Accepted for/Published in: Journal of Medical Internet Research
Date Submitted: Apr 1, 2024
Date Accepted: Oct 8, 2024
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
The use of artificial intelligence for non-invasive blood glucose monitoring: A scoping review
ABSTRACT
Background:
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. Non-invasive blood glucose monitoring (NIBGMS) is ideal in minimising discomfort, reducing the risk of infection, and increasing convenience.
Objective:
Our main objective was to map the use cases of artificial intelligence (AI) in non-invasive continuous blood glucose monitoring.
Methods:
A systematic scoping review was conducted according to the Arksey O’Malley five-step framework. Seven electronic databases (EMBASE, Pubmed, Web of Science, Scopus, The Cochrane-Central Library, ACM Digital Library and IEEE Xplore) were searched from inception until 08 February 2023. Study selection was conducted by two independent reviewers, descriptive analysis was conducted, and findings were presented narratively.
Results:
33 articles were included, representing studies from Asia, US, Europe, Middle East and Africa published between 2005 and 2023. Most studies used optical techniques (n=19, 57.6%) to estimate blood glucose levels (n=27, 81.8%). 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 artificial neural network (n=6). The studies reviewed demonstrate that some AI techniques can accurately predict glucose levels from non-invasive 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.
Conclusions:
Efforts are needed to standardize and regulate the use of AI technologies in blood glucose monitoring, 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.
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