Pulmonology

A comprehensive analysis of retrieving optically inactive indicators from multi-level remote sensing product(s) in Irish waters using data science techniques.

The present research was carried out to retrieve dissolved oxygen (DOX) using the Copernicus Marine Services products from the Irish transitional and coastal waters. To achieve the research goal, the study developed and validated 2101 machine learning (ML)/artificial intelligence (AI) (supervised learning, stacking-ensembles, equations, and voting-based ensembles) and statistical models using mult...

Plasma proteomic profiling with machine learning identifies immunometabolic perturbations associated with smoking and smoking cessation.

Cigarette smoking is the leading modifiable risk factor for chronic obstructive pulmonary disease (COPD) and is associated with systemic molecular alterations linking exposure to disease. We profiled the plasma proteome (2,920 proteins) of 38,133 UK Biobank participants and applied machine-learning approaches to characterize smoking-associated alterations and develop risk models. The circulating p...

Biosphere expansion drives Earth's secular oxygenation while tectonics modulate oxygen variability revealed by machine learning.

The rise of atmospheric oxygen fundamentally transformed Earth's surface environment and enabled the evolution of complex life. However, the processes...

Interpretable Multi-Modality Consensus QSAR Framework: Integrating Machine and Deep Learning for Enhanced Multi-Endpoint Toxicity Assessment.

Accurate toxicity assessment is essential for chemical safety, but experimental testing is costly, slow, and ethically constrained, motivating the ado...

Combining computed tomography radiomics and clinical features to predict lymph node metastasis in patients with lung cancer.

BACKGROUND: Accurate preoperative assessment of lymph node metastasis (LNM) is crucial for treatment planning and prognostic stratification in patient...

Artificial Intelligence in Predicting Surgical Problems and Postoperative Morbidity in Mandibular Third Molar Extractions.

INTRODUCTION: Mandibular third molar (MTM) extractions are among the most frequent oral surgical procedures, often associated with variable surgical d...

Deep learning-based identification of chronic pulmonary embolism on CTPA: a regional lung analysis using multiplanar MIP images.

OBJECTIVE: Chronic pulmonary embolism (CPE) and chronic thromboembolic pulmonary hypertension (CTEPH) are challenging to diagnose, with delayed detect...

CT acquisition protocols in lung cancer screening: implications for guideline development from a worldwide survey.

OBJECTIVES: To assess the currently applied CT image acquisition protocols in lung cancer screening (LCS) and thereby fill a knowledge gap to support ...

Current and future contributions of AI to pulmonary function test interpretation, diagnostic approaches, and predictions of disease progression.

INTRODUCTION: Artificial intelligence (AI) methods - including machine learning, deep learning, and explainable AI - are increasingly applied to pulmo...

On Demographic Group Fairness Guarantees in Deep Learning.

We present a comprehensive theoretical framework analyzing the relationship between data distributions and fairness guarantees in equitable deep learn...

Assessing interstitial fibrosis and tubular atrophy in kidney biopsies artificial intelligence versus humans.

PURPOSE OF REVIEW: The degree to which computerized methods, such as artificial intelligence (AI), will aid in the assessment of kidney histopathology...

Med-ViX-Ray: Enhancing explainable chest X-ray analysis with clinical knowledge graphs.

BACKGROUND AND OBJECTIVE: Deep learning has achieved remarkable success in chest x-ray interpretation, yet most models remain black boxes, producing a...

Explainable machine learning for postoperative respiratory failure prediction in open-heart surgery patients - a study based on the MIMIC-IV database.

BACKGROUND: Postoperative respiratory failure (PRF) is a severe complication after open-heart surgery, associated with increased mortality and prolong...

Integrative Approaches in Lung Cancer Diagnosis: Bridging Molecular Biomarkers and AI Driven Imaging.

Though critical, traditional diagnostic approaches such as X-ray, CT scans, bronchoscopy and tissue biopsy don't reliably detect lung cancer at early ...

Bone tuberculosis: molecular pathogenesis, diagnostic advances, therapeutic challenges, and emerging strategies for effective management.

A review of artificial intelligence-based research on chronic obstructive pulmonary disease.

In recent years, with the rapid development of artificial intelligence (AI), Chronic Obstructive Pulmonary Disease (COPD), one of the world's three ma...

Machine learning-based proteogenomic data modeling identifies circulating plasma biomarkers for early detection of lung cancer.

BACKGROUND: Genetic aberrations are among the critical driving factors of lung cancer. Importantly, the impact of genetic variations on proteomic dysr...

Prediction of favorable outcomes of acute basilar artery occlusion using machine learning.

BACKGROUND: This study aims to develop an interpretable machine learning model using SHapley Additive exPlanations (SHAP) to predict favorable outcome...

Assessing the influence of kernel selection on chest computed tomography image quality across varying dose levels using TrueFidelity reconstruction.

Deep learning image reconstruction (DLIR) utilizes neural networks to generate high-quality computed tomography (CT) images. One commercially availabl...

Improving visibility of small anatomical details on low and ultra-low dose computed tomography with artificial intelligence-based image reconstructions.

To assess how computed tomography (CT) image reconstruction techniques affect perceived diagnostic image quality at varying radiation dose levels in c...