Pulmonology

Artificial intelligence in lung scintigraphy.

Lung scintigraphy is a common nuclear medicine procedure for evaluating pulmonary perfusion and ventilation, particularly in the diagnosis of pulmonary embolism (PE) and preoperative lung function assessment. With the advent of artificial intelligence (AI), this modality is undergo-ing a transformative evolution. This review explores the multifaceted integration of AI across the lung scintigraphy ...

Improved visualization of pancreas and tumor boundaries using high-frequency kernels with deep-learning image reconstruction at high-strength level.

PURPOSE: This study aimed to investigate the feasibility of combining high-frequency reconstruction kernels and deep-learning image reconstruction at high-strength level (DLIR-H) for improving visualization of the pancreas and tumor boundaries on pancreatic protocol CT. MATERIALS AND METHODS: This retrospective study included 30 patients (median age, 75 years; 16 women) who underwent pancreatic pr...

Novel Artificial Intelligence Chest X-ray Diagnostics: A Quality Assessment of Their Agreement with Human Doctors in Clinical Routine.

The rising demand for radiology services calls for innovative solutions to sustain diagnostic quality and efficiency. This study evaluated the diagnos...

Cost-effectiveness of lung cancer screening: insights from risk stratification, guidelines, and emerging technologies-a systematic review.

Lung cancer is the leading cause of cancer-related mortality worldwide, with most patients diagnosed at advanced stages. Early detection through scree...

Indoor air quality in primary schools: real-time monitoring and predictive modeling of PM10 in Kenitra, Morocco.

The health of children may be adversely influenced by the air quality in schools because they are more sensitive to indoor air pollutants. PM10, which...

Enhancing Explainability in AI-Based Interstitial Lung Disease Detection via Multi-Class Learning Based on Lesion Areas.

Interstitial lung disease (ILD) comprises a group of lung disorders characterized by inflammation and fibrosis of the lung interstitium. Early detecti...

Cell death-related gene signatures as dual-function biomarkers: Early diagnosis and therapeutic targeting in Staphylococcus aureus pneumonia.

BACKGROUND: Staphylococcus aureus (S. aureus) pneumonia constitutes a lethal respiratory infection with persistently high clinical mortality. Although...

Leveraging machine learning to predict de novo skin malignancy following lung transplantation.

AIMS: This study aimed to predict post-transplant malignancy risks at multiple levels among lung transplant recipients using machine learning (ML) and...

Early management of acute heart failure.

PURPOSE OF REVIEW: Acute heart failure (AHF) is a frequent, high-risk emergency department presentation in which early diagnostic and therapeutic deci...

Time-Series Machine Learning for Prediction of Bronchopulmonary Dysplasia.

OBJECTIVE: To build a time-series machine learning (ML) model that improves bronchopulmonary dysplasia (BPD) prediction compared with published online...

Evaluation of an Artificial Intelligence Defined Lung Nodule Malignancy Score in Incidental Pulmonary Nodules: The CREATE Study.

OBJECTIVE: To evaluate the effectiveness of the artificial intelligence-based qXR lung nodule malignancy score (qXR-LNMS) in detecting high-risk incid...

Machine Learning Integrated Extracellular Vesicle Proteome Analysis For Early Markers Of Bronchopulmonary Dysplasia.

Bronchopulmonary dysplasia (BPD) is a serious and often lethal complication of pre-term birth that typically manifests about one month after pre-term ...

Fibroblast-like cells accumulate late in human coronary atherosclerosis contributing to necrotic core formation.

AIMS: Proliferation of arterial smooth muscle cells (SMCs) and their modulation to alternative mesenchymal phenotypes is central to atherosclerotic le...

Attenuation-based ultra-low-dose lung computed tomography at 0.1 mSv to 0.3 mSv effective dose in children.

BACKGROUND: Radiation dose reduction is essential in paediatric lung computed tomography (CT). Advances in energy-integrating detector CT and deep-lea...

Morphometric analysis and its application in lupus nephritis: a systematic review.

Lupus nephritis (LN) is a severe manifestation of systemic lupus erythematosus (SLE), characterized by marked histological heterogeneity and high inte...

Artificial intelligence for early diagnosis in emergency department.

In recent years, artificial intelligence (AI) has become an increasingly prominent player in emergency medicine, offering innovative tools to enhance ...

Interpretable machine learning model for predicting in-hospital mortality in elderly acute pancreatitis: Development and validation in a multicenter cohort.

BACKGROUND: Elderly acute pancreatitis (AP) patients face significantly higher in-hospital all-cause mortality, highlighting the need for effective ri...

Clinical validation of a unified data-driven respiratory motion correction technique in 18F-FDG PET/CT imaging of upper abdominal lesions: a real-world study.

PURPOSE: Respiratory motion (RM)-related artifacts significantly impact image quality and diagnostic accuracy in PET/CT imaging. This study aimed to p...

From Clusters to Outcomes: Machine Learning-Based Phenotyping in Intermediate-High-Risk Acute Pulmonary Embolism.

Intermediate-high-risk (IHR) pulmonary embolism (PE) represents a heterogeneous group in whom guideline-based criteria may insufficiently capture biol...

Morphological characterization of a novel model of steatohepatitis in the FAH-/- pig.

Metabolic dysfunction-associated steatohepatitis (MASH) is a global health care burden. Appropriate large animal models mimicking the main MASH charac...