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CardiacField: computational echocardiography for automated heart function estimation using two-dimensional echocardiography probes.
Eur Heart J Digit Health
January 2025
Department of Cardiovascular Surgery of Zhongshan Hospital, Fudan University, Shanghai 200032, China.
Aims: Accurate heart function estimation is vital for detecting and monitoring cardiovascular diseases. While two-dimensional echocardiography (2DE) is widely accessible and used, it requires specialized training, is prone to inter-observer variability, and lacks comprehensive three-dimensional (3D) information. We introduce CardiacField, a computational echocardiography system using a 2DE probe for precise, automated left ventricular (LV) and right ventricular (RV) ejection fraction (EF) estimations, which is especially easy to use for non-cardiovascular healthcare practitioners.
View Article and Find Full Text PDFUse of artificial intelligence to predict outcomes in mild aortic valve stenosis.
Eur Heart J Digit Health
January 2025
Department of Cardiovascular Medicine, Mayo Clinic, 200 First Street SW, Rochester, MN 55905, USA.
Aims: Aortic stenosis (AS) is a common and progressive disease, which, if left untreated, results in increased morbidity and mortality. Monitoring and follow-up care can be challenging due to significant variability in disease progression. This study aimed to develop machine learning models to predict the risks of disease progression and mortality in patients with mild AS.
View Article and Find Full Text PDFMultimodal data integration to predict atrial fibrillation.
Eur Heart J Digit Health
January 2025
Cardiovascular Division, Department of Medicine, University of Minnesota Medical School, 401 East River Parkway, Minneapolis, MN, USA.
Aims: Many studies have utilized data sources such as clinical variables, polygenic risk scores, electrocardiogram (ECG), and plasma proteins to predict the risk of atrial fibrillation (AF). However, few studies have integrated all four sources from a single study to comprehensively assess AF prediction.
Methods And Results: We included 8374 (Visit 3, 1993-95) and 3730 (Visit 5, 2011-13) participants from the Atherosclerosis Risk in Communities Study to predict incident AF and prevalent (but covert) AF.
Machine learning based prediction models for cardiovascular disease risk using electronic health records data: systematic review and meta-analysis.
Eur Heart J Digit Health
January 2025
School of Life Course & Population Sciences, King's College London, SE1 1UL London, UK.
Cardiovascular disease (CVD) remains a major cause of mortality in the UK, prompting the need for improved risk predictive models for primary prevention. Machine learning (ML) models utilizing electronic health records (EHRs) offer potential enhancements over traditional risk scores like QRISK3 and ASCVD. To systematically evaluate and compare the efficacy of ML models against conventional CVD risk prediction algorithms using EHR data for medium to long-term (5-10 years) CVD risk prediction.
View Article and Find Full Text PDFCardiac involvement in systemic sclerosis: A critical review of knowledge gaps and opportunities.
J Scleroderma Relat Disord
January 2025
Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Cardiovascular complications are observed in up to one-third of patients with systemic sclerosis (SSc). Early identification and management of SSc-associated primary cardiac disease is often challenging, given the complex disease pathophysiology, significant variability in clinical presentation, and scarce disease-modifying therapeutics. Here, we review the molecular mechanisms involved in SSc-associated cardiac disease pathogenesis, novel diagnostic tools and emerging therapies.
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