Smoking & Tobacco

Multipath cycleGAN for harmonization of paired and unpaired low-dose lung computed tomography reconstruction kernels

Reconstruction kernels in computed tomography (CT) affect spatial resolution and noise characteristics, introducing systematic variability in quantitative imaging measurements such as emphysema quantification. Choosing an appropriate kernel is therefore essential for consistent quantitative analysis. We propose a multipath cycleGAN model for CT kernel harmonization, trained on a mixture of paire...

[Artificial intelligence-based automated assessment of coronary flow reserve from angiography and the impact of different vasodilators].

To explore the feasibility of a coronary angiography-based method developed with artificial intelligence which was able to automatically and quickly calculate coronary volumetric blood flow and coronary flow reserve (CFR), and explore the differences in CFR after injection of different vasodilators. This was a observational study screening patients with suspected coronary artery disease who unde...

InspectionV3: Enhancing Tobacco Quality Assessment with Deep Convolutional Neural Networks for Automated Workshop Management

The problems that tobacco workshops encounter include poor curing, inconsistencies in supplies, irregular scheduling, and a lack of oversight, all o...

AI for Tobacco Control: Identifying Tobacco-Promoting Social Media Content Using Large Language Models.

INTRODUCTION: Tobacco companies use social media to bypass marketing restrictions. Studies show that exposure to tobacco promotion on social media inf...

Machine learning in colorectal polyp surveillance: A paradigm shift in post-endoscopic mucosal resection follow-up.

Colorectal cancer remains a major health concern, with colorectal polyps as key precursors. Endoscopic mucosal resection (EMR) is a common treatment, ...

Smoking Status Normalization with Cross-Encoders and SNOMED CT.

Accurately documenting smoking status is essential for clinical decision-making and patient care. However, smoking status information is often only av...

Neural Networks and Chemical Messengers: Insights into Tobacco Addiction.

This study investigates changes in resting-state networks (RSNs) associated with tobacco addiction (TA) and whether these changes reflect alterations ...

Past, Present and Future Perspectives of Forensic Genetics.

Forensic genetics has experienced remarkable advancements over the past decades, evolving from the analysis of a limited number of DNA segments to com...

M-GNN: A Graph Neural Network Framework for Lung Cancer Detection Using Metabolomics and Heterogeneous Graph Modeling.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with early detection critical for improving survival rates, yet conventio...

Investigating the correlation between smoking and blood pressure via photoplethysmography.

Smoking has been widely identified for its detrimental effects on human health, particularly on the cardiovascular health. The prediction of these eff...

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...

Attention-enabled Explainable AI for Bladder Cancer Recurrence Prediction

Non-muscle-invasive bladder cancer (NMIBC) is a relentless challenge in oncology, with recurrence rates soaring as high as 70-80%. Each recurrence t...

M-TabNet: A Multi-Encoder Transformer Model for Predicting Neonatal Birth Weight from Multimodal Data

Birth weight (BW) is a key indicator of neonatal health, with low birth weight (LBW) linked to increased mortality and morbidity. Early prediction o...

Improving Diseases Predictions Utilizing External Bio-Banks

Machine learning has been successfully used in critical domains, such as medicine. However, extracting meaningful insights from biomedical data is o...

A multi-locus predictiveness curve and its summary assessment for genetic risk prediction

With the advance of high-throughput genotyping and sequencing technologies, it becomes feasible to comprehensive evaluate the role of massive geneti...

Fine-Tuning LLMs on Small Medical Datasets: Text Classification and Normalization Effectiveness on Cardiology reports and Discharge records

We investigate the effectiveness of fine-tuning large language models (LLMs) on small medical datasets for text classification and named entity reco...

AI-Enabled Knowledge Sharing for Enhanced Collaboration and Decision-Making in Non-Profit Healthcare Organizations: A Scoping Review Protocol

This protocol outlines a scoping review designed to systematically map the existing body of evidence on AI-enabled knowledge sharing in resource-lim...

A Protocol to Exposure Path Analysis for Multiple Stressors Associated with Cardiovascular Disease Risk: A Novel Approach Using NHANES Data

Background: Multiple medical and non-medical stressors, along with the complicity of their exposure pathways, have posted significant challenges to ...

Cathodal Transcranial Direct Current Stimulation Does Not Change Implicit Associations Against Alcohol in Alcohol Use Disorder: A Preregistered Clinical Trial.

Addictive behaviour is shaped by the dynamic interaction of implicit, bottom-up and explicit, top-down cognitive processes. In alcohol use disorder (A...

Towards Precision Oncology: Predicting Mortality and Relapse-Free Survival in Head and Neck Cancer Using Clinical Data

Head and neck squamous cell carcinoma (HNSCC) presents significant challenges in clinical oncology due to its heterogeneity and high mortality rates...