AI Center for Precision Health, Weill Cornell Medicine-Qatar, Doha, Qatar.
Center for Digital Health and Precision Medicine, Qatar Computing Research Institute, Doha, Qatar.
J Med Internet Res. 2022 Aug 9;24(8):e36010. doi: 10.2196/36010.
Prevalence of diabetes has steadily increased over the last few decades with 1.5 million deaths reported in 2012 alone. Traditionally, analyzing patients with diabetes has remained a largely invasive approach. Wearable devices (WDs) make use of sensors historically reserved for hospital settings. WDs coupled with artificial intelligence (AI) algorithms show promise to help understand and conclude meaningful information from the gathered data and provide advanced and clinically meaningful analytics.
This review aimed to provide an overview of AI-driven WD features for diabetes and their use in monitoring diabetes-related parameters.
We searched 7 of the most popular bibliographic databases using 3 groups of search terms related to diabetes, WDs, and AI. A 2-stage process was followed for study selection: reading abstracts and titles followed by full-text screening. Two reviewers independently performed study selection and data extraction, and disagreements were resolved by consensus. A narrative approach was used to synthesize the data.
From an initial 3872 studies, we report the features from 37 studies post filtering according to our predefined inclusion criteria. Most of the studies targeted type 1 diabetes, type 2 diabetes, or both (21/37, 57%). Many studies (15/37, 41%) reported blood glucose as their main measurement. More than half of the studies (21/37, 57%) had the aim of estimation and prediction of glucose or glucose level monitoring. Over half of the reviewed studies looked at wrist-worn devices. Only 41% of the study devices were commercially available. We observed the use of multiple sensors with photoplethysmography sensors being most prevalent in 32% (12/37) of studies. Studies reported and compared >1 machine learning (ML) model with high levels of accuracy. Support vector machine was the most reported (13/37, 35%), followed by random forest (12/37, 32%).
This review is the most extensive work, to date, summarizing WDs that use ML for people with diabetes, and provides research direction to those wanting to further contribute to this emerging field. Given the advancements in WD technologies replacing the need for invasive hospital setting devices, we see great advancement potential in this domain. Further work is needed to validate the ML approaches on clinical data from WDs and provide meaningful analytics that could serve as data gathering, monitoring, prediction, classification, and recommendation devices in the context of diabetes.
在过去的几十年中，糖尿病的患病率稳步上升，仅在 2012 年就报告了 150 万人死亡。传统上，对糖尿病患者的分析仍然是一种主要的侵入性方法。可穿戴设备 (WD) 利用了历史上仅用于医院环境的传感器。WD 与人工智能 (AI) 算法相结合，有望帮助从收集的数据中了解和得出有意义的信息，并提供先进且具有临床意义的分析。
本综述旨在概述用于糖尿病的人工智能驱动 WD 功能及其在监测与糖尿病相关的参数中的应用。
我们使用与糖尿病、WD 和 AI 相关的 3 组搜索词，在 7 个最受欢迎的文献数据库中进行了搜索。研究选择遵循 2 阶段流程：阅读摘要和标题，然后进行全文筛选。两名审查员独立进行研究选择和数据提取，如果存在分歧，则通过共识解决。采用叙述方法综合数据。
从最初的 3872 项研究中，我们根据预先确定的纳入标准，报告了经过过滤后的 37 项研究的特征。大多数研究针对 1 型糖尿病、2 型糖尿病或两者 (21/37，57%)。许多研究 (15/37，41%) 报告了血糖作为其主要测量指标。超过一半的研究 (21/37，57%) 旨在估计和预测血糖或监测血糖水平。综述的研究中有一半以上着眼于腕戴设备。只有 41% (12/37) 的研究设备可商业化使用。我们观察到使用了多种传感器，其中光体积描记传感器在 32% (12/37) 的研究中最为常见。研究报告并比较了 >1 种机器学习 (ML) 模型，这些模型具有很高的准确性。支持向量机是报告最多的 (13/37，35%)，其次是随机森林 (12/37，32%)。
这是迄今为止最全面的综述，总结了使用 ML 技术的 WD 对糖尿病患者的应用，并为那些希望进一步为这一新兴领域做出贡献的人提供了研究方向。鉴于 WD 技术的进步，取代了对侵入性医院设备的需求，我们看到了这个领域有很大的发展潜力。需要进一步的工作来验证 ML 方法在 WD 临床数据上的有效性，并提供有意义的分析，作为糖尿病背景下的数据收集、监测、预测、分类和推荐设备。
