Department of Nursing, Ng Teng Fong General Hospital, Singapore, Singapore.
Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore.
J Med Internet Res. 2024 Nov 19;26:e58892. doi: 10.2196/58892.
Current blood glucose monitoring (BGM) methods are often invasive and require repetitive pricking of a finger to obtain blood samples, predisposing individuals to pain, discomfort, and infection. Noninvasive blood glucose monitoring (NIBGM) is ideal for minimizing discomfort, reducing the risk of infection, and increasing convenience.
This review aimed to map the use cases of artificial intelligence (AI) in NIBGM.
A systematic scoping review was conducted according to the Arksey O'Malley five-step framework. Eight electronic databases (CINAHL, Embase, PubMed, Web of Science, Scopus, The Cochrane-Central Library, ACM Digital Library, and IEEE Xplore) were searched from inception until February 8, 2023. Study selection was conducted by 2 independent reviewers, descriptive analysis was conducted, and findings were presented narratively. Study characteristics (author, country, type of publication, study design, population characteristics, mean age, types of noninvasive techniques used, and application, as well as characteristics of the BGM systems) were extracted independently and cross-checked by 2 investigators. Methodological quality appraisal was conducted using the Checklist for assessment of medical AI.
A total of 33 papers were included, representing studies from Asia, the United States, Europe, the Middle East, and Africa published between 2005 and 2023. Most studies used optical techniques (n=19, 58%) to estimate blood glucose levels (n=27, 82%). Others used electrochemical sensors (n=4), imaging (n=2), mixed techniques (n=2), and tissue impedance (n=1). Accuracy ranged from 35.56% to 94.23% and Clarke error grid (A+B) ranged from 86.91% to 100%. The most popular machine learning algorithm used was random forest (n=10) and the most popular deep learning model was the artificial neural network (n=6). The mean overall checklist for assessment of medical AI score on the included papers was 33.5 (SD 3.09), suggesting an average of medium quality. The studies reviewed demonstrate that some AI techniques can accurately predict glucose levels from noninvasive sources while enhancing comfort and ease of use for patients. However, the overall range of accuracy was wide due to the heterogeneity of models and input data.
Efforts are needed to standardize and regulate the use of AI technologies in BGM, as well as develop consensus guidelines and protocols to ensure the quality and safety of AI-assisted monitoring systems. The use of AI for NIBGM is a promising area of research that has the potential to revolutionize diabetes management.
目前的血糖监测（BGM）方法往往具有侵入性，需要反复刺破手指以获取血样，这容易导致个体疼痛、不适和感染。非侵入性血糖监测（NIBGM）是一种理想的方法，可以最大限度地减少不适，降低感染风险，提高便利性。
本综述旨在绘制人工智能（AI）在 NIBGM 中的应用案例图。
根据 Arksey O'Malley 的五步框架进行了系统的范围界定综述。从开始到 2023 年 2 月 8 日，在 8 个电子数据库（CINAHL、Embase、PubMed、Web of Science、Scopus、The Cochrane-Central Library、ACM Digital Library 和 IEEE Xplore）中进行了搜索。由 2 名独立评审员进行研究选择，进行描述性分析，并以叙述性方式呈现结果。独立提取研究特征（作者、国家、出版物类型、研究设计、人口特征、平均年龄、使用的非侵入性技术类型以及应用，以及 BGM 系统的特征），由 2 名调查人员交叉核对。使用评估医疗 AI 的清单对方法学质量进行评估。
共纳入 33 篇论文，代表了 2005 年至 2023 年期间来自亚洲、美国、欧洲、中东和非洲的研究。大多数研究使用光学技术（n=19，58%）来估计血糖水平（n=27，82%）。其他研究使用电化学传感器（n=4）、成像（n=2）、混合技术（n=2）和组织阻抗（n=1）。准确性范围从 35.56%到 94.23%，Clarke 误差网格（A+B）范围从 86.91%到 100%。使用最多的机器学习算法是随机森林（n=10），使用最多的深度学习模型是人工神经网络（n=6）。纳入研究的平均整体评估医疗 AI 清单得分为 33.5（SD 3.09），表明平均质量中等。综述中的研究表明，一些 AI 技术可以从非侵入性来源准确预测血糖水平，同时提高患者的舒适度和易用性。然而，由于模型和输入数据的异质性，整体准确性范围很广。
需要努力规范和监管 BGM 中 AI 技术的使用，并制定共识指南和协议，以确保 AI 辅助监测系统的质量和安全性。AI 在 NIBGM 中的应用是一个很有前途的研究领域，有可能彻底改变糖尿病管理。
