Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness - Report - MDSpire
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Federated learning for cardiovascular disease prediction: a systematic review of clinical applications, validation, and translation readiness
Clinical Report: A Systematic Review of Federated Learning Applications in Cardiovascular Disease Prediction
Overview
This systematic review evaluates the implementation of federated learning in cardiovascular disease prediction, highlighting its potential while noting significant gaps in clinical validation and readiness for real-world application. The findings emphasize the need for improved validation methods and operational feasibility to enhance the deployment of these models.
Background
Cardiovascular disease (CVD) remains a leading cause of mortality globally, necessitating effective predictive models for early detection and management. Traditional machine learning approaches face challenges due to data privacy concerns and institutional data silos, limiting the potential for multi-center collaboration. Federated learning presents a solution by enabling model training across institutions without sharing sensitive patient data, thus addressing privacy issues while leveraging diverse datasets.
Data Highlights
No numerical data available in the source material.
Key Findings
Twenty-two studies were reviewed, covering various cardiovascular prediction tasks.
Most studies utilized horizontal federated learning with FedAvg baselines.
Federated learning performance often approached or exceeded centralized training benchmarks.
The evidence base was predominantly retrospective, with inconsistent reporting on validation and privacy safeguards.
Key adoption priorities include external validation, subgroup fairness monitoring, and robust privacy measures.
Clinical Implications
The findings suggest that while federated learning holds promise for enhancing cardiovascular disease prediction, there is a critical need for rigorous validation and operational frameworks to ensure clinical readiness. Healthcare professionals should be aware of the limitations in current evidence and the importance of robust validation strategies.
Conclusion
Federated learning represents a significant advancement in the privacy-preserving prediction of cardiovascular disease, yet further research is necessary to establish its clinical efficacy and readiness for widespread implementation.
