Smoking & Tobacco

A Machine Learning-Based Clinical Tool for Predicting Inadequate Bowel Preparation: Development and Validation.

OBJECTIVES: Colonoscopy is an essential tool for diagnosing and treating colorectal diseases, with bowel preparation serving as a key determinant of its quality. This study aimed to develop and validate a machine learning model utilizing non-pharmacological parameters to predict the risk of inadequate bowel preparation and develop a clinical risk assessment tool. METHODS: Clinical data were prospe...

Explainable artificial intelligence for early Alzheimer's diagnosis using enhanced grey relational features and multimodal data.

Alzheimer's disease, a progressive neurodegenerative disorder, presents a growing global health challenge due to its increasing prevalence and lack of accessible early diagnostic methods. Even though it has enhanced the diagnostic accuracy of machine learning, there is a major concern about striking a balance between predictive performance and interpretability. The proposed study presents an inter...

Value of Endobronchial Ultrasound-Derived Radiomics in Differentiating Pulmonary Sarcoidosis from Mediastinal Lymph Node Tuberculosis.

INTRODUCTION: To explore the feasibility of an ultrasound radiomics machine learning model based on endobronchial ultrasound (EBUS) for differentiatin...

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 alt...

Development of death-risk score based on epidemiology of six mental disorders and application to mortality reduction via modifiable health behaviors.

OBJECTIVE: Prediction models for mortality rarely incorporate diverse psychiatric conditions, alongside clinical, behavioral, and demographic risk fac...

A Smartphone-based Comprehensive Dataset of Annotated Oral Cavity Images for Enhanced Oral Disease Diagnosis.

This study introduces a SMARTphone-based, expert annotated dataset of Oral Mucosa images (SMART-OM), collected to facilitate the development of Artifi...

Clustering of cardiovascular risk factors and their association with socio-demographic and lifestyle factors in middle-aged and older adults in rural northeast South Africa: Findings from HAALSI.

OBJECTIVES: This study examined patterns of clustering of intermediate risk factors for Cardiovascular diseases (CVDs) in a rural South African popula...

Real-time automated billing for tobacco treatment: performance evaluation of the CigStopper machine learning framework.

OBJECTIVE: To evaluate CigStopper, a machine learning algorithm designed to predict billing eligibility for tobacco cessation counseling (CPT 99406/99...

Detection of Lung and Colon Cancer Using XRx-Net With Histopathological Images.

Lung and Colon Cancer (LCC) is a prevalent type of cancer influenced by factors such as genetics, smoking, second-hand smoke exposure, and alcohol con...

Definitions of early COPD and predictors for disease progression: a systematic review.

INTRODUCTION: Early chronic obstructive pulmonary disease (COPD) is considered to represent the initial phase of the disease. However, inconsistent te...

GOLD 2026: Transforming COPD Management with Early Intervention, Multi-dimensional Assessment, and Personalized Care.

The 2026 report from the Global Initiative for Chronic Obstructive Lung Disease (GOLD) introduces substantial conceptual and practical updates to the ...

Interpretable machine learning based decision tree model for predicting obstructive airway disease in a large non-smoking health screening population.

Machine Learning Models for Individualized Osteoradionecrosis Risk Prediction in Head and Neck Cancer.

To develop and validate predictive models for osteoradionecrosis (ORN) after head and neck radiation therapy (RT) using time-to-event data with death ...

Race/ethnicity, disability, and antenatal depression in the United States: population-level insights from machine learning.

OBJECTIVE: Women with intersecting identities, such as being both Black and disabled, face heightened risk of antenatal depression, yet few studies ex...

Association between the oxidative balance score and depressive symptoms in adults with metabolic syndrome: a cross-sectional study from NHANES.

OBJECTIVE: This study investigated the association between the Oxidative Balance Score (OBS) and the odds of depressive symptoms in adults with metabo...

Assessing the clinical competence of large language models for tobacco use disorder: A multi-domain expert evaluation.

BACKGROUND: Tobacco use disorder (TUD) remains the leading preventable cause of death globally, yet fewer than one-third of users receive guideline-co...

Integrated analysis of therapeutic strategies and prognostic factors in advanced lung adenocarcinoma: Retrospective study with emphasis on gene assays, multimodality treatment approaches and predictive machine learning models.

Patients with advanced lung adenocarcinoma have a range of treatment options, including targeted therapy and gene assay-guided chemotherapy. The aim o...

Artificial intelligence-derived electrocardiographic age gap as a predictor of mortality after coronary revascularization: prognostic value and short-term intra-patient variability.

AIMS: The artificial intelligence (AI)-derived electrocardiographic (ECG) age gap-the difference between AI-predicted ECG age and chronological age-is...

Association between fundus sex index and hypertension, diabetes mellitus, and smoking in the Kumejima study.

Fundus parameters can be used to quantify masculinity or femininity as a fundus sex index (FSI) ranging from 0 to 1. The purpose of this study was to ...

Plasma proteomic profiles of lung volume-based phenotypes in Tobacco-Exposed Individuals Without Spirometric COPD.

BACKGROUND: Among individuals with a history of smoking but preserved spirometry (tobacco exposed with preserved spirometry, or TEPS), lung volume-bas...