Photoplethysmography-based glucose monitoring combined with hybrid machine learning models demonstrates significant potential for accurate, non-invasive continuous blood glucose measurement.
Systematic Review (n=106)
Does photoplethysmography (PPG) based glucose monitoring provide an accurate and feasible non-invasive method for measuring blood glucose levels?
PPG-based glucose monitoring combined with AI and hybrid physiological models represents a promising non-invasive alternative to conventional invasive glucometers for diabetes management.
Non-invasive and continuous blood glucose monitoring is crucial for effective diabetes management. Photoplethysmography (PPG) signal in wearable devices has gained recognition as a potential approach because of its simplicity, accessibility, and remote monitoring capability. This systematic analysis comprehensively assesses the feasibility, accuracy, and limitations of using PPG for measuring blood glucose levels. This review synthesizes 106 peer-reviewed studies, comprehensively analyzing the physiological principles and technological advancements of PPG-based glucose monitoring, while comparing it with conventional and emerging methods. Our analysis reveals several promising research directions in key areas. For PPG sensors, near-infrared wavelengths (850–940 nm) with reflective mode show better glucose sensitivity. AI-based methods, particularly deep learning approaches, often show improved performance in PPG signal preprocessing for motion artifact reduction compared to traditional techniques. Physical–mathematical models incorporating blood volume pulse characteristics could help identify novel PPG features correlating with glucose variations. Furthermore, hybrid approaches combining machine learning with physiological models show the most potential for accurate glucose level interpretation from PPG signals. These findings provide guidance for future research to advance PPG-based glucose monitoring toward clinical implementation.
Jiang et al. (Thu,) conducted a systematic review in Diabetes (n=106). Photoplethysmography (PPG) based glucose monitoring vs. Conventional invasive methodologies was evaluated. Photoplethysmography-based glucose monitoring combined with hybrid machine learning models demonstrates significant potential for accurate, non-invasive continuous blood glucose measurement.