Pulmonology

[Acute respiratory distress syndrome-quo vadis : Innovative and individualized treatment approaches].

Acute respiratory distress syndrome (ARDS) is a heterogeneous clinical syndrome characterized by variable pathophysiology and different therapeutic approaches. Recent guidelines emphasize the importance of prone positioning and venovenous extracorporeal membrane oxygenation (VV-ECMO) for the most severe cases, while routine recruitment maneuvers and extracorporeal CO-removal techniques are no long...

Bridging surgical oncology and personalized medicine: the role of artificial intelligence and machine learning in thoracic surgery.

Lung cancer remains the leading cause of cancer-related deaths globally, often detected in advanced stages with poor prognosis. While surgical resection is the mainstay of curative treatment, early detection remains a significant challenge. Advances in personalized medicine, including genomic profiling and low-dose CT scans, have led to more tailored therapies, offering improved outcomes. Integrat...

Advancing lung transplantation through machine learning and artificial intelligence.

PURPOSE OF REVIEW: To explore the current applications of artificial intelligence and machine learning in lung transplantation, including outcome pred...

A comparative study on TB incidence and HIVTB coinfection using machine learning models on WHO global TB dataset.

Tuberculosis, a deadly and contagious disease caused by Mycobacterium tuberculosis, remains a significant global public health threat. HIV co-infectio...

A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study.

BACKGROUND: Acute respiratory distress syndrome (ARDS) is a severe complication associated with a high mortality rate in patients with sepsis. Early i...

A machine learning model for predicting severe mycoplasma pneumoniae pneumonia in school-aged children.

OBJECTIVE: To develop an interpretable machine learning (ML) model for predicting severe Mycoplasma pneumoniae pneumonia (SMPP) in order to provide re...

Bioinformatics Approach to Identifying Molecular Targets of Isoliquiritigenin Affecting Chronic Obstructive Pulmonary Disease: A Machine Learning Pharmacology Study.

To identify the molecular targets and possible mechanisms of isoliquiritigenin (ISO) in affecting chronic obstructive pulmonary disease (COPD) by regu...

Estimating oxygen uptake in simulated team sports using machine learning models and wearable sensor data: A pilot study.

Accurate assessment of training status in team sports is crucial for optimising performance and reducing injury risk. This pilot study investigates th...

CAD-Unet: A capsule network-enhanced Unet architecture for accurate segmentation of COVID-19 lung infections from CT images.

Since the outbreak of the COVID-19 pandemic in 2019, medical imaging has emerged as a primary modality for diagnosing COVID-19 pneumonia. In clinical ...

Construction of a deep learning model and identification of the pivotal characteristics of FGF7- and MGST1- positive fibroblasts in heart failure post-myocardial infarction.

Dysregulation of fibroblast function is closely associated with the occurrence of heart failure after myocardial infarction (post-MI HF). Myocardial f...

Advancing harmful algal bloom predictions using chlorophyll-a as an Indicator: Combining deep learning and EnKF data assimilation method.

The use of data driven deep learning models to predict and monitor Harmful Algal Blooms (HABs) has evolved over the years due to increasing technologi...

Deep learning unlocks the true potential of organ donation after circulatory death with accurate prediction of time-to-death.

Increasing the number of organ donations after circulatory death (DCD) has been identified as one of the most important ways of addressing the ongoing...

Explainable machine learning for predicting lung metastasis of colorectal cancer.

Patients with lung metastasis of colorectal cancer typically have a poor prognosis. Therefore, establishing an effective screening and diagnosis model...

Utilizing artificial intelligence to predict and analyze socioeconomic, environmental, and healthcare factors driving tuberculosis globally.

Tuberculosis (TB) is a major global health issue, contributing significantly to mortality and morbidity rates worldwide. Socioeconomic, environmental,...

Development and external validation of a machine learning model to predict bronchopulmonary dysplasia using dynamic factors.

We hypothesized that incorporating postnatal dynamic factors would enhance the prediction accuracy of bronchopulmonary dysplasia in preterm infants. T...

Diagnostic MicroRNA Signatures to Support Classification of Pulmonary Hypertension.

BACKGROUND: Patients with pulmonary hypertension (PH) are classified based on disease pathogenesis and hemodynamic drivers. Classification informs tre...

Updated perspectives on visceral pleural invasion in non-small cell lung cancer: A propensity score-matched analysis of the SEER database.

BACKGROUND: Visceral pleural invasion (VPI), including PL1 (the tumor invades beyond the elastic layer) and PL2 (the tumor extends to the surface of t...

Artificial Intelligence Models for Pediatric Lung Sound Analysis: Systematic Review and Meta-Analysis.

BACKGROUND: Pediatric respiratory diseases, including asthma and pneumonia, are major causes of morbidity and mortality in children. Auscultation of l...

Predicting mortality and risk factors of sepsis related ARDS using machine learning models.

Sepsis related acute respiratory distress syndrome (ARDS) is a common and serious disease in clinic. Accurate prediction of in-hospital mortality of p...

A machine learning-based severity stratification tool for high altitude pulmonary edema.

This study aimed to identify key predictors for the severity of High Altitude Pulmonary Edema (HAPE) to assist clinicians in promptly recognizing seve...