Noninvasive Self-monitoring of Blood Glucose at Your Fingertips, Literally!: Smartphone-Based Photoplethysmography

Abstract

AbstractDiabetes is a chronic disease and one of the major public health problems worldwide. It is a multifactorial disease, caused by genetic factors and lifestyle habits. Brazil had ∼ 16.8 million individuals living with diabetes in 2019 and is expected to reach 26 million people by 2045. There are global increasing needs for the development of noninvasive diagnostic methods and use of mobile health, mainly in face of the pandemic caused by the coronavirus disease 2019 (COVID-19). For daily glycemic control, diabetic patients use a portable glucometer for glycemic self-monitoring and need to prick their fingertips three or more times a day, generating a huge discomfort throughout their lives. Our goal here is to present a review with very recent emerging studies in the field of noninvasive diagnosis and to emphasize that smartphone-based photoplethysmography (spPPG), powered by artificial intelligence, might be a trend to self-monitor blood glucose levels. In photoplethysmography, a light source travels through the tissue, interacts with the interstitium and with cells and molecules present in the blood. Reflection of light occurs as it passes through the biological tissues and a photodetector can capture these interactions. When using a smartphone, the built-in flashlight is a white light-emitting LED and the camera works as a photodetector. The higher the concentration of circulating glucose, the greater the absorbance and, consequently, the lesser the reflected light intensity will be. Due to these optical phenomena, the signal intensity captured will be inversely proportional to the blood glucose level. Furthermore, we highlight the microvascular changes in the progression of diabetes that can interfere in the signals captured by the photodetector using spPPG, due to the decrease of peripheral blood perfusion, which can be confused with high blood glucose levels. It is necessary to create strategies to filter or reduce the impact of these vascular changes in the blood glucose level analysis. Deep learning strategies can help the machine to solve these challenges, allowing an accurate blood glucose level and interstitial glucose prediction.