Pulmonology

Effect of Radiation Doses to the Heart on Survival for Stereotactic Ablative Radiotherapy for Early-stage Non-Small-cell Lung Cancer: An Artificial Neural Network Approach.

INTRODUCTION: The cardiac radiation dose is an important predictor of cardiac toxicity and overall survival (OS) for patients with locally advanced non-small-cell lung cancer (NSCLC). However, radiation-induced cardiac toxicity among patients with early-stage NSCLC who have undergone stereotactic ablative radiotherapy (SABR) has been less well-characterized. Our objective was to assess the associa...

Dehydroepiandrosterone Supplementation May Benefit Women with Asthma Who Have Low Androgen Levels: A Pilot Study.

INTRODUCTION: Among individuals with severe asthma, FEV is low in individuals with low dehydroepiandrosterone (DHEA) sulfate (DHEAS) levels. In the Severe Asthma Research Program (SARP), no women with DHEAS > 200 μg/dL had an FEV < 60% predicted. DHEA has benefited patients with COPD and pulmonary hypertension in small trials. Therefore, we hypothesized that DHEA supplementation may improve FEV in...

Effect of dexmedetomidine sedation on swallowing reflex: A pilot study.

BACKGROUND/PURPOSE: Swallowing reflex depression during dental treatment or oral surgery may cause water to enter the lower respiratory tract, leading...

Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems.

Deep learning (DL) neural networks have only recently been employed to interpret chest radiography (CXR) to screen and triage people for pulmonary tub...

Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method.

BACKGROUND: Current lung cancer screening guidelines use mean diameter, volume or density of the largest lung nodule in the prior computed tomography ...

Identifying tuberculous pleural effusion using artificial intelligence machine learning algorithms.

BACKGROUND: The differential diagnosis of tuberculous pleural effusion (TPE) is challenging. In recent years, artificial intelligence (AI) machine lea...

MR-Forest: A Deep Decision Framework for False Positive Reduction in Pulmonary Nodule Detection.

With the development of deep learning methods such as convolutional neural network (CNN), the accuracy of automated pulmonary nodule detection has bee...

Outpatient Anesthesia Facilitates Stereotactic Body Radiation Therapy for Early Stage Lung Cancer Patients With Advanced Cognitive Impairments.

PURPOSE: To report on the use of outpatient anesthesia (OPA) facilitating delivery of stereotactic body radiation therapy (SBRT) in patients with seve...

Rapid multiplex microfiber-based immunoassay for anti-MERS-CoV antibody detection.

On-site multiplex biosensors for innate immunity antibodies are ideal tools for monitoring health status of individuals against various diseases. This...

A comparative study of machine learning classifiers for risk prediction of asthma disease.

Asthma is a chronic disease characterized by wheezing, chest tightening and difficulty in breathing due to inflammation of lung airways. Early risk pr...

A New Multimodel Machine Learning Framework to Improve Hepatic Fibrosis Grading Using Ultrasound Elastography Systems from Different Vendors.

The purpose of the work described here was to determine if the diagnostic performance of point and 2-D shear wave elastography (pSWE; 2-DSWE) using sh...

ICU staffing feature phenotypes and their relationship with patients' outcomes: an unsupervised machine learning analysis.

PURPOSE: To study whether ICU staffing features are associated with improved hospital mortality, ICU length of stay (LOS) and duration of mechanical v...

DeepBranch: Deep Neural Networks for Branch Point Detection in Biomedical Images.

Morphology reconstruction of tree-like structures in volumetric images, such as neurons, retinal blood vessels, and bronchi, is of fundamental interes...

Application of Artificial Intelligence to Gastroenterology and Hepatology.

Since 2010, substantial progress has been made in artificial intelligence (AI) and its application to medicine. AI is explored in gastroenterology for...

Machine Learning Approach for Prediction of Hematic Parameters in Hemodialysis Patients.

This paper shows the application of machine learning techniques to predict hematic parameters using blood visible spectra during ex-vivo treatments. ...

Predicting post-stroke pneumonia using deep neural network approaches.

BACKGROUND AND PURPOSE: Pneumonia is a common complication after stroke, causing an increased length of hospital stay and death. Therefore, the timely...

Predictive model for acute respiratory distress syndrome events in ICU patients in China using machine learning algorithms: a secondary analysis of a cohort study.

BACKGROUND: To develop a machine learning model for predicting acute respiratory distress syndrome (ARDS) events through commonly available parameters...

Clinical Study on Minimally Invasive Liquefaction and Drainage of Hypertensive Putaminal Hemorrhage through Frontal Approach.

 Hypertensive intracerebral hemorrhage is one of the most common cerebrovascular diseases with high mortality and high disability rate. The aim of th...

Robotic anatomic pulmonary segmentectomy: technical approach and outcomes.

OBJECTIVE: to report our initial experience with pulmonary robotic segmentectomy, describing the surgical technique, the preferred positioning of port...

Comparison of CT and MRI images for the prediction of soft-tissue sarcoma grading and lung metastasis via a convolutional neural networks model.

AIM: To realise the automated prediction of soft-tissue sarcoma (STS) grading and lung metastasis based on computed tomography (CT), T1-weighted (T1W)...