Pulmonology

Learning Bayesian networks from demographic and health survey data.

Child mortality from preventable diseases such as pneumonia and diarrhoea in low and middle-income countries remains a serious global challenge. We combine knowledge with available Demographic and Health Survey (DHS) data from India, to construct Causal Bayesian Networks (CBNs) and investigate the factors associated with childhood diarrhoea. We make use of freeware tools to learn the graphical str...

Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X ray.

Accurate diagnosis of pulmonary hypertension (PH) is crucial to ensure that patients receive timely treatment. We hypothesized that application of artificial intelligence (AI) to the chest X-ray (CXR) could identify elevated pulmonary artery pressure (PAP) and stratify the risk of heart failure hospitalization with PH. We retrospectively enrolled a total of 900 consecutive patients with suspected ...

Using the National Trauma Data Bank (NTDB) and machine learning to predict trauma patient mortality at admission.

A 400-estimator gradient boosting classifier was trained to predict survival probabilities of trauma patients. The National Trauma Data Bank (NTDB) pr...

Type IV Collagen 7S Is the Most Accurate Test For Identifying Advanced Fibrosis in NAFLD With Type 2 Diabetes.

This study aimed to examine whether the diagnostic accuracy of four noninvasive tests (NITs) for detecting advanced fibrosis in nonalcoholic fatty liv...

FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms.

The novel coronavirus (COVID-19) pneumonia has become a serious health challenge in countries worldwide. Many radiological findings have shown that X-...

Classification and Detection of Breathing Patterns with Wearable Sensors and Deep Learning.

Rapid assessment of breathing patterns is important for several emergency medical situations. In this research, we developed a non-invasive breathing ...

Analytics with artificial intelligence to advance the treatment of acute respiratory distress syndrome.

Artificial intelligence (AI) has found its way into clinical studies in the era of big data. Acute respiratory distress syndrome (ARDS) or acute lung ...

Neuromorphic on-chip recognition of saliva samples of COPD and healthy controls using memristive devices.

Chronic Obstructive Pulmonary Disease (COPD) is a life-threatening lung disease, affecting millions of people worldwide. Implementation of Machine Lea...

Machine learning in predicting respiratory failure in patients with COVID-19 pneumonia-Challenges, strengths, and opportunities in a global health emergency.

AIMS: The aim of this study was to estimate a 48 hour prediction of moderate to severe respiratory failure, requiring mechanical ventilation, in hospi...

Simulated four-dimensional CT for markerless tumor tracking using a deep learning network with multi-task learning.

INTRODUCTION: Our markerless tumor tracking algorithm requires 4DCT data to train models. 4DCT cannot be used for markerless tracking for respiratory-...

CheXLocNet: Automatic localization of pneumothorax in chest radiographs using deep convolutional neural networks.

BACKGROUND: Pneumothorax can lead to a life-threatening emergency. The experienced radiologists can offer precise diagnosis according to the chest rad...

Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning.

BACKGROUND: Identifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational appr...

Artificial intelligence solution to classify pulmonary nodules on CT.

PURPOSE: The purpose of this study was to create an algorithm to detect and classify pulmonary nodules in two categories based on their volume greater...

Capsules for biomedical image segmentation.

Our work expands the use of capsule networks to the task of object segmentation for the first time in the literature. This is made possible via the in...

Artificial intelligence identifies inflammation and confirms fibroblast foci as prognostic tissue biomarkers in idiopathic pulmonary fibrosis.

A large number of fibroblast foci (FF) predict mortality in idiopathic pulmonary fibrosis (IPF). Other prognostic histological markers have not been i...

Deep learning networks on chronic liver disease assessment with fine-tuning of shear wave elastography image sequences.

Chronic liver disease (CLD) is currently one of the major causes of death worldwide. If not treated, it may lead to cirrhosis, hepatic carcinoma and d...

Deep learning-based model for detecting 2019 novel coronavirus pneumonia on high-resolution computed tomography.

Computed tomography (CT) is the preferred imaging method for diagnosing 2019 novel coronavirus (COVID19) pneumonia. We aimed to construct a system bas...

A gradient boosting machine learning approach in modeling the impact of temperature and humidity on the transmission rate of COVID-19 in India.

Meteorological parameters were crucial and effective factors in past infectious diseases, like influenza and severe acute respiratory syndrome (SARS),...

A Model Using Support Vector Machines Recursive Feature Elimination (SVM-RFE) Algorithm to Classify Whether COPD Patients Have Been Continuously Managed According to GOLD Guidelines.

PURPOSE: Patients with chronic obstructive pulmonary disease (COPD) would have a poor prognosis if they were not continuously managed according to the...

Predictive diagnosis of chronic obstructive pulmonary disease using serum metabolic biomarkers and least-squares support vector machine.

OBJECTIVE: Development of biofluid-based biomarkers is attractive for the diagnosis of chronic obstructive pulmonary disease (COPD) but still lacking....