Pulmonology

Interpreting patient-Specific risk prediction using contextual decomposition of BiLSTMs: application to children with asthma.

BACKGROUND: Predictive modeling with longitudinal electronic health record (EHR) data offers great promise for accelerating personalized medicine and better informs clinical decision-making. Recently, deep learning models have achieved state-of-the-art performance for many healthcare prediction tasks. However, deep models lack interpretability, which is integral to successful decision-making and c...

Lung Nodule Detection based on Ensemble of Hand Crafted and Deep Features.

Lung cancer is considered as a deadliest disease worldwide due to which 1.76 million deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer detection at a premature stage is a more significant requirement to reduce the probability of mortality rate. This manuscript depicts an approach of finding lung nodule at an initial stage that comprises of three major phases: ...

Safety and efficacy of Belfeed B MP/ML (endo-1,4-β-xylanase) as a feed additive for sows, in order to have benefits in piglets, and for all porcine species.

Following a request from the European Commission, the Panel on Additives and Products or Substances used in Animal Feed (FEEDAP) was asked to deliver ...

Multi-resolution convolutional neural networks for fully automated segmentation of acutely injured lungs in multiple species.

Segmentation of lungs with acute respiratory distress syndrome (ARDS) is a challenging task due to diffuse opacification in dependent regions which re...

Novelties in imaging in pulmonary fibrosis and nodules. A narrative review.

In recent months two major fields of interest in pulmonary imaging have stood out: pulmonary fibrosis and pulmonary nodules. New guidelines have been ...

Automatic classification of lung nodule candidates based on a novel 3D convolution network and knowledge transferred from a 2D network.

OBJECTIVE: In the automatic lung nodule detection system, the authenticity of a large number of nodule candidates needs to be judged, which is a class...

Pulmonary Textures Classification via a Multi-Scale Attention Network.

Precise classification of pulmonary textures is crucial to develop a computer aided diagnosis (CAD) system of diffuse lung diseases (DLDs). Although d...

Integration of convolutional neural networks for pulmonary nodule malignancy assessment in a lung cancer classification pipeline.

The early identification of malignant pulmonary nodules is critical for a better lung cancer prognosis and less invasive chemo or radio therapies. Nod...

Deep learning, reusable and problem-based architectures for detection of consolidation on chest X-ray images.

BACKGROUND AND OBJECTIVE: In most patients presenting with respiratory symptoms, the findings of chest radiography play a key role in the diagnosis, m...

A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans.

We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability...

Multi-resolution convolutional networks for chest X-ray radiograph based lung nodule detection.

Lung cancer is the leading cause of cancer death worldwide. Early detection of lung cancer is helpful to provide the best possible clinical treatment ...

MediMLP: Using Grad-CAM to Extract Crucial Variables for Lung Cancer Postoperative Complication Prediction.

Lung cancer postoperative complication prediction (PCP) is significant for decreasing the perioperative mortality rate after lung cancer surgery. In t...

ATBdiscrimination: An in Silico Tool for Identification of Active Tuberculosis Disease Based on Routine Blood Test and T-SPOT.TB Detection Results.

Tuberculosis remains one of the deadliest infectious diseases worldwide. Only 5-15% of people infected with develop active TB disease (ATB), while ot...

A machine learning approach for the prediction of pulmonary hypertension.

BACKGROUND: Machine learning (ML) is a powerful tool for identifying and structuring several informative variables for predictive tasks. Here, we inve...

Comprehensive anatomic ontologies for lung development: A comparison of alveolar formation and maturation within mouse and human lung.

BACKGROUND: Although the mouse is widely used to model human lung development, function, and disease, our understanding of the molecular mechanisms in...

Artificial Intelligence Meets Chinese Medicine.

As an interdisciplinary subject of medicine and artificial intelligence, intelligent diagnosis and treatment has received extensive attention. The sta...

Excitation Emission Matrix Fluorescence Spectroscopy for Combustion Generated Particulate Matter Source Identification.

The inhalation of particulate matter (PM) is a significant health risk associated with reduced life expectancy due to increased cardio-pulmonary disea...

Chronic Obstructive Pulmonary Disease: Thoracic CT Texture Analysis and Machine Learning to Predict Pulmonary Ventilation.

Background Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast...

Machine Learning Reveals the Texture of Regional Lung Ventilation at CT.

Use of pressure-regulated volume control in the first 48 hours of hospitalization of mechanically ventilated patients with sepsis or septic shock, with or without ARDS.

PURPOSE: To evaluate the impact of pressure-regulated volume control (PRVC/VC+) use on delivered tidal volumes in patients with acute respiratory dist...