Primary Care

Artificial Intelligence in Tuberculosis Imaging: A Global Bibliometric Analysis of Research Trends and Collaborations.

INTRODUCTION: Tuberculosis (TB), a leading infectious cause of death, remains a global health challenge. Imaging is central to diagnosis and screening, while artificial intelligence (AI) is increasingly applied to chest X-rays (CXR) and computed tomography (CT). However, no bibliometric study has comprehensively mapped publication trends, collaborations, modalities, technological evolution, and em...

Start4All protocol for a Bayesian cost-effectiveness model of tuberculosis screening and diagnosis in seven high burden low-income and middle-income countries.

INTRODUCTION: High costs of screening and diagnostic tests remain a major barrier to timely tuberculosis (TB) identification in resource-limited settings. Evidence on the cost-effectiveness of scalable screening algorithms is limited. Start4All is a research project aimed at developing and evaluating algorithmic approaches to TB screening and diagnosis, with the goal of optimising technical and al...

Predicting cardiovascular events from routine mammograms using machine learning.

BACKGROUND: Cardiovascular risk is underassessed in women. Many women undergo screening mammography in midlife when the risk of cardiovascular disease...

Diagnosis of Cardiac Amyloidosis on Echocardiography Using Artificial Intelligence.

BACKGROUND: Diagnosing cardiac amyloidosis (CA) on echocardiography can be challenging due to the imaging overlap between CA and more prevalent causes...

Diagnostic model and identification of IGFBP2 as an aging-related biomarker and therapeutic target for systemic sclerosis-associated interstitial lung disease through integrated bioinformatics and machine learning approaches.

BACKGROUND: Cellular senescence is a critical contributor to the pathogenesis of systemic sclerosis-associated interstitial lung disease (SSc-ILD). Ho...

Explainable machine learning model based on clinical and radiological features for predicting hematoma expansion or rebleeding after decompressive craniectomy in traumatic brain injury: a bicentric cohort study.

BACKGROUND: Hematoma expansion or rebleeding after decompressive craniectomy (DC) is a critical determinant of poor prognosis in traumatic brain injur...

A Strengths, Weaknesses, Opportunities, and Threats Analysis of Generative Artificial Intelligence in Aphasia Management With Proposed Ethical Guidelines.

PURPOSE: Generative artificial intelligence (GenAI) is rapidly emerging as a transformative technology in aphasia management, necessitating judicious ...

Trust in AI-Supported Screening in General Practice Among Urban and Rural Citizens: Cross-Sectional Study.

BACKGROUND: The early detection of diseases is one of the tasks of general practice. Artificial intelligence (AI)-based technologies could be useful f...

Evaluating an AI-Enabled Mobile Mental Health Monitoring Tool Among Family Caregivers of Adults Living With Cancer: Single-Arm Feasibility and Acceptability Trial Protocol.

BACKGROUND: Psychological distress, particularly symptoms of depression and anxiety (D&A), is highly prevalent among family caregivers of individuals ...

Artificial intelligence for lung cancer: a systematic review of head‑to‑head CT, FDG PET/CT, and multimodal models across screening, staging, and prognosis.

BACKGROUND: Artificial intelligence (AI) has shown increasing potential in lung cancer imaging, particularly in detection, staging, prognosis, and rec...

Leveraging natural language processing and machine learning to identify chronic conditions from primary care electronic medical records.

Primary care electronic medical records (EMRs) contain rich data that can support proactive identification of chronic health conditions. However, leve...

Predicting risk of ascending thoracic aortic aneurysm in asymptomatic adults using machine learning.

Most patients with ascending thoracic aortic aneurysms (ATAA) remain asymptomatic until they develop fatal complications, including aortic dissection ...

From Observation to Prediction: Machine Learning Analysis of Progression of Visual loss in Nonarteritic Anterior Ischemic Optic Neuropathy.

OBJECTIVE: To determine whether combinations of modifiable clinical/systemic risk factors and structured trial variables predict early disease progres...

Efficacy of a social robot-assisted training for socio-cognitive and cognitive functions in Parkinson's disease: A comparison with an active control group.

This study evaluated the efficacy of 12-week of Socially Assistive Robot (SAR) training program on socio-cognitive and cognitive functions in 11 indiv...

Artificial intelligence 12-lead electrocardiography to determine atrial fibrillation risk among UK Biobank participants with predisposing conditions.

AIMS: Artificial intelligence electrocardiography (AI-ECG) algorithms are emerging tools for identifying individuals at risk of atrial fibrillation (A...

Criteria to Assess the Predictive and Clinical Utility of Novel Models, Biomarkers, and Tools for Risk of Cardiovascular Disease: A Scientific Statement From the American Heart Association.

Risk prediction has been used in the primary prevention of cardiovascular disease for >3 decades. Contemporary cardiovascular risk assessment relies o...

DNA Methylation Markers for Pregnancy Hypertension via Machine Learning Methods.

BACKGROUND: This study aims to develop a prediction model to identify individuals at risk of hypertensive disorders of pregnancy (HDPs), including ges...

Phenotypic clustering of cardiovascular risk profiles in systemic lupus erythematosus.

ObjectiveThis study aimed to identify distinct cardiovascular risk phenotypes in systemic lupus erythematosus (SLE) using an unsupervised cluster anal...

Clinical impact of adopting an AI-driven opportunistic bone health screening software using routine X-ray.

OBJECTIVES: To evaluate the clinical impact of an artificial intelligence device, Rho, that opportunistically screens X-rays for low bone mineral dens...

Early Prediction of Delirium in Postcardiac Surgery Patients: Machine Learning Model Development and External Validation.

BACKGROUND: Delirium is a frequent postoperative complication among patients who have undergone cardiac surgery and is associated with prolonged hospi...