Pulmonology

Latest AI and machine learning research in pulmonology for healthcare professionals.

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Non-directed bronchial lavage is a safe method for sampling the respiratory tract in critically ill patient.

Ventilated patients are at risk of acquiring ventilator-associated pneumonia. Various techniques are...

Wheeze type classification using non-dyadic wavelet transform based optimal energy ratio technique.

BACKGROUND AND OBJECTIVE: Wheezes in pulmonary sounds are anomalies which are often associated with ...

3-D Convolutional Neural Networks for Automatic Detection of Pulmonary Nodules in Chest CT.

Deep two-dimensional (2-D) convolutional neural networks (CNNs) have been remarkably successful in p...

Multiple Human-Behaviour Indicators for Predicting Lung Cancer Mortality with Support Vector Machine.

Lung cancer is still one of the most common causes of death around the world, while there is overwhe...

A machine learning texture model for classifying lung cancer subtypes using preliminary bronchoscopic findings.

PURPOSE: Bronchoscopy is useful in lung cancer detection, but cannot be used to differentiate cancer...

A Lightweight Multi-Section CNN for Lung Nodule Classification and Malignancy Estimation.

The size and shape of a nodule are the essential indicators of malignancy in lung cancer diagnosis. ...

Chest Radiographs in Congestive Heart Failure: Visualizing Neural Network Learning.

Purpose To examine Generative Visual Rationales (GVRs) as a tool for visualizing neural network lear...

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study.

BACKGROUND: There is interest in using convolutional neural networks (CNNs) to analyze medical imagi...

Machine learning to predict lung nodule biopsy method using CT image features: A pilot study.

Computed tomography (CT)-based screening on lung cancer mortality is poised to make lung nodule mana...

Automated detection of lung cancer at ultralow dose PET/CT by deep neural networks - Initial results.

OBJECTIVES: We evaluated whether machine learning may be helpful for the detection of lung cancer in...

Learning-Based Quality Control for Cardiac MR Images.

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the op...

Convolutional Neural Networks Promising in Lung Cancer T-Parameter Assessment on Baseline FDG-PET/CT.

AIM: To develop an algorithm, based on convolutional neural network (CNN), for the classification of...

Monitoring changes in distribution of pulmonary ventilation by functional electrical impedance tomography in anaesthetized ponies.

OBJECTIVE: To assess changes in the distribution in pulmonary ventilation in anaesthetized ponies us...

Pulmonary CT Registration Through Supervised Learning With Convolutional Neural Networks.

Deformable image registration can be time consuming and often needs extensive parameterization to pe...

Design, synthesis, and biological evaluation of novel benzimidazole tethered allylidenehydrazinylmethylthiazole derivatives as potent inhibitors of .

Tuberculosis (TB) has become one of the most significant public health problems in recent years. Ant...

Low vitamin D at ICU admission is associated with cancer, infections, acute respiratory insufficiency, and liver failure.

OBJECTIVES: Vitamin D deficiency may be associated with comorbidities and poor prognosis. However, t...

Prediction and functional analysis of prokaryote lysine acetylation site by incorporating six types of features into Chou's general PseAAC.

Lysine acetylation is one of the most important types of protein post-translational modifications (P...

Analysis of tuberculosis disease through Raman spectroscopy and machine learning.

We present the effectiveness of Raman spectroscopy (RS) in combination with machine learning for scr...

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