The European Respiratory Society congress in the year 2020, a year dominated by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, was the first virtual congress planned with an innovative and interactive congress programme upfront. It was a large, novel platform for scientific discussion and presentations of cutting-edge innovative developments. This article summarises a selection of the scientific highlights from the Clinical Techniques, Imaging and Endoscopy assembly (assembly 14). In addition to presentations on the important role of bronchoscopy, imaging and ultrasound techniques in the field of SARS-CoV-2 infection, novel diagnostic approaches and innovative therapeutic strategies in patients with lung cancer, interstitial lung disease, obstructive airway disorders and infectious diseases were discussed.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8165368PMC
http://dx.doi.org/10.1183/23120541.00118-2021DOI Listing

Publication Analysis

Top Keywords

highlights clinical
8
clinical techniques
8
techniques imaging
8
imaging endoscopy
8
endoscopy assembly
8
ers international
4
congress
4
international congress
4
congress 2020
4
2020 highlights
4

Similar Publications

Validating the Accuracy of Parkinson's Disease Clinical Diagnosis: A UK Brain Bank Case-Control Study.

Ann Neurol

January 2025

Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Rome, Italy.

Objective: Despite diagnostic criteria refinements, Parkinson's disease (PD) clinical diagnosis still suffers from a not satisfying accuracy, with the post-mortem examination as the gold standard for diagnosis. Seminal clinicopathological series highlighted that a relevant number of patients alive-diagnosed with idiopathic PD have an alternative post-mortem diagnosis. We evaluated the diagnostic accuracy of PD comparing the in-vivo clinical diagnosis with the post-mortem diagnosis performed through the pathological examination in 2 groups.

View Article and Find Full Text PDF

Background: Kidney tumors, common in the urinary system, have widely varying survival rates post-surgery. Current prognostic methods rely on invasive biopsies, highlighting the need for non-invasive, accurate prediction models to assist in clinical decision-making.

Purpose: This study aimed to construct a K-means clustering algorithm enhanced by Transformer-based feature transformation to predict the overall survival rate of patients after kidney tumor resection and provide an interpretability analysis of the model to assist in clinical decision-making.

View Article and Find Full Text PDF

: Major Depressive Disorder (MDD) is a prevalent and debilitating mental disorder that has been linked to hyperhomocysteinemia and folate deficiency. These conditions are influenced by the methylenetetrahydrofolate reductase () gene, which plays a crucial role in converting homocysteine to methionine and is essential for folate metabolism and neurotransmitter synthesis, including serotonin. : This study explored the association between and polymorphisms among Saudi MDD patients attending the Erada Complex for Mental Health and Erada Services outpatient clinic in Jeddah, Saudi Arabia.

View Article and Find Full Text PDF

Magnetic resonance imaging (MRI) is frequently used to monitor disease progression in multiple sclerosis (MS). This study aims to systematically evaluate the correlation between MRI measures and histopathological changes, including demyelination, axonal loss, and gliosis, in the central nervous system of MS patients. We systematically reviewed post-mortem histological studies evaluating myelin density, axonal loss, and gliosis using quantitative imaging in MS.

View Article and Find Full Text PDF

Systematic Review of Hybrid Vision Transformer Architectures for Radiological Image Analysis.

J Imaging Inform Med

January 2025

School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.

Vision transformer (ViT)and convolutional neural networks (CNNs) each possess distinct strengths in medical imaging: ViT excels in capturing long-range dependencies through self-attention, while CNNs are adept at extracting local features via spatial convolution filters. While ViT may struggle with capturing detailed local spatial information, critical for tasks like anomaly detection in medical imaging, shallow CNNs often fail to effectively abstract global context. This study aims to explore and evaluate hybrid architectures that integrate ViT and CNN to leverage their complementary strengths for enhanced performance in medical vision tasks, such as segmentation, classification, reconstruction, and prediction.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!