Leveraging Digital Biomarkers and Advanced Data Analytics in Medical Laboratory to Enhance Early Detection and Diagnostic Accuracy in Cardiovascular Diseases

Abstract

Cardiovascular diseases (CVDs) remain a leading cause of morbidity and mortality worldwide, necessitating innovative approaches for early detection and accurate diagnosis. This review explores the integration of digital biomarkers and advanced data analytics in medical laboratory settings to enhance the precision, efficiency, and predictive capabilities of cardiovascular diagnostics. Digital biomarkers, derived from wearable sensors, electronic health records (EHRs), and imaging technologies, provide real-time, non-invasive monitoring of physiological parameters such as heart rate variability, blood pressure trends, and biochemical markers. Coupled with advanced data analytics, including machine learning algorithms and predictive modeling, these biomarkers enable early risk stratification, automated anomaly detection, and personalized treatment strategies. The review examines the role of artificial intelligence (AI) and big data in refining diagnostic workflows, reducing false positives and negatives, and improving clinical decision-making. Additionally, it highlights challenges such as data privacy, interoperability, and regulatory compliance in implementing digital biomarker-driven diagnostics. By bridging the gap between emerging technologies and clinical practice, this study underscores the transformative potential of digital biomarkers and data-driven methodologies in revolutionizing cardiovascular disease detection and management.