TY - JOUR AU - Ahmed, Arfan AU - Aziz, Sarah AU - Abd-alrazaq, Alaa AU - Farooq, Faisal AU - Sheikh, Javaid PY - 2022 DA - 2022/8/9 TI -人工智能驱动的糖尿病可穿戴设备概述:范围审查JO - J Med Internet Res SP - e36010 VL - 24 IS - 8 KW -糖尿病KW -人工智能KW -可穿戴设备KW -机器学习KW -手机AB -背景:在过去的几十年里，糖尿病的患病率稳步上升，仅2012年就有150万人死亡。传统上，分析糖尿病患者仍然是一种主要的侵入性方法。可穿戴设备(WDs)利用了过去为医院设置保留的传感器。WDs与人工智能(AI)算法相结合，有望帮助从收集的数据中理解和总结有意义的信息，并提供先进的和有临床意义的分析。目的:本文综述了人工智能驱动的糖尿病WD特征及其在糖尿病相关参数监测中的应用。方法:我们使用3组与糖尿病、白癜风和人工智能相关的搜索词搜索了7个最流行的文献数据库。研究选择遵循两个阶段的过程:阅读摘要和标题，然后是全文筛选。两名审稿人独立进行了研究选择和数据提取，分歧通过协商一致解决。采用叙述的方法来综合数据。 Results: 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%). Conclusions: 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. SN - 1438-8871 UR - //www.mybigtv.com/2022/8/e36010 UR - https://doi.org/10.2196/36010 UR - http://www.ncbi.nlm.nih.gov/pubmed/35943772 DO - 10.2196/36010 ID - info:doi/10.2196/36010 ER -