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| http://dx.doi.org/10.1080/08998280.2017.11930237 | DOI Listing |
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Approaching a nationwide registry: analyzing big data in patients with heart failure.
Turk J Med Sci
December 2024
Department of Biostatistics and Medical Informatics, Faculty of Medicine, Mersin University, Mersin, Turkiye.
Background/aim: Randomized controlled trials usually lack generabilizity to real-world context. Real-world data, enabled by the use of big data analysis, serve as a connection between the results of trials and the implementation of findings in clinical practice. Nevertheless, using big data in the healthcare has difficulties such as ensuring data quality and consistency.
View Article and Find Full Text PDFDeep learning assists early-detection of hypertension-mediated heart change on ECG signals.
Hypertens Res
October 2024
Department of Automation, Xiamen University, Xiamen, Fujian, China.
Article Synopsis
- Arterial hypertension significantly increases the risk of cardiovascular diseases, and while cardiac ultrasound is a common diagnostic tool, it often misses early structural changes in the heart.
- This study introduces a deep learning model designed to analyze ECG signals for detecting subtle indicators of hypertension, leveraging a large dataset of ECG recordings.
- The model, MML-Net, showed impressive performance with an 82% recall and an 87% precision in identifying hypertension, surpassing typical recall rates of 30-50% achieved by cardiologists through visual inspections.
The Frailty Trajectory's Additional Edge Over the Frailty Index: Retrospective Cohort Study of Veterans With Heart Failure.
JMIR Aging
June 2024
VA Health Services Research & Development, Center for Innovations in Quality, Effectiveness and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, United States.
A Semi-supervised Four-Chamber Echocardiographic Video Segmentation Algorithm Based on Multilevel Edge Perception and Calibration Fusion.
Ultrasound Med Biol
September 2024
Department of Cardiology, People's Hospital of Guizhou Province, Guiyang, China.
Objective: Echocardiographic videos are commonly used for automatic semantic segmentation of endocardium, which is crucial in evaluating cardiac function and assisting doctors to make accurate diagnoses of heart disease. However, this task faces two distinct challenges: one is the edge blurring, which is caused by the presence of speckle noise or excessive de-noising operation, and the other is the lack of an effective feature fusion approach for multilevel features for obtaining accurate endocardium.
Methods: In this study, a deep learning model, based on multilevel edge perception and calibration fusion is proposed to improve the segmentation performance.
Decoding 2.3 million ECGs: interpretable deep learning for advancing cardiovascular diagnosis and mortality risk stratification.
Eur Heart J Digit Health
May 2024
Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, OX3 7DQ, UK.
Aims: Electrocardiogram (ECG) is widely considered the primary test for evaluating cardiovascular diseases. However, the use of artificial intelligence (AI) to advance these medical practices and learn new clinical insights from ECGs remains largely unexplored. We hypothesize that AI models with a specific design can provide fine-grained interpretation of ECGs to advance cardiovascular diagnosis, stratify mortality risks, and identify new clinically useful information.
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