Evidence map›Paper›PMID 36018623›Full record

ArticleJMIR formative research2022

Development of a Noninvasive Blood Glucose Monitoring System Prototype: Pilot Study.

Maria Valero, Priyanka Pola, Oluwaseyi Falaiye, Katherine H Ingram, Liang Zhao, Hossain Shahriar, Sheikh Iqbal Ahamed

Open access · goldAbstract read
In one paragraph

Article in JMIR formative research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 1 pooled it
6.9field-weighted citation impact, top 3% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed, 1 synthesis or guideline pooled it, 26 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. AI-Based Noninvasive Blood Glucose Monitoring: Scoping Review.Journal of medical Internet research · 2024
    Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors at 2 institutions in 1 country.

Maria ValeroDepartment of Information Technology, Kennesaw State University, Marietta, GA, United States.ORCID https://orcid.org/0000-0001-8913-9604
Priyanka PolaDepartment of Information Technology, Kennesaw State University, Marietta, GA, United States.ORCID https://orcid.org/0000-0002-6918-7112
Oluwaseyi FalaiyeDepartment of Software Engineering and Game Development, Kennesaw State University, Marietta, GA, United States.ORCID https://orcid.org/0000-0001-8889-9396
Katherine H IngramDepartment of Exercise Science and Sport Management, Kennesaw State University, Kennesaw, GA, United States.ORCID https://orcid.org/0000-0002-3052-9461
Liang ZhaoDepartment of Information Technology, Kennesaw State University, Marietta, GA, United States.ORCID https://orcid.org/0000-0003-3910-3536
Hossain ShahriarDepartment of Information Technology, Kennesaw State University, Marietta, GA, United States.ORCID https://orcid.org/0000-0003-1021-7986
Sheikh Iqbal AhamedDepartment of Computer Science, Marquette University, Milwaukee, WI, United States.ORCID https://orcid.org/0000-0001-5385-7647
Kennesaw State University · USMarquette University · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes mellitus is a severe disease characterized by high blood glucose levels resulting from dysregulation of the hormone insulin. Diabetes is managed through physical activity and dietary modification and requires careful monitoring of blood glucose concentration. Blood glucose concentration is typically monitored throughout the day by analyzing a sample of blood drawn from a finger prick using a commercially available glucometer. However, this process is invasive and painful, and leads to a risk of infection. Therefore, there is an urgent need for noninvasive, inexpensive, novel platforms for continuous blood sugar monitoring.

objectiveOur study aimed to describe a pilot test to test the accuracy of a noninvasive glucose monitoring prototype that uses laser technology based on near-infrared spectroscopy.

methodsOur system is based on Raspberry Pi, a portable camera (Raspberry Pi camera), and a visible light laser. The Raspberry Pi camera captures a set of images when a visible light laser passes through skin tissue. The glucose concentration is estimated by an artificial neural network model using the absorption and scattering of light in the skin tissue. This prototype was developed using TensorFlow, Keras, and Python code. A pilot study was run with 8 volunteers that used the prototype on their fingers and ears. Blood glucose values obtained by the prototype were compared with commercially available glucometers to estimate accuracy.

resultsWhen using images from the finger, the accuracy of the prototype is 79%. Taken from the ear, the accuracy is attenuated to 62%. Though the current data set is limited, these results are encouraging. However, three main limitations need to be addressed in future studies of the prototype: (1) increase the size of the database to improve the robustness of the artificial neural network model; (2) analyze the impact of external factors such as skin color, skin thickness, and ambient temperature in the current prototype; and (3) improve the prototype enclosure to make it suitable for easy finger and ear placement.

conclusionsOur pilot study demonstrates that blood glucose concentration can be estimated using a small hardware prototype that uses infrared images of human tissue. Although more studies need to be conducted to overcome limitations, this pilot study shows that an affordable device can be used to avoid the use of blood and multiple finger pricks for blood glucose monitoring in the diabetic population.

Indexed as

deep learningdiabetesglucose concentrationglucose monitoringmachine learningnoninvasive monitoringoptical sensors

Identifiers

PMID36018623
PMCPMC9463623
OpenAlexW4284962959

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.