Pulmonology

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

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The Emerging Role of Radiomics in COPD and Lung Cancer.

Medical imaging plays a key role in evaluating and monitoring lung diseases such as chronic obstruct...

Determination of causes of death via spectrochemical analysis of forensic autopsies-based pulmonary edema fluid samples with deep learning algorithm.

This study investigated whether infrared spectroscopy combined with a deep learning algorithm could ...

Untethered Soft Robotics with Fully Integrated Wireless Sensing and Actuating Systems for Somatosensory and Respiratory Functions.

There has been a great deal of interest in designing soft robots that can mimic a human system with ...

Investigating the use of data-driven artificial intelligence in computerised decision support systems for health and social care: A systematic review.

There is growing interest in the potential of artificial intelligence to support decision-making in ...

A deep convolutional neural network architecture for interstitial lung disease pattern classification.

Interstitial lung disease (ILD) refers to a group of various abnormal inflammations of lung tissues ...

Image Quality and Lesion Detection on Deep Learning Reconstruction and Iterative Reconstruction of Submillisievert Chest and Abdominal CT.

The objective of this study was to compare image quality and clinically significant lesion detectio...

Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis.

OBJECTIVES: To propose a transfer learning (TL) radiomics model that efficiently combines the inform...

Portable Detection of Apnea and Hypopnea Events Using Bio-Impedance of the Chest and Deep Learning.

Sleep apnea is one of the most common sleep-related breathing disorders. It is diagnosed through an ...

Scalogram based prediction model for respiratory disorders using optimized convolutional neural networks.

Auscultation of the lung is a conventional technique used for diagnosing chronic obstructive pulmona...

Automatic lesion segmentation and classification of hepatic echinococcosis using a multiscale-feature convolutional neural network.

Hepatic echinococcosis (HE) is a life-threatening liver disease caused by parasites that requires a ...

Classification of Interstitial Lung Abnormality Patterns with an Ensemble of Deep Convolutional Neural Networks.

Subtle interstitial changes in the lung parenchyma of smokers, known as Interstitial Lung Abnormalit...

Digging Deeper to Save the Old Anti-tuberculosis Target: D-Alanine-D-Alanine Ligase With a Novel Inhibitor, IMB-0283.

The emergence of drug-resistant (Mtb) has hampered treatments for tuberculosis, which consequently ...

Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India.

In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active pulmon...

A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration.

To achieve accurate and fast deformable image registration (DIR) for pulmonary CT, we proposed a Mul...

Dynamic coronary roadmapping via catheter tip tracking in X-ray fluoroscopy with deep learning based Bayesian filtering.

Percutaneous coronary intervention (PCI) is typically performed with image guidance using X-ray angi...

Biomimetic smoking robot for in vitro inhalation exposure compatible with microfluidic organ chips.

Exposure of lung tissues to cigarette smoke is a major cause of human disease and death worldwide. U...

Retraining an open-source pneumothorax detecting machine learning algorithm for improved performance to medical images.

PURPOSE: To validate a machine learning model trained on an open source dataset and subsequently opt...

An effective approach for CT lung segmentation using mask region-based convolutional neural networks.

Computer vision systems have numerous tools to assist in various medical fields, notably in image di...

Real-time prediction of tumor motion using a dynamic neural network.

Radiation dose delivery into the thoracic and abdomen cavities during radiotherapy treatment is a ch...

A Two-Stage Convolutional Neural Networks for Lung Nodule Detection.

Early detection of lung cancer is an effective way to improve the survival rate of patients. It is a...

Distributed learning on 20 000+ lung cancer patients - The Personal Health Train.

BACKGROUND AND PURPOSE: Access to healthcare data is indispensable for scientific progress and innov...

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