Pulmonology

Identification of a Biomarker Panel in Extracellular Vesicles Derived From Non-Small Cell Lung Cancer (NSCLC) Through Proteomic Analysis and Machine Learning.

Antigen fingerprint profiling of tumour-derived extracellular vesicles (TDEVs) in the body fluids is a promising strategy for identifying tumour biomarkers. In this study, proteomic and immunological assays reveal significantly higher CD155 levels in plasma extracellular vesicles (EVs) from patients with non-small cell lung cancer (NSCLC) than from healthy individuals. Utilizing CD155 as a bait pr...

Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation.

BACKGROUND: Lung cancer is one of the most common types of cancer and the leading cause of cancer death in the United Kingdom. Artificial intelligence-based software has been developed to reduce the number of missed or misdiagnosed lung nodules on computed tomography images.

Identifying GAP43, NMU, and TEX29 as Potential Prognostic Biomarkers for COPD Combined With Lung Cancer Patients Using Machine Learning.

Chronic obstructive pulmonary disease (COPD) and lung cancer, frequently comorbid conditions intricately linked through smoking, represent significant...

Image-Based Deep Learning Model for Predicting Lymph Node Metastasis in Lung Adenocarcinoma With CT ≤ 2 cm.

BACKGROUND: Lymph node metastasis (LNM) poses a considerable threat to survival in lung adenocarcinoma. Currently, minor resection is the recommended ...

FEBS-IUBMB-ENABLE Conference crosses seven seas: Report on the 3 International Molecular Biosciences PhD and Postdoc Conference-"Artificial intelligence: reshaping biomedical and healthcare research".

The 3 FEBS-IUBMB-ENABLE International Molecular Biosciences PhD and Postdoc Conference was held for the first time outside Europe, in South East Asia....

Successful Application of Artificial Intelligence-Assisted Analysis of Invasive Pulmonary Adenocarcinoma Less Than 6 mm in Size: A Case Report and Literature Review.

INTRODUCTION: Screening of lung nodules helps on early diagnosis of lung cancer, especially invasive pulmonary adenocarcinoma. Artificial intelligence...

Deep Learning for Predicting Acute Exacerbation and Mortality of Interstitial Lung Disease.

Some patients with interstitial lung disease (ILD) have a high mortality rate or experience acute exacerbation of ILD (AE-ILD) that results in increa...

The combination of physiology and machine learning for prediction of CPAP pressure and residual AHI in OSA.

STUDY OBJECTIVES: Continuous positive airway pressure (CPAP) is the treatment of choice for obstructive sleep apnea; however, some people have residua...

Deep Learning-based Aligned Strain from Cine Cardiac MRI for Detection of Fibrotic Myocardial Tissue in Patients with Duchenne Muscular Dystrophy.

Purpose To develop a deep learning (DL) model that derives aligned strain values from cine (noncontrast) cardiac MRI and evaluate performance of these...

Utilizing large language models for detecting hospital-acquired conditions: an empirical study on pulmonary embolism.

OBJECTIVES: Adverse event detection from Electronic Medical Records (EMRs) is challenging due to the low incidence of the event, variability in clinic...

Predicting Respiratory Disease Mortality Risk Using Open-Source AI on Chest Radiographs in an Asian Health Screening Population.

Purpose To assess the prognostic value of an open-source deep learning-based chest radiographs algorithm, CXR-Lung-Risk, for stratifying respiratory d...

Machine Learning Models for Predicting Pediatric Hospitalizations Due to Air Pollution and Humidity: A Retrospective Study.

BACKGROUND: Exposure to air pollution and meteorological conditions, such as humidity, has been linked to adverse respiratory health outcomes in child...

Association of Deep Learning-based Chest CT-derived Respiratory Parameters with Disease Progression in Amyotrophic Lateral Sclerosis.

Background Forced vital capacity (FVC) is a standard measure of respiratory function in patients with amyotrophic lateral sclerosis (ALS) but has limi...

Clinical and Social Characterization of Patients Hospitalized for COPD Exacerbation Using Machine Learning Tools.

OBJECTIVE: This study aims to employ machine learning (ML) tools to cluster patients hospitalized for acute exacerbations of chronic obstructive pulmo...

Utility of AI digital pathology as an aid for pathologists scoring fibrosis in MASH.

BACKGROUND & AIMS: Intra and inter-pathologist variability poses a significant challenge in metabolic dysfunction-associated steatohepatitis (MASH) bi...

Artificial Intelligence Assessment of Chest Radiographs for COVID-19.

BACKGROUND: The sensitivity of reverse-transcription polymerase chain reaction (RT-PCR) is limited for diagnosis of severe acute respiratory syndrome ...

Artificial intelligence-based echocardiography assessment to detect pulmonary hypertension.

BACKGROUND: Tricuspid regurgitation jet velocity (TRJV) on echocardiography is used for screening patients with suspected pulmonary hypertension (PH)....

Predicting outcomes after hospitalisation for COPD exacerbation using machine learning.

BACKGROUND: Early readmission and death are critical adverse outcomes following hospitalisation due to exacerbation of chronic obstructive pulmonary d...

Effects of chronic exposure to biomass pollutants on cardiorespiratory responses and the occurrence of exercise-induced bronchoconstriction in healthy men.

Exposure to charcoal biomass (CB) pollutants affects the cardiorespiratory system. We assessed cardiopulmonary responses (CPR) to exercise in charcoal...

Artificial intelligence-driven microsatellite instability profiling reveals distinctive genetic features in patients with lung cancer.

BACKGROUND: Microsatellite instability (MSI) has emerged as a predictive biomarker for immunotherapy response in various cancers, but its role in non-...