Pulmonology

Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists.

BACKGROUND: Chest radiograph interpretation is critical for the detection of thoracic diseases, including tuberculosis and lung cancer, which affect millions of people worldwide each year. This time-consuming task typically requires expert radiologists to read the images, leading to fatigue-based diagnostic error and lack of diagnostic expertise in areas of the world where radiologists are not ava...

Automated detection of moderate and large pneumothorax on frontal chest X-rays using deep convolutional neural networks: A retrospective study.

BACKGROUND: Pneumothorax can precipitate a life-threatening emergency due to lung collapse and respiratory or circulatory distress. Pneumothorax is typically detected on chest X-ray; however, treatment is reliant on timely review of radiographs. Since current imaging volumes may result in long worklists of radiographs awaiting review, an automated method of prioritizing X-rays with pneumothorax ma...

Measurement and immunophenotyping of pleural fluid EpCAM-positive cells and clusters for the management of non-small cell lung cancer patients.

OBJECTIVES: A malignant pleural effusion (MPE) is a common complication in non-small cell lung cancer (NSCLC) with important staging and prognostic in...

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 available for diagnosing ventilator-associated pn...

Machine learning to identify pairwise interactions between specific IgE antibodies and their association with asthma: A cross-sectional analysis within a population-based birth cohort.

BACKGROUND: The relationship between allergic sensitisation and asthma is complex; the data about the strength of this association are conflicting. We...

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 obstructive type of lung diseases. The previous wo...

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 producing record-breaking results in a variety of c...

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 overwhelming evidence that the environment and lifestyle ...

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 types. A computer-aided diagnosis (CAD) system wa...

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. However, effectively capturing the nodule's struct...

Comparison of the automatic segmentation of multiple organs at risk in CT images of lung cancer between deep convolutional neural network-based and atlas-based techniques.

BACKGROUND: In this study, a deep convolutional neural network (CNN)-based automatic segmentation technique was applied to multiple organs at risk (OA...

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

Purpose To examine Generative Visual Rationales (GVRs) as a tool for visualizing neural network learning of chest radiograph features in congestive he...

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 imaging to provide computer-aided diagnosis (CAD). Rece...

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 management a growing public health problem. Biopsy and...

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 FDG-PET imaging in the setting of ultralow dose P...

Learning-Based Quality Control for Cardiac MR Images.

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameter...

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 lung cancer lesions as T1-T2 or T3-T4 on staging ...

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 using electrical impedance tomography (EIT).

Pulmonary CT Registration Through Supervised Learning With Convolutional Neural Networks.

Deformable image registration can be time consuming and often needs extensive parameterization to perform well on a specific application. We present a...

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. Antibiotic therapy remains the mainstay of TB control...