Biosensing, Monitoring, and Machine Learning-Driven Physiological Control in Mechanical Circulatory Support

Mechanical circulatory support (MCS) systems have fundamentally transformed the management of acute and end-stage heart failure, bridging the gap to recovery, transplantation, or serving as destination therapy. Despite remarkable clinical progress, current devices still face major limitations, including hemocompatibility-related adverse events, infection risks, battery and power constraints, and a lack of dynamic physiological autonomy. Addressing these challenges increasingly depends on the integration of biosensing and biomolecular electronics with device engineering: implantable and wearable sensors, continuous physiological monitoring, and intelligent data-driven control systems are becoming central to safer, more adaptive MCS solutions. This Research Topic focuses on the sensing, monitoring, and computational technologies that enable next-generation short- and long-term MCS devices to detect complications earlier, respond to changing physiological demand, and support safer long-term use.This Research Topic aims to showcase cutting-edge bioengineering breakthroughs and translational innovations in the biosensing, monitoring, and intelligent control of mechanical circulatory support systems. We invite original research, reviews, and methods articles that bridge the gap between sensor and biomolecular electronics engineering, biomaterials science, and cardiorespiratory device design. Our goal is to bring together contributions that advance implantable and wearable biosensors, real-time physiological monitoring, and machine learning-driven predictive and control algorithms for MCS, alongside the biomaterial and interface innovations that support safe, hemocompatible long-term sensor and device integration. By consolidating this work in one collection, we hope to accelerate the translation of sensing and monitoring technologies from bench to bedside, improve early detection of device-related complications, and support the development of MCS systems with greater physiological autonomy and improved patient safety. We particularly welcome contributions that connect novel sensor or electronic developments to demonstrated or proposed improvements in device monitoring, control, or safety outcomes.Submissions are welcomed across, but not limited to, the following topics: • Implantable and wearable biosensors for continuous flow, pressure, and physiological status monitoring in MCS• Biomolecular electronics and telemetry systems for early detection of suction events and hemocompatibility-related adverse events• Machine learning and predictive algorithms for patient risk stratification, anomaly detection, and automated pump management• Sensor-integrated smart pumping and physiological control strategies, including adaptive, feedback-driven speed modulation• Advanced biomaterials and novel surfaces supporting biosensor integration, including anti-thrombogenic and endothelialisation strategies• Sensor and monitoring innovations for next-generation cannulas, conduits, and Total Artificial Heart systemsWe also welcome studies using device- or sensor-derived monitoring data from patients, such as pump data from an implanted LVAD used as a physiological sensor during ICU or ward-based care, where the focus is on sensing, monitoring, or engineering methodology. Technical or data-rich studies drawn from a single patient or a small patient series are welcome and do not need to be framed as case reports. We welcome Original Research, Reviews, Systematic Reviews, Methods, and Perspective articles. In line with the Biosensors and Biomolecular Electronics section’s scope, this Research Topic does not accept clinical trials or bibliometric analyses.Dr. Thomas Schlöglhofer is a consultant and advisor for Medtronic Inc., Abbott Inc., Berlin Heart, BiVACOR, and CorWave, and receives research grants from BrioHealth Solutions, Bravida Medical, Abbott Inc., Berlin Heart, BiVACOR, CorWave, and J&J MedTech. Topic editors: Thomas Schlöglhofer, Jamshid Karimov Source: https://www.frontiersin.org/research-topics/84727/biosensing-monitoring-and-machine-learning-driven-physiological-control-in-mechanical-circulatory-support