Photoplethysmography and Artificial Intelligence for Blood Glucose Level Estimation in Diabetic Patients: A Scoping Review

New technologies, including artificial intelligence (AI), offer significant opportunities to improve blood glucose level (BGL) estimation systems, potentially enhancing care and quality of life for diabetic patients. This study aimed to assess the accuracy of BGL estimation using photoplethysmographic signal (PPG) analysis and AI methods by comparing various studies in terms of population, PPG signal acquisition and analysis, AI approaches, and BGL estimation performance. A systematic search was conducted in Scopus, Web of Science, Embase, PubMed and CINAHL databases. Conference proceedings and book chapters were included, excluding other gray literature, focusing on English-language studies published from 2010 to February 2024. Only publications concerning PPG signal analysis using AI algorithms for noninvasive estimation of BGL in patients with diabetes were considered. Of 48 identified articles, 24 were reviewed in full text, and 5 were deemed eligible. These studies varied in methodology (populations, devices, AI solutions) and evaluation metrics. However, all studies used Clarke error grid or Parkes error grid, with over 98% of estimates falling into clinically acceptable zones A or B. Current research confirm that PPG-based BGL estimation is feasible and accurate. Further studies are needed to overcome existing limitations and make this procedure available, accurate, and easy to perform.