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Lung

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Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton ( H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acqui...

Deep Learning-based Approach to Predict Pulmonary Function at Chest CT.

Radiology
Background Low-dose chest CT screening is recommended for smokers with the potential for lung function abnormality, but its role in predicting lung function remains unclear. Purpose To develop a deep learning algorithm to predict pulmonary function w...

Motion compensated self supervised deep learning for highly accelerated 3D ultrashort Echo time pulmonary MRI.

Magnetic resonance in medicine
PURPOSE: To investigate motion compensated, self-supervised, model based deep learning (MBDL) as a method to reconstruct free breathing, 3D pulmonary UTE acquisitions.

Ultra-low-dose CT lung screening with artificial intelligence iterative reconstruction: evaluation via automatic nodule-detection software.

Clinical radiology
AIM: To test the feasibility of ultra-low-dose (ULD) computed tomography (CT) combined with an artificial intelligence iterative reconstruction (AIIR) algorithm for screening pulmonary nodules using computer-assisted diagnosis (CAD).

[Robot-assisted Lung Surgery: Techniques, Evidence and Data on Anatomical Resections].

Zentralblatt fur Chirurgie
Thanks to improved visualisation and instruments with an endowrist function, robot-assisted thoracic surgery has led to technical progress in thoracic surgery. This makes it easier to carry out complex thoracic surgical interventions, e.g. with an in...

Improved UNet Deep Learning Model for Automatic Detection of Lung Cancer Nodules.

Computational intelligence and neuroscience
Uncontrolled cell growth in the two spongy lung organs in the chest is the most prevalent kind of cancer. When cells from the lungs spread to other tissues and organs, this is referred to as metastasis. This work uses image processing, deep learning,...

Compressed Sensing Data with Performing Audio Signal Reconstruction for the Intelligent Classification of Chronic Respiratory Diseases.

Sensors (Basel, Switzerland)
Chronic obstructive pulmonary disease (COPD) concerns the serious decline of human lung functions. These have emerged as one of the most concerning health conditions over the last two decades, after cancer around the world. The early diagnosis of COP...

Evaluation of emphysema on thoracic low-dose CTs through attention-based multiple instance deep learning.

Scientific reports
In addition to lung cancer, other thoracic abnormalities, such as emphysema, can be visualized within low-dose CT scans that were initially obtained in cancer screening programs, and thus, opportunistic evaluation of these diseases may be highly valu...

Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU.

Critical care medicine
OBJECTIVES: To evaluate the accuracy of a bedside, real-time deployment of a deep learning (DL) model capable of distinguishing between normal (A line pattern) and abnormal (B line pattern) lung parenchyma on lung ultrasound (LUS) in critically ill p...

Deep Learning-Based Segmentation of Airway Morphology from Endobronchial Optical Coherence Tomography.

Respiration; international review of thoracic diseases
BACKGROUND: Manual measurement of endobronchial optical coherence tomography (EB-OCT) images means a heavy workload in the clinical practice, which can also introduce bias if the subjective opinions of doctors are involved.