Pulmonology

A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration.

To achieve accurate and fast deformable image registration (DIR) for pulmonary CT, we proposed a Multi-scale DIR framework with unsupervised Joint training of Convolutional Neural Network (MJ-CNN). MJ-CNN contains three models at multi-scale levels for a coarse-to-fine DIR to avoid being trapped in a local minimum. It is trained based on image similarity and deformation vector field (DVF) smoothne...

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering.

Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angiograms in which coronary arteries are opacified with X-ray opaque contrast agents. Interventional cardiologists typically navigate instruments using non-contrast-enhanced fluoroscopic images, since higher use of contrast agents increases the risk of kidney failure. When using fluoroscopic images, th...

Biomimetic smoking robot for in vitro inhalation exposure compatible with microfluidic organ chips.

Exposure of lung tissues to cigarette smoke is a major cause of human disease and death worldwide. Unfortunately, adequate model systems that can reli...

Retraining an open-source pneumothorax detecting machine learning algorithm for improved performance to medical images.

PURPOSE: To validate a machine learning model trained on an open source dataset and subsequently optimize it to chest X-rays with large pneumothoraces...

Real-time prediction of tumor motion using a dynamic neural network.

Radiation dose delivery into the thoracic and abdomen cavities during radiotherapy treatment is a challenging task as respiratory motion leads to the ...

An effective approach for CT lung segmentation using mask region-based convolutional neural networks.

Computer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal...

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection.

Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a critical step to have accurate detection of lung ...

A Convolutional Neural Network for Real Time Classification, Identification, and Labelling of Vocal Cord and Tracheal Using Laryngoscopy and Bronchoscopy Video.

BACKGROUND: The use of artificial intelligence, including machine learning, is increasing in medicine. Use of machine learning is rising in the predic...

A Multimodal Wearable System for Continuous and Real-Time Breathing Pattern Monitoring During Daily Activity.

OBJECTIVE: This study aims to understand breathing patterns during daily activities by developing a wearable respiratory and activity monitoring (WRAM...

Semi-Supervised Learning for Semantic Segmentation of Emphysema With Partial Annotations.

Segmentation and quantification of each subtype of emphysema is helpful to monitor chronic obstructive pulmonary disease. Due to the nature of emphyse...

A quite sensitive fluorescent loop-mediated isothermal amplification for rapid detection of respiratory syncytial virus.

INTRODUCTION: Human respiratory syncytial virus (hRSV) is a common respiratory virus closely related to respiratory tract infection (RTI). Rapid and a...

A modern approach to identifying and characterizing child asthma and wheeze phenotypes based on clinical data.

'Asthma' is a complex disease that encapsulates a heterogeneous group of phenotypes and endotypes. Research to understand these phenotypes has previou...

Machine Learning Characterization of COPD Subtypes: Insights From the COPDGene Study.

COPD is a heterogeneous syndrome. Many COPD subtypes have been proposed, but there is not yet consensus on how many COPD subtypes there are and how th...

Unsupervised machine learning for the discovery of latent disease clusters and patient subgroups using electronic health records.

Machine learning has become ubiquitous and a key technology on mining electronic health records (EHRs) for facilitating clinical research and practice...

Comparison of the predictive outcomes for anti-tuberculosis drug-induced hepatotoxicity by different machine learning techniques.

BACKGROUND: The study compared the predictive outcomes of artificial neural network, support vector machine and random forest on the occurrence of ant...

Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.

The wide adoption of electronic health record systems in health care generates big real-world data that open new venues to conduct clinical research. ...

The doctor will see you now: How machine learning and artificial intelligence can extend our understanding and treatment of asthma.

An antifouling peptide-based biosensor for determination of Streptococcus pneumonia markers in human serum.

We report a peptide-based sensor that involves a multivalent interaction with L-ascorbate 6-phosphate lactonase (UlaG), a protein marker of Streptococ...

Unravelling machine learning: insights in respiratory medicine.

Predicting fish kills and toxic blooms in an intensive mariculture site in the Philippines using a machine learning model.

Harmful algal blooms (HABs) that produce toxins and those that lead to fish kills are global problems that appear to be increasing in frequency and ex...