Pulmonology

Cascaded one-shot deformable convolutional neural networks: Developing a deep learning model for respiratory motion estimation in ultrasound sequences.

Improving the quality of image-guided radiation therapy requires the tracking of respiratory motion in ultrasound sequences. However, the low signal-to-noise ratio and the artifacts in ultrasound images make it difficult to track targets accurately and robustly. In this study, we propose a novel deep learning model, called a Cascaded One-shot Deformable Convolutional Neural Network (COSD-CNN), to ...

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

The Long Short-Term Memory (LSTM) network is widely used in modeling sequential observations in fields ranging from natural language processing to medical imaging. The LSTM has shown promise for interpreting computed tomography (CT) in lung screening protocols. Yet, traditional image-based LSTM models ignore interval differences, while recently proposed interval-modeled LSTM variants are limited i...

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 interpreting medical images. While many previous st...

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 dysfunction, and neuromuscular disorder, among other c...

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

Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcino...

Predicting respiratory failure after pulmonary lobectomy using machine learning techniques.

BACKGROUND: When pulmonary complications occur, postlobectomy patients have a higher mortality rate, increased length of stay, and higher readmission ...

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 health. While chest computed tomography (CT) plays...

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

A significant proportion of the current technological developments in pneumology originate from the various areas of information technology. The spect...

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 medical diagnostics. This study aimed to evaluate t...

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. Therefore, we aim to establish 2 prediction models for ...

Use of artificial intelligence in diagnosis of head and neck precancerous and cancerous lesions: A systematic review.

This systematic review analyses and describes the application and diagnostic accuracy of Artificial Intelligence (AI) methods used for detection and g...

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

BACKGROUNDCurrently recommended traditional spirometry outputs do not reflect the relative contributions of emphysema and airway disease to airflow ob...

The first use of artificial intelligence (AI) in the ER: triage not diagnosis.

Predictions related to the impact of AI on radiology as a profession run the gamut from AI putting radiologists out of business to having no effect at...

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) for identification of pneumothorax, (2) compare ...

Using trauma registry data to predict prolonged mechanical ventilation in patients with traumatic brain injury: Machine learning approach.

OBJECTIVES: We aimed to build a machine learning predictive model to predict the risk of prolonged mechanical ventilation (PMV) for patients with Trau...

Machine learning-based prediction of acute severity in infants hospitalized for bronchiolitis: a multicenter prospective study.

We aimed to develop machine learning models to accurately predict bronchiolitis severity, and to compare their predictive performance with a conventio...

Predicting the chemical reactivity of organic materials using a machine-learning approach.

Stability and compatibility between chemical components are essential parameters that need to be considered in the selection of functional materials i...

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

OBJECTIVES: To utilize a deep learning model for automatic detection of abnormalities in chest CT images from COVID-19 patients and compare its quanti...

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

This paper presents an automatic lobe-based labeling of airway tree method, which can detect the bifurcation points for reconstructing and labeling th...

Pulmonary Embolism at CT Pulmonary Angiography in Patients with COVID-19.

PURPOSE: To evaluate pulmonary embolism (PE) prevalence at CT pulmonary angiography in patients testing positive for coronavirus disease 2019 (COVID-1...