Pulmonology

A survey of machine learning-based methods for COVID-19 medical image analysis.

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 de...

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

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 COPD, particularly of lung function degradation, together with monitoring the condition by physicians, and predicting the likelihood of exacerbation even...

IoMT-Enabled Computer-Aided Diagnosis of Pulmonary Embolism from Computed Tomography Scans Using Deep Learning.

The Internet of Medical Things (IoMT) has revolutionized Ambient Assisted Living (AAL) by interconnecting smart medical devices. These devices generat...

Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population.

In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with...

Effect of Contrast Level and Image Format on a Deep Learning Algorithm for the Detection of Pneumothorax with Chest Radiography.

Under the black-box nature in the deep learning model, it is uncertain how the change in contrast level and format affects the performance. We aimed t...

CNN-RNN Network Integration for the Diagnosis of COVID-19 Using Chest X-ray and CT Images.

The 2019 coronavirus disease (COVID-19) has rapidly spread across the globe. It is crucial to identify positive cases as rapidly as humanely possible ...

Free-breathing and instantaneous abdominal T mapping via single-shot multiple overlapping-echo acquisition and deep learning reconstruction.

OBJECTIVES: To develop a real-time abdominal T mapping method without requiring breath-holding or respiratory-gating.

Electrocardiogram Detection of Pulmonary Hypertension Using Deep Learning.

BACKGROUND: Pulmonary hypertension (PH) is life-threatening, and often diagnosed late in its course. We aimed to evaluate if a deep learning approach ...

Deep learning identified genetic variants for COVID-19-related mortality among 28,097 affected cases in UK Biobank.

Analysis of host genetic components provides insights into the susceptibility and response to viral infection such as severe acute respiratory syndrom...

Implementing deep learning models for the classification of Echinococcus multilocularis infection in human liver tissue.

BACKGROUND: The histological diagnosis of alveolar echinococcosis can be challenging. Decision support models based on deep learning (DL) are increasi...

Imaging-derived biomarkers in Asthma: Current status and future perspectives.

Asthma is a common disorder affecting around 315 million individuals worldwide. The heterogeneity of asthma is becoming increasingly important in the ...

Classification of COVID-19 from community-acquired pneumonia: Boosting the performance with capsule network and maximum intensity projection image of CT scans.

BACKGROUND: The coronavirus disease 2019 (COVID-19) and community-acquired pneumonia (CAP) present a high degree of similarity in chest computed tomog...

COVID-19 Classification on Chest X-ray Images Using Deep Learning Methods.

Since December 2019, the coronavirus disease has significantly affected millions of people. Given the effect this disease has on the pulmonary systems...

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

In addition to lung cancer, other thoracic abnormalities, such as emphysema, can be visualized within low-dose CT scans that were initially obtained i...

Survival prediction for stage I-IIIA non-small cell lung cancer using deep learning.

BACKGROUND AND PURPOSE: The aim of this study was to develop and evaluate a prediction model for 2-year overall survival (OS) in stage I-IIIA non-smal...

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

OBJECTIVES: To evaluate the accuracy of a bedside, real-time deployment of a deep learning (DL) model capable of distinguishing between normal (A line...

Artificial intelligence in lung cancer diagnosis and prognosis: Current application and future perspective.

Lung cancer is one of the malignant tumors with the highest incidence and mortality in the world. The overall five-year survival rate of lung cancer i...

A radiomics-based deep learning approach to predict progression free-survival after tyrosine kinase inhibitor therapy in non-small cell lung cancer.

BACKGROUND: The epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are a first-line therapy for non-small cell lung cancer (NSC...

Deep learning for improving ZTE MRI images in free breathing.

INTRODUCTION: Despite a growing interest in lung MRI, its broader use in a clinical setting remains challenging. Several factors limit the image quali...

A comparison of total thoracoscopic versus robotic approach for cardiac myxoma resection: a single-center retrospective study.

Advances in instrumentation and technique have facilitated minimally invasive surgeries for cardiac myxoma treatment. This study aims to compare the c...