Bar-Ilan University, Faculty of Engineering, Ramat Gan, Israel.
Indian Institute of Technology (Indian School of Mines) Dhanbad, Department of Electronics Engineering, Dhanbad, Jharkhand, India.
J Biomed Opt. 2023 Aug;28(8):087001. doi: 10.1117/1.JBO.28.8.087001. Epub 2023 Aug 1.
Diabetes is a prevalent disease worldwide that can cause severe health problems. Accurate blood glucose detection is crucial for diabetes management, and noninvasive methods can be more convenient and less painful than traditional finger-prick methods.
We aim to report a noncontact speckle-based blood glucose measurement system that utilizes artificial intelligence (AI) data processing to improve glucose detection accuracy. The study also explores the influence of an alternating current (AC) induced magnetic field on the sensitivity and selectivity of blood glucose detection.
The proposed blood glucose sensor consists of a digital camera, an AC-generated magnetic field source, a laser illuminating the subject's finger, and a computer. A magnetic field is applied to the finger, and a camera records the speckle patterns generated by the laser light reflected from the finger. The acquired video data are preprocessed for machine learning (ML) and deep neural networks (DNNs) to classify blood plasma glucose levels. The standard finger-prick method is used as a reference for blood glucose level classification.
The study found that the noncontact speckle-based blood glucose measurement system with AI data processing allows for the detection of blood plasma glucose levels with high accuracy. The ML approach gives better results than the tested DNNs as the proposed data preprocessing is highly selective and efficient.
The proposed noncontact blood glucose sensing mechanism utilizing AI data processing and a magnetic field can potentially improve glucose detection accuracy, making it more convenient and less painful for patients. The system also allows for inexpensive blood glucose sensing mechanisms and fast blood glucose screening. The results suggest that noninvasive methods can improve blood glucose detection accuracy, which can have significant implications for diabetes management. Investigations involving representative sampling data, including subjects of different ages, gender, race, and health status, could allow for further improvement.
糖尿病是一种在全球普遍存在的疾病，会导致严重的健康问题。准确的血糖检测对糖尿病管理至关重要，非侵入性方法比传统的手指刺破方法更方便、痛苦更小。
我们旨在报告一种基于散斑的非接触式血糖测量系统，该系统利用人工智能（AI）数据处理来提高血糖检测的准确性。该研究还探讨了交流（AC）感应磁场对血糖检测灵敏度和选择性的影响。
所提出的血糖传感器由数码相机、AC 产生的磁场源、照亮受试者手指的激光和计算机组成。磁场施加到手指上，相机记录从手指反射的激光产生的散斑图案。获取的视频数据经过机器学习（ML）和深度神经网络（DNN）的预处理，以对血糖水平进行分类。标准的手指刺破法被用作血糖水平分类的参考。
研究发现，具有人工智能数据处理的非接触式基于散斑的血糖测量系统能够高精度地检测血浆血糖水平。与测试的 DNN 相比，ML 方法的结果更好，因为所提出的数据预处理具有高度的选择性和效率。
所提出的利用人工智能数据处理和磁场的非接触式血糖传感机制有可能提高血糖检测的准确性，使患者更方便、痛苦更小。该系统还允许使用廉价的血糖传感机制和快速的血糖筛查。结果表明，非侵入性方法可以提高血糖检测的准确性，这对糖尿病管理具有重要意义。涉及代表性抽样数据的研究，包括不同年龄、性别、种族和健康状况的受试者，可以进一步提高。
