Primary Care

Quantitative Histological Insights Into Sudden Arrhythmic Death Syndrome: Findings From a Forensic Autopsy Cohort.

Sudden arrhythmic death syndrome (SADS) is a major cause of sudden cardiac death in young individuals, characterized by structurally normal hearts and negative toxicology. Although guidelines recommend family screening, phenotyping remains challenging. This study applied quantitative histology and deep-learning-based cell segmentation to investigate morphological features in SADS compared to contr...

Human and artificial intelligence performance in radiographic caries detection: ex vivo tooth section-referenced evaluation and implications for clinical decision-making.

OBJECTIVES: To evaluate the caries detection performance of a commercially available AI-system (Nostic) and human raters for radiographic caries detection using tooth sections as reference. METHODS: Radiographs and corresponding tooth sections from extracted teeth (548 approximal and 91 cervical sites) were assessed by three dentists and AI-system (graded/probabilistic outputs). Performance versus...

Fetal and neonatal alloimmune thrombocytopenia (FNAIT): From reactive transfusion to proactive prevention.

Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a major cause of severe thrombocytopenia, intracranial hemorrhage, and long-term neurologica...

Gut Microbiota and Metabolic Health: From Dysbiosis to Therapeutics.

The gut microbiota (GM) is a pivotal regulator of host metabolism and a contributor to the pathophysiology of obesity, type 2 diabetes (T2D), and meta...

Evaluating Accuracy of ChatGPT-4o in Automated Carbohydrate Estimation From Images as a Self-Management Tool for Adolescents With Type 1 Diabetes.

INTRODUCTION: Type 1 diabetes mellitus (T1D) requires precise carbohydrate estimation to manage blood glucose and prevent chronic and acute complicati...

Puerarin mitigates tumor necrosis factor-nuclear factor-κB axis-driven macrophage inflammation and atherogenesis: Integrative network pharmacology and experimental validation.

BACKGROUND: Atherosclerosis (AS) is a chronic inflammatory disease that constitutes the primary pathological basis of cardiovascular disorders. Althou...

Artificial intelligence to investigate metabolomics data for precision medicine.

BACKGROUND: Metabolomic data offers insights into disease mechanisms, diagnostics, and therapeutic targets by analyzing metabolic profiles. In analyzi...

Automated detection of Chagas disease from ECG signals using wavelet scattering transform and RUSBoost classifier.

Early diagnosis of Chagas disease plays a vital role in enabling timely treatment and reducing the likelihood of underlying severe cardiovascular comp...

Impact of quality routine health data utilisation on health service delivery outcomes in low-income and middle-income countries: a systematic review protocol.

BACKGROUND: Evidence-based decision-making in healthcare relies heavily on routine health information. However, in many low-income and middle-income c...

A streamlined CMR-derived machine-learning model for estimating cardiovascular biological age: development and validation in the UK-biobank and multi-ethnic study of atherosclerosis.

AIMS: Current models predicting cardiovascular biological age rely on radiomics or complex large feature sets including T1 and strain. We developed an...

A Preliminary Report: Screening Idiopathic Scoliosis (IS) With a Smartphone Application.

INTRODUCTION: Idiopathic scoliosis (IS) is a 3-dimensional spinal deformity that affects ∼3.1% of the population. Current strategies for IS screening ...

Integrated machine learning risk model for predicting radiation pneumonitis in lung cancer patients with interstitial lung disease.

BACKGROUND: Radiation pneumonitis (RP) is a serious complication in lung cancer patients with pre-existing interstitial lung disease (ILD) undergoing ...

Elucidating the mechanisms of aristolochic acid-induced upper tract urothelial carcinoma: A multi-omics approach combining bioinformatics and computational modeling.

Aristolochic acids (AAs) are established human carcinogens strongly associated with upper tract urothelial carcinoma (UTUC). However, the multi-target...

Machine learning models to detect opioid misuse in emergency department patients at triage.

OBJECTIVE: Emergency department (ED) encounters represent valuable opportunities to initiate evidence-based treatments for patients with opioid misuse...

Effectiveness of natural language intelligence technology in chronic diseases nursing: A systematic review and meta-analysis.

BACKGROUND: Chronic diseases pose a heavy global burden, with challenges in utilizing unstructured data for continuous care. Natural language intellig...

Deep Learning Model to Detect Diabetic Retinopathy in 45° Images Using Ground Truth from Ultra-Widefield Imaging.

PURPOSE: To evaluate the performance of a deep learning (DL) model in classifying diabetic retinopathy (DR) severity using fundus images with varying ...

Opportunistic Screening with Imaging: Actionable Insights from Unused Data.

The growing volume of clinical imaging and the emergence of artificial intelligence technologies present a unique opportunity to extract additional va...

Predicting Nocturnal Hypertension in CKD Through Table-Value Diffusion Model.

BACKGROUND: Ambulatory blood pressure monitoring is indispensable for diagnosing nocturnal hypertension (NH) among patients with chronic kidney diseas...

Protocol for a pragmatic trial comparing navigator and automated virtual interventions for guaranteeing adherence to fecal immunochemical testing (NAVIGATE-FIT).

BACKGROUND: Fecal immunochemical testing (FIT) - a non-invasive colorectal cancer (CRC) screening method offers an opportunity to bridge CRC screening...

Predicting the Course of High-Impact Chronic Pain Using Machine Learning Algorithms.

High-impact chronic pain (HICP) affects over 17 million U.S. adults and follows highly variable courses. To date, the relative importance of biopsycho...