Pulmonology

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

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: deep learning-based radiomics for the time-to-event outcome prediction in lung cancer.

Hand-crafted radiomics has been used for developing models in order to predict time-to-event clinica...

Drawing insights from COVID-19-infected patients using CT scan images and machine learning techniques: a study on 200 patients.

As the whole world is witnessing what novel coronavirus (COVID-19) can do to the mankind, it present...

DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images.

While many pre-defined computed tomographic (CT) measures have been utilized to characterize chronic...

Discrimination between transient and persistent subsolid pulmonary nodules on baseline CT using deep transfer learning.

OBJECTIVES: To develop and validate a deep learning model to discriminate transient from persistent ...

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

Background It is uncertain whether a deep learning-based automatic detection algorithm (DLAD) for id...

Artificial Intelligence Methods for Constructing Wine Barrels with a Controlled Oxygen Transmission Rate.

Oxygen is an important factor in the wine aging process, and the oxygen transmission rate (OTR) is t...

Implementation of data intelligence models coupled with ensemble machine learning for prediction of water quality index.

In recent decades, various conventional techniques have been formulated around the world to evaluate...

From community-acquired pneumonia to COVID-19: a deep learning-based method for quantitative analysis of COVID-19 on thick-section CT scans.

OBJECTIVE: To develop a fully automated AI system to quantitatively assess the disease severity and ...

Time-distanced gates in long short-term memory networks.

The Long Short-Term Memory (LSTM) network is widely used in modeling sequential observations in fiel...

Diagnosing Heart Failure from Chest X-Ray Images Using Deep Learning.

The development of deep learning technology has enabled machines to achieve high-level accuracy in i...

Development of an artificial intelligence diagnostic model based on dynamic uncertain causality graph for the differential diagnosis of dyspnea.

Dyspnea is one of the most common manifestations of patients with pulmonary disease, myocardial dysf...

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma.

Early cancer detection greatly increases the chances for successful treatment, but available diagnos...

Predicting respiratory failure after pulmonary lobectomy using machine learning techniques.

BACKGROUND: When pulmonary complications occur, postlobectomy patients have a higher mortality rate,...

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

BACKGROUND: The novel coronavirus disease 2019 (COVID-19) is an emerging worldwide threat to public ...

[Technological Innovations in Pulmonology - Examples from Diagnostics and Therapy].

A significant proportion of the current technological developments in pneumology originate from the ...

Accuracy of deep learning for automated detection of pneumonia using chest X-Ray images: A systematic review and meta-analysis.

BACKGROUND: Recently, deep learning (DL) algorithms have received widespread popularity in various m...

Predicting the Risk of Adverse Events in Pregnant Women With Congenital Heart Disease.

Background Women with congenital heart disease are considered at high risk for adverse events. There...

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

BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contribut...

Can AI outperform a junior resident? Comparison of deep neural network to first-year radiology residents for identification of pneumothorax.

PURPOSE: To (1) develop a deep learning system (DLS) using a deep convolutional neural network (DCNN...

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