Аннотации:
Understanding the correlation between different organ activities is essential for advancing knowledge in physiology, medicine, and bioengineering. While existing literature often focuses on individual organs, a significant gap remains in synthesizing the diverse mathematical methodologies used to decode complex multi-organ relationships. This review addresses that gap by providing a comprehensive analysis of advanced mathematical tools - including time series analysis, signal processing, entropy measures, fractal theory, network modeling, and machine learning (ML) - that have been applied to characterize dynamic inter-organ communication. We discuss how these methods reveal nonlinear, causal, and scale-invariant relationships among organ systems and how they are used to predict pathological conditions, monitor health status, and inform personalized interventions. By bridging theoretical models with clinical applications, this review offers a unified framework for understanding systemic physiology and supports future advancements in multi-organ diagnostics and therapies.
