Primary Care

Clinical and economic impact of deep learning-enhanced opportunistic osteoporosis screening using chest radiographs with and without the osteoporosis self-assessment tool for Asians (OSTA).

UNLABELLED: Opportunistic analysis of chest radiographs with deep learning detects osteoporosis cost-effectively in women aged ≥ 50 years in Singapore. Adding OSTA marginally improves efficiency, but DL "deep learning" screening alone already reduces fractures and increases quality-adjusted life-years (QALYs) at acceptable cost. PURPOSE: Osteoporosis is underdiagnosed in older women due to limited...

Insights and Advances in Physical Activity Research on Youth with Type 1 Diabetes.

PURPOSE OF REVIEW: Most youth with type 1 diabetes (T1D) do not meet the guidelines for physical activity engagement, thereby diminishing potential benefits to physical and mental health. This review synthesizes the recent literature on physical activity among youth with T1D and offers recommendations for future research. RECENT FINDINGS: Studies highlight challenges related to the use of inconsis...

Quantitative assessment of neonatal health using dried blood spot metabolite profiles and deep learning.

Neonatal prematurity leads to considerable morbidity and mortality, partly because of acquired conditions such as bronchopulmonary dysplasia (BPD), in...

Thermostability Prediction Powered by Synergistic Deep Learning at Experimental and Theoretical Levels for Nanobodies.

Nanobodies have emerged as powerful biorecognition elements in various fields, while thermostability is a key factor in practical application. Experim...

Machine Learning Prediction of Pharmacogenetic Testing Uptake Among Opioid-Prescribed Patients Using Electronic Health Records: Retrospective Cohort Study.

BACKGROUND: Opioids are a widely prescribed class of medication for pain management. However, they have variable efficacy and adverse effects among pa...

Ligand-based machine learning models to classify active compounds for prostaglandin EP2 receptor.

Prostaglandin receptors are pharmacologically validated targets with implications in several medical indications, including glaucoma, cardiac cyanotic...

Patient perceptions and attitudes towards the use of artificial intelligence in the symptomatic breast unit.

OBJECTIVES: Artificial intelligence (AI) has been applied in a number of breast screening settings with favourable results. While there are a limited ...

An online interpretable machine learning model for predicting cardiometabolic multimorbidity risk in patients with type 2 diabetes mellitus.

Cardiometabolic multimorbidity (CMM), a major complication in type 2 diabetes mellitus (T2DM), increases mortality and healthcare burden. Early identi...

Associations between prenatal exposure to indoor air pollution and ambient particulate matter with infant growth.

Despite growing concern, the developmental effects of prenatal exposure to indoor and ambient particulate pollution remain poorly characterized. This ...

The role of artificial intelligence in obstetrics and gynecology: Innovations, challenges, and opportunities explored through a bibliometric analysis.

OBJECTIVE: Artificial intelligence (AI) applications have garnered increasing interest in obstetrics and gynecology. This study aims to analyze the ev...

Integrating Artificial Intelligence in Nursing Practice With Decubitus Risk Prediction Alerts: A Pilot Process Evaluation.

AIMS: To evaluate the acceptability and feasibility among nurses of Decubitus Risk Prediction Alerts based on Artificial Intelligence (DRAAI), and to ...

Lifespan Pancreas Morphology for Control Versus Type 2 Diabetes Using AI on Largescale Clinical Imaging.

Understanding how pancreas size and shape change with normal aging is critical for establishing a baseline to detect deviations in type 2 diabetes and...

First trimester prediction of gestational diabetes mellitus by machine learning in twin pregnancies.

INTRODUCTION: We aimed to develop a machine learning model for first-trimester prediction of gestational diabetes mellitus (GDM) in twin pregnancies u...

Artificial intelligence in lung scintigraphy.

Lung scintigraphy is a common nuclear medicine procedure for evaluating pulmonary perfusion and ventilation, particularly in the diagnosis of pulmonar...

Self-Supervised Speech Representations for Sleep Apnea Severity Prediction.

OBJECTIVE: Obstructive sleep apnea (OSA) is a prevalent yet underdiagnosed condition characterized by repetitive upper airway obstruction during sleep...

Retinal Coronary Artery Calcium Scoring - A Scalable Tool for Life Insurers.

BACKGROUND.—: Cardiovascular risk estimation for life insurance underwriting relies on risk estimation from conventional metrics: age, sex, smoking st...

Preoperative Neuroimaging Markers, Clinical Severity Measures, and Shunt Characteristics for Predicting Shunt Revision in Idiopathic Intracranial Hypertension: An Explainable Machine-Learning Study.

BACKGROUND AND PURPOSE: Surgical shunt placement is a common treatment for idiopathic intracranial hypertension (IIH) but is hampered by high revision...

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...

Head-to-head comparison of the ability of the cardiometabolic index and triglyceride-glucose index to predict 3-year major adverse cardiovascular events in patients with atrial fibrillation: insights from a community cohort.

BACKGROUND: Atrial fibrillation (AF) represents the most common sustained cardiac arrhythmia and confers an elevated risk of major adverse cardiovascu...

ShenLingBaiZhu powder ameliorates obesity and atherosclerosis by inhibiting inflammation and apoptosis through the suppression of the TLR4/NF-κB pathway.

ETHNOPHARMACOLOGICAL RELEVANCE: ShenLingBaiZhu Powder (SLBZP) is a renowned traditional Chinese medicinal formula that has been historically and clini...