1.Automating Quality Control in Cardiac MRI: AI for Discriminative Assessment of Planning and Movement Artefacts and Real-Time Reacquisition Guidance

DOI: 10.1016/j.jocmr.2024.101067

https://www.sciencedirect.com/science/article/pii/S1097664724010949

Accurate measurement of cardiac MRI images requires precise localization of the scanning plane and the elimination of motion artifacts caused by arrhythmias or respiration. Unrecognized or improperly managed artifacts can degrade image quality, render clinical measurements invalid, and reduce diagnostic confidence. Currently, radiographers must manually inspect each acquired image to confirm diagnostic quality and decide whether re-acquisition or sequence changes are necessary. This study develops an AI system to perform continuous quality assessment across 1940 videos, demonstrating that AI can evaluate cardiac MRI videos and provide scores closely aligned with expert consensus.

2.Increased cardiovascular disease risk among adolescents and young adults with gastric cancer

DOI: 10.1007/s10120-024-01540-3

https://link.springer.com/article/10.1007/s10120-024-01540-3

Gastric cancer is a major global health issue, yet research on gastric cancer in adolescents and young adults is scant. Previous studies have found that cardiovascular risk factors in gastric cancer patients decrease after gastrectomy. However, there is a lack of data on cardiovascular incidence in adolescents and young adults. This article identifies 3,243,041 adolescent and young adult patients from a database, revealing that survivors of gastric cancer in this age group have an increased risk of developing cardiovascular disease (CVD).

3.A Machine Learning Model for Predicting In-Hospital Mortality in Chinese Patients With ST-Segment Elevation Myocardial Infarction: Findings From the China Myocardial Infarction Registry

DOI: 10.2196/50067

https://www.jmir.org/2024/1/e50067

Acute myocardial infarction (AMI) is a leading cause of hospitalization and death in China, with ST-segment elevation myocardial infarction (STEMI) accounting for over 80% of AMI cases. Most existing risk prediction models rely on generalized linear regression methods, which often lack predictive accuracy. This study leverages machine learning techniques to predict AMI, demonstrating that ML-based risk prediction models have high accuracy in forecasting in-hospital mortality. Their flexibility and interpretability make these models more practical for clinical use, offering better guidance for patient management.

4.Status and timing of angiotensin receptor-neprilysin inhibitor implementation in patients with heart failure and reduced ejection fraction: Data from the Swedish Heart Failure Registry

DOI: 10.1002/ejhf.3404

https://onlinelibrary.wiley.com/doi/10.1002/ejhf.3404

Heart failure is a widespread health issue with several available medications and devices for treatment. However, the rising prevalence of the disease, combined with its poor prognosis, poses a significant burden on global healthcare systems. Although guideline-directed medical therapy is effective, its implementation is often slow and delayed, contributing to the increasing incidence of heart failure. This study explores the timing, settings, and predictors for initiating angiotensin receptor-neprilysin inhibitors (ARNI) to find better methods for managing heart failure. Clinical data indicates that ARNI is initiated in more severe heart failure patients and typically only after clinical deterioration has occurred.

5.Association Between Cardiovascular Health and Subclinical Atherosclerosis Among Young Adults Using the American Heart Association's "Life's Essential 8" Metrics

DOI: 10.1161/JAHA.123.033990

https://www.ahajournals.org/doi/10.1161/JAHA.123.033990

For decades, cardiovascular disease (CVD) has been one of the most prevalent chronic diseases in the United States and worldwide. Recognizing the clinical significance of CVD prevention and management, the American Heart Association (AHA) released a new set of cardiovascular health (CVH) guidelines in 2022, titled Life's Essential 8 (LE8), aimed at improving, maintaining, and reducing the risk of cardiovascular events in the general population. Clinical observations indicate that cardiovascular risk factors increase more rapidly in young people compared to older adults. This study investigates the association between the AHA's cardiovascular health guidelines, Life's Essential 8 (LE8) and Life's Simple 7 (LS7), and carotid outcomes in young adults. According to the study results, young individuals who adhere more closely to the AHA guidelines have a lower risk of subclinical atherosclerosis.