AIMC Topic: Cardiovascular Diseases

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Cardiovascular Care in Pediatric Cancer Survivors: Updates on Risk, Prevention, and Therapies.

Current treatment options in oncology
Improved survival in pediatric oncology has highlighted the growing burden of cancer treatment-related cardiotoxicity among survivors of childhood cancers. While the cardiotoxicity of anthracyclines and chest radiation are well documented as major co...

Mapping the evidence on mHealth interventions for cardiovascular event care in Africa: a scoping review.

Heart (British Cardiac Society)
BACKGROUND: The burden of cardiovascular events in Africa is projected to rise significantly in the coming decade, placing additional strain on already overburdened healthcare systems. Mobile health (mHealth) technologies present a promising approach...

Omics approaches to understand cardiovascular disease.

BMC cardiovascular disorders
Omics approaches have emerged as indispensable tools in unravelling the intricate molecular landscape of cardiovascular disease (CVD) by providing comprehensive insights into the underlying mechanisms driving CVD pathogenesis, progression, and respon...

ECG-based deep learning for chronic kidney disease detection and cardiovascular risk prediction.

BMC medical informatics and decision making
BACKGROUND: Chronic kidney disease (CKD) is a global health burden with low awareness among both patients and healthcare providers. Deep learning models (DLMs) have shown promise in interpreting electrocardiograms (ECGs) for various disease and may o...

The History of Appropriate Use Criteria in Cardiovascular Diagnostic Imaging: Bridging the Past, Present, and Future.

Journal of nuclear medicine technology
Cardiovascular diagnostic imaging plays a crucial role in modern health care, supporting accurate diagnosis, risk stratification, and the management of cardiovascular diseases. However, ensuring that imaging is used appropriately has become a key foc...

Context matters in machine learning based disease prediction with insights from diverse clinical and symptom data.

Scientific reports
Machine learning (ML) has the potential to drastically improve clinical decision-making by predicting diseases early, accurately, and based on data. This study evaluated and compared the performance of several machine learning models, including a fee...

Determining the Feasibility and Usability of a Co-Designed Culturally Appropriate Conversational Agent (DESI-Heart) to Support Self-Care in People With Cardiovascular Diseases: Protocol for a Single-Arm Pilot Trial.

JMIR research protocols
BACKGROUND: Cardiovascular diseases (CVDs) are a leading cause of death and disability worldwide. For people living with CVD, clinical guidelines recommend ongoing self-care such as symptom monitoring, medication adherence, and lifestyle modification...

Current State of Artificial Intelligence in Assessing Cardiac Function.

Current cardiology reports
PURPOSE OF REVIEW: Accurate, timely quantification of cardiac function is central to the diagnosis, management, and monitoring of cardiovascular disease. This review synthesizes recent advances in artificial intelligence (AI) applications across the ...

Data augmentation alters feature importance in XGBoost for CVD prediction.

Scientific reports
Machine learning models are powerful tools for cardiovascular disease (CVD) prediction, but their performance is often limited by dataset size and class imbalance. While data augmentation techniques can address these issues, their impact on model int...