Innovative Advances in Non-Invasive Detection Technologies for Heart Failure: Synergistic Application of Multimodal...

Heart failure (HF) remains a leading global cause of chronic disease-related disability and mortality, with rising incidence driven largely by population aging. Early diagnosis is challenging because initial symptoms are often subtle and non-specific, leading to delayed detection and poor prognosis. While conventional tools such as echocardiography and B-type natriuretic peptide (BNP) testing remain diagnostic gold standards, these approaches are limited by operator dependency, restricted accessibility, and dynamic monitoring. Recent advances in artificial intelligence (AI) and cloud computing have enabled a new generation of non-invasive, intelligent technologies that integrate wearable sensors (e.g., ReDS™) with multimodal platforms (e.g., HeartLogic™, CardioSignal) to support real-time risk tracking and personalized management. Indeed, supported by favorable policy environments and strengthened collaboration among manufacturers, clinicians, and researchers across multiple fields and disciplines, the development of intelligent non-invasive HF detection devices has accelerated, leading to rapid innovation, commercialization, and continuous emergence of novel technologies and products. This review systematically summarizes HF pathophysiological mechanisms and current clinical monitoring strategies. Moreover, this review critically evaluates emerging devices and AI-driven platforms, highlighting the associated underlying principles, data integration capabilities, and clinical applicability. Finally, the analysis addresses key challenges, including the "black box" dilemma associated with AI, data bias, and privacy concerns, and proposes future directions for early screening, risk stratification, and precision intervention. By synthesizing technological comparisons and limitations, this review aims to provide a comprehensive reference for advancing intelligent HF diagnostics.