Ultra-protective mechanical ventilation without extra-corporeal carbon dioxide removal for acute respiratory distress syndrome.

BACKGROUND: Tidal hyperinflation can still occur with mechanical ventilation using low tidal volume (LVT) (6 mL/kg predicted body weight (PBW)) in acu...

Using machine learning to identify patterns of lifetime health problems in decedents with autism spectrum disorder.

Very little is known about the health problems experienced by individuals with autism spectrum disorder (ASD) throughout their life course. We retrosp...

Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: a prospective multicentre study.

OBJECTIVE: We aimed to evaluate the performance of the newly developed deep learning Radiomics of elastography (DLRE) for assessing liver fibrosis sta...