Surveillance

Getting ahead of sudden cardiac arrest in pediatric hypertrophic cardiomyopathy patients.

INTRODUCTION: Sudden cardiac arrest (SCA) remains one of the most devastating complications of pediatric hypertrophic cardiomyopathy (HCM). Despite major advances in genetic diagnostics, cardiac imaging, and risk stratification strategies, the prevention of sudden cardiac death (SCD) in children and adolescents with HCM remains an unresolved clinical challenge. Contemporary guideline-based approac...

The Rise in Artificial Intelligence and Machine Learning Models to Screen for Cleft-Related Velopharyngeal Dysfunction: A Systematic Review.

ObjectiveTo systematically review literature on the use of artificial intelligence (AI) and machine learning (ML) models for detecting velopharyngeal dysfunction (VPD) in patients with cleft palate.DesignSystematic review conducted in accordance with PRISMA guidelines (PROSPERO CRD420251034524).SettingStudies published were identified through EMBASE, ProQuest, Google Scholar, and PubMed.Participan...

Modelling approaches for predicting the distribution of skin NTDs: A systematic review.

BACKGROUND: Skin neglected tropical diseases (NTDs) such as cutaneous leishmaniasis, lymphatic filariasis, mycetoma, and podoconiosis affect millions ...

Methods for estimating the burden of acute tropical infectious diseases: A scoping review.

Acute infectious diseases, particularly lots of neglected tropical diseases (NTDs), pose significant public health challenges, especially in resource-...

Understanding how language barriers in the paediatric emergency care setting influences safety of care delivery: a scoping review.

BACKGROUND: Communication in a family's primary language can support safe care. Vital steps within the care delivery process are contingent on success...

AI-based prediction of dengue incidence using climatic, environmental, and socio-demographic factors: an ensemble random forest approach with agile system development.

BACKGROUND: Dengue transmission in Indonesia is shaped by interacting climatic, environmental, and socio-demographic factors, yet most forecasting sys...

Artificial intelligence predictions of knee kinematics, kinetics, and internal biomechanics during walking in people with knee osteoarthritis: A systematic review and meta-analysis.

This review synthesizes current literature on the use of artificial intelligence (AI) to predict knee biomechanics during walking in people with knee ...

Contemporary heart failure evidence in 2025: a joint summary of key trials from the European Journal of Heart Failure and ESC Heart Failure Journal.

Heart failure (HF) remains a major global health challenge, characterized by high morbidity, mortality, and healthcare costs despite substantial advan...

A systematic review of machine learning approaches to bovine tuberculosis in cattle.

Bovine tuberculosis (bTB) is a chronic zoonotic disease, caused by Mycobacterium bovis which despite years of eradication attempts, is still prevalent...

AI-Enabled Imaging for Pathogen Detection Under Stress Conditions: A Systematic Review.

Advances in pathogen detection that incorporate artificial intelligence (AI) may capture microbial signals under challenging environmental conditions ...

Research priorities for improved pandemic and epidemic intelligence.

Pandemic and epidemic intelligence integrates surveillance data with contextual knowledge to assess health risks and inform public health decisions. A...

DeepTYLCV: An interpretable and experimentally validated AI model for predicting virulence of different tomato yellow leaf curl virus strains.

Tomato yellow leaf curl virus (TYLCV) is among the most devastating pathogens affecting tomato production worldwide, with emerging virulent strains in...

AI for Hospital Infection Prevention and Control: Real-World Implementation, Impact, and the Gap Beyond Model Development.

BACKGROUND: Artificial intelligence and machine learning (AI/ML) may strengthen hospital infection prevention and control (IPC) through automated surv...

Evaluation of AI-enhanced FTIR spectroscopy for species and biovar typing of intracellular pathogenic Brucella spp.

BACKGROUND: Rapid and accurate identification of intracellular pathogenic Brucella species and biovars is essential for effective public health survei...

Using Generative Artificial Intelligence to Identify Central Line-associated Bloodstream Infections.

BACKGROUND: Central line-associated blood stream infection (CLABSI) surveillance is mandated and publicly reported in United States hospitals but requ...

A review of machine learning in toxicology: current practices and reporting gaps.

In recent years, machine learning and artificial intelligence approaches have been increasingly applied in the context of toxicological risk assessmen...

Predicting early response to ablative radiotherapy in oligometastatic disease: a scoping review of radiomics-based machine learning and deep learning models.

OBJECTIVES: Oligometastatic disease represents an intermediate stage of cancer, often treated with surgery or ablative radiotherapy (ART). This scopin...

A quantitative analysis of global AI medical studies: gaps in randomized controlled trials.

Medical artificial intelligence (AI) has advanced rapidly, yet a comprehensive quantitative overview of its clinical evaluation landscape remains lack...

Predicting survival of Hodgkin lymphoma using machine learning-an analysis based on the SEER database.

OBJECTIVES: This study aimed to develop an effective model for predicting Hodgkin lymphoma (HL) prognosis as to assist clinicians in making optimal cl...

Machine and deep learning in REM sleep behavior disorder: a scoping review and analysis of reporting quality.

Rapid eye movement (REM) sleep behavior disorder (RBD) is a parasomnia, and its isolated form is of particular interest, as it is an early phase alpha...