AI Medical Compendium Topic:
Lung

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Novel biomarker genes which distinguish between smokers and chronic obstructive pulmonary disease patients with machine learning approach.

BMC pulmonary medicine
BACKGROUND: Chronic obstructive pulmonary disease (COPD) is combination of progressive lung diseases. The diagnosis of COPD is generally based on the pulmonary function testing, however, difficulties underlie in prognosis of smokers or early stage of...

Robotic fluidic coupling and interrogation of multiple vascularized organ chips.

Nature biomedical engineering
Organ chips can recapitulate organ-level (patho)physiology, yet pharmacokinetic and pharmacodynamic analyses require multi-organ systems linked by vascular perfusion. Here, we describe an 'interrogator' that employs liquid-handling robotics, custom s...

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

Artificial intelligence in medicine
Auscultation of the lung is a conventional technique used for diagnosing chronic obstructive pulmonary diseases (COPDs) and lower respiratory infections and disorders in patients. In most of the earlier works, wavelet transforms or spectrograms have ...

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

Scientific reports
Subtle interstitial changes in the lung parenchyma of smokers, known as Interstitial Lung Abnormalities (ILA), have been associated with clinical outcomes, including mortality, even in the absence of Interstitial Lung Disease (ILD). Although several ...

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

Artificial intelligence in medicine
Computer vision systems have numerous tools to assist in various medical fields, notably in image diagnosis. Computed tomography (CT) is the principal imaging method used to assist in the diagnosis of diseases such as bone fractures, lung cancer, hea...

Automatic multi-organ segmentation in dual-energy CT (DECT) with dedicated 3D fully convolutional DECT networks.

Medical physics
PURPOSE: Dual-energy computed tomography (DECT) has shown great potential in many clinical applications. By incorporating the information from two different energy spectra, DECT provides higher contrast and reveals more material differences of tissue...

Semi-Supervised Learning for Semantic Segmentation of Emphysema With Partial Annotations.

IEEE journal of biomedical and health informatics
Segmentation and quantification of each subtype of emphysema is helpful to monitor chronic obstructive pulmonary disease. Due to the nature of emphysema (diffuse pulmonary disease), it is very difficult for experts to allocate semantic labels to ever...

Survey on deep learning for pulmonary medical imaging.

Frontiers of medicine
As a promising method in artificial intelligence, deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing. With medical imaging becoming an important part of disease screening and d...

Deep segmentation networks predict survival of non-small cell lung cancer.

Scientific reports
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/comp...

A fast and scalable method for quality assurance of deformable image registration on lung CT scans using convolutional neural networks.

Medical physics
PURPOSE: To develop and evaluate a method to automatically identify and quantify deformable image registration (DIR) errors between lung computed tomography (CT) scans for quality assurance (QA) purposes.