Evidence map›Paper›PMID 37998166›Full record

ArticleBiosensors2023

Portable Infrared-Based Glucometer Reinforced with Fuzzy Logic.

Hasan Mhd Nazha, Mhd Ayham Darwich, Ebrahim Ismaiel, Anas Shahen, Tamim Nasser, Maher Assaad, Daniel Juhre

Open access · goldAbstract read
In one paragraph

Article in Biosensors, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.9field-weighted citation impact, top 9% 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, 8 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Review
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 3 institutions in 3 countries.

Hasan Mhd NazhaComputational Mechanics, Faculty of Mechanical Engineering, Otto Von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.ORCID 0000-0003-1531-1824
Mhd Ayham DarwichFaculty of Technical Engineering, Tartous University, Tartous P.O. Box 2147, Syria.ORCID 0000-0002-2027-7950
Ebrahim IsmaielFaculty of Biomedical Engineering, Al-Andalus University for Medical Sciences, Tartous P.O. Box 101, Syria.ORCID 0000-0003-4139-9639
Anas ShahenFaculty of Biomedical Engineering, Al-Andalus University for Medical Sciences, Tartous P.O. Box 101, Syria.
Tamim NasserFaculty of Biomedical Engineering, Al-Andalus University for Medical Sciences, Tartous P.O. Box 101, Syria.
Maher AssaadDepartment of Electrical and Computer Engineering, College of Engineering and IT, Ajman University, Ajman P.O. Box 346, United Arab Emirates.ORCID 0000-0002-1584-8747
Daniel JuhreComputational Mechanics, Faculty of Mechanical Engineering, Otto Von Guericke University Magdeburg, Universitätsplatz 2, 39106 Magdeburg, Germany.ORCID 0000-0001-8997-3818
Al Andalus University · SYOtto-von-Guericke-Universität Magdeburg · DEAjman University · AE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetes mellitus (DM) is a chronic metabolic condition characterized by high blood glucose levels owing to decreased insulin production or sensitivity. Current diagnostic approaches for gestational diabetes entail intrusive blood tests, which are painful and impractical for regular monitoring. Additionally, typical blood glucose monitoring systems are restricted in their measurement frequency and need finger pricks for blood samples. This research study focuses on the development of a non-invasive, real-time glucose monitoring method based on the detection of glucose in human tears and finger blood using mid-infrared (IR) spectroscopy. The proposed solution combines a fuzzy logic-based calibration mechanism with an IR sensor and Arduino controller. This calibration technique increases the accuracy of non-invasive glucose testing based on MID absorbance in fingertips and human tears. The data demonstrate that our device has high accuracy and reliability, with an error rate of less than 3%, according to the EGA. Out of 360 measurements, 97.5% fell into zone A, 2.2% into zone B, and 0.3% into zone C of the Clarke Error Grid. This suggests that our device can give clinically precise and acceptable estimates of blood glucose levels without inflicting any harm or discomfort on the user.

Indexed as

Blood Glucose Self-MonitoringDiabetes Mellitus, Type 2Blood GlucoseFuzzy LogicGlucoseHumansReproducibility of ResultsBlood GlucoseGlucosefuzzy logicglucose monitoringmid-infrared probetears

Identifiers

PMID37998166
PMCPMC10669386
OpenAlexW4388834296

What Socratic holds

Textmetadata
LicenceCC BY
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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 Socratic graph.