Researchers developed a computational framework using peripheral photoplethysmography (PPG) signals to track aortic and mitral valve regurgitation severity over time. The system achieved tracking errors of 0.023-0.028 for aortic regurgitation and 0.075-0.101 for mitral regurgitation across 240 simulated patients over 36 months, with dicrotic notch timing proving most informative for both valve types. This represents a potential breakthrough in cardiovascular monitoring, as valve regurgitation currently requires periodic echocardiography in clinical settings to assess progression. The ability to continuously monitor these progressive heart valve diseases through wearable PPG sensors could enable earlier intervention and better patient outcomes. However, this work relies entirely on computational modeling and synthetic patient data rather than real clinical validation. The theoretical framework, while sophisticated, cannot account for the full complexity of human physiology and individual variations. As a preprint awaiting peer review, these promising computational results require validation in actual patients with valve disease before clinical implementation. The approach could transform cardiac care if validated, but remains an early-stage theoretical proof-of-concept.