一种无创血糖监测系统原型的开发:卡塔尔世界杯8强波胆分析试点研究瓦莱罗，玛丽亚·波拉，普里扬卡·法莱耶，奥卢瓦塞伊·英格拉姆，凯瑟琳·H·A Zhao，梁·Shahriar，侯赛因·A·艾哈迈德，谢赫·伊克巴尔+肯尼索州立大学信息技术系，阿恩森大道680号，J312套，GA, 30060，美国，1470 578 4552，mvalero2@kennesaw.edu %K糖尿病%K深度学习%K机器学习%K葡萄糖浓度%K无创监测%K光学传感器%K葡萄糖监测%D 2022 %7 26.8.2022 %9原始论文%J JMIR表格Res %G英文%X背景:糖尿病是一种严重的疾病，其特征是由胰岛素激素调节异常引起的高血糖水平。糖尿病可以通过体育锻炼和饮食调整来控制，需要仔细监测血糖浓度。血糖浓度通常是全天通过分析从手指刺取的血液样本来监测的，使用的是市售的血糖计。然而，这个过程是侵入性的和痛苦的，并导致感染的风险。因此，迫切需要无创、廉价、新型的血糖连续监测平台。目的:我们的研究旨在描述一个试点试验，以测试基于近红外光谱的激光技术的无创葡萄糖监测原型的准确性。方法:我们的系统基于树莓派，便携式相机(树莓派相机)和可见光激光。当可见光激光穿过皮肤组织时，树莓派相机就会捕捉到一组图像。 The glucose concentration is estimated by an artificial neural network model using the absorption and scattering of light in the skin tissue. This prototype was developed using TensorFlow, Keras, and Python code. A pilot study was run with 8 volunteers that used the prototype on their fingers and ears. Blood glucose values obtained by the prototype were compared with commercially available glucometers to estimate accuracy. Results: When using images from the finger, the accuracy of the prototype is 79%. Taken from the ear, the accuracy is attenuated to 62%. Though the current data set is limited, these results are encouraging. However, three main limitations need to be addressed in future studies of the prototype: (1) increase the size of the database to improve the robustness of the artificial neural network model; (2) analyze the impact of external factors such as skin color, skin thickness, and ambient temperature in the current prototype; and (3) improve the prototype enclosure to make it suitable for easy finger and ear placement. Conclusions: Our pilot study demonstrates that blood glucose concentration can be estimated using a small hardware prototype that uses infrared images of human tissue. Although more studies need to be conducted to overcome limitations, this pilot study shows that an affordable device can be used to avoid the use of blood and multiple finger pricks for blood glucose monitoring in the diabetic population. %M 36018623 %R 10.2196/38664 %U https://formative.www.mybigtv.com/2022/8/e38664 %U https://doi.org/10.2196/38664 %U http://www.ncbi.nlm.nih.gov/pubmed/36018623