AIMC Topic: Lung

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Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs.

PloS one
This study employed deep-learning convolutional neural networks to stage lung disease severity of Coronavirus Disease 2019 (COVID-19) infection on portable chest x-ray (CXR) with radiologist score of disease severity as ground truth. This study consi...

Deep Learning-based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs.

Radiology
Background It is uncertain whether a deep learning-based automatic detection algorithm (DLAD) for identifying malignant nodules on chest radiographs will help diagnose lung cancers. Purpose To evaluate the efficacy of using a DLAD in observer perform...

Predicting respiratory failure after pulmonary lobectomy using machine learning techniques.

Surgery
BACKGROUND: When pulmonary complications occur, postlobectomy patients have a higher mortality rate, increased length of stay, and higher readmission rates. Because of a lack of high-quality consolidated clinical data, it is challenging to assess and...

Automated detection and quantification of COVID-19 pneumonia: CT imaging analysis by a deep learning-based software.

European journal of nuclear medicine and molecular imaging
BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public health. While chest computed tomography (CT) plays an indispensable role in its diagnosis, the quantification and localization of lesions cannot be ac...

Deep neural network analyses of spirometry for structural phenotyping of chronic obstructive pulmonary disease.

JCI insight
BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow obstruction. We hypothesized that machine-learning algorithms can be trained on spirometry data to ide...

A deep learning approach to characterize 2019 coronavirus disease (COVID-19) pneumonia in chest CT images.

European radiology
OBJECTIVES: To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quantitative determination performance with radiological residents.

Automated labeling of the airway tree in terms of lobes based on deep learning of bifurcation point detection.

Medical & biological engineering & computing
This paper presents an automatic lobe-based labeling of airway tree method, which can detect the bifurcation points for reconstructing and labeling the airway tree from a computed tomography image. A deep learning-based network structure is designed ...