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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.16968966 |
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| _version_ | 1866902339767500800 |
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| author | Okunola, Abiodun |
| author_facet | Okunola, Abiodun |
| contents | <p><span>Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, often presenting with late-stage symptoms after significant, irreversible damage has occurred. Traditional screening methods, while valuable, are largely reactive, resource-intensive, and can miss subtle, pre-clinical indicators of risk. This paper explores the paradigm shift towards proactive cardiovascular screening enabled by artificial intelligence (AI) and machine learning (ML). We review AI-driven frameworks that leverage diverse data sources, including electronic health records (EHRs), medical imaging (echocardiograms, cardiac CT scans), wearable device data, and genomic information. These frameworks utilize sophisticated algorithms, such as deep learning and ensemble methods, to identify complex, non-linear patterns predictive of future CVD events. We discuss key applications, including the development of enhanced risk stratification models that surpass traditional clinical scores, the automated analysis of imaging data for early detection of structural and functional abnormalities, and the continuous monitoring of physiological signals from wearables for personalized risk assessment. Furthermore, the paper addresses critical challenges for clinical implementation, including data privacy, algorithmic bias, model interpretability ("black box" problem), and the necessity for robust validation through large-scale clinical trials. The conclusion underscores that AI-driven frameworks hold immense potential to transform cardiovascular care from a reactive to a proactive, personalized, and preventative model, ultimately enabling earlier interventions and improving population health outcomes.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_16968966 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | AI-Driven Frameworks for Proactive Cardiovascular Screening Okunola, Abiodun <p><span>Cardiovascular diseases (CVDs) remain the leading cause of mortality globally, often presenting with late-stage symptoms after significant, irreversible damage has occurred. Traditional screening methods, while valuable, are largely reactive, resource-intensive, and can miss subtle, pre-clinical indicators of risk. This paper explores the paradigm shift towards proactive cardiovascular screening enabled by artificial intelligence (AI) and machine learning (ML). We review AI-driven frameworks that leverage diverse data sources, including electronic health records (EHRs), medical imaging (echocardiograms, cardiac CT scans), wearable device data, and genomic information. These frameworks utilize sophisticated algorithms, such as deep learning and ensemble methods, to identify complex, non-linear patterns predictive of future CVD events. We discuss key applications, including the development of enhanced risk stratification models that surpass traditional clinical scores, the automated analysis of imaging data for early detection of structural and functional abnormalities, and the continuous monitoring of physiological signals from wearables for personalized risk assessment. Furthermore, the paper addresses critical challenges for clinical implementation, including data privacy, algorithmic bias, model interpretability ("black box" problem), and the necessity for robust validation through large-scale clinical trials. The conclusion underscores that AI-driven frameworks hold immense potential to transform cardiovascular care from a reactive to a proactive, personalized, and preventative model, ultimately enabling earlier interventions and improving population health outcomes.</span></p> |
| title | AI-Driven Frameworks for Proactive Cardiovascular Screening |
| url | https://doi.org/10.5281/zenodo.16968966 |