Machine learning in biofluid mechanics: A review of recent developments.

This review paper comprehensively examines recent advancements in machine learning (ML) applications within biofluid mechanics, with a targeted focus on enabling clinically actionable diagnostics and simulations. It demonstrates how ML, and in particular physics-informed ML methods, are used to enhance the analysis and understanding of intricate biofluid dynamics. The review systematically analyzes various ML techniques, detailing their strengths and limitations in modeling biofluid behaviors. By integrating physics-informed ML methods, such as Physics-Informed Neural Networks (PINNs), this work addresses critical challenges in translating complex biofluid dynamics into practical clinical tools. Differentiating itself from previous literature, this review not only summarizes current methods but also proposes potential solutions-including data augmentation, transfer learning, and hybrid modeling approaches (e.g., PINNs)-to overcome challenges related to limited datasets and the integration of complex physics. The review emphasizes ML's ability to enhance diagnostic accuracy, enable personalized treatment strategies, and accelerate computational simulations for applications like cardiovascular disease detection and respiratory disorder diagnosis, with findings showing that ML-driven approaches can reduce diagnostic errors by up to 30 % in cardiovascular applications and improve early detection rates in metabolomics-based diagnostics. Findings indicate that while ML techniques have significantly improved predictive capabilities in biofluid dynamics, challenges such as data scarcity and multi-scale physical integration remain critical. By outlining strategies to bridge the gap between ML advancements and clinical implementation, this review provides a robust framework for future research aimed at integrating ML with biofluid mechanics to revolutionize healthcare delivery. The paper concludes by identifying future research directions aimed at further integrating ML with domain-specific physical insights to achieve more reliable and accurate biofluid models.