Evidence map›Paper›PMID 37810055›Full record

ArticleHeliyon2023

A novel multipurpose device for dataset creation and on-device immediate estimation of blood glucose level from reflection ppg.

Kazi Mosaddequr, Tanzilur Rahman

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.1field-weighted citation impact, top 11% 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

2 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. 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

2 authors at 1 institution in 1 country.

Kazi MosaddequrDepartment of Electrical & Computer Engineering, North South University, Dhaka 1229, Bangladesh.
Tanzilur RahmanDepartment of Electrical & Computer Engineering, North South University, Dhaka 1229, Bangladesh.
North South University · BD

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We propose a completely non-invasive and highly accurate portable blood glucose estimator, which is simple hardware that anyone, regardless of their prior experience or knowledge, can use to obtain an immediate reading of their blood sugar level. Glucose levels can be monitored in real time and displayed on the device thanks to its infrared LED light source, sensor with built-in amplification unit, processing unit with blood glucose regression model, power management unit for autonomous operation, and display. The device was initially used to create a dataset of photoplethysmography (PPG) signals collected from the fingertip of 50 subjects. The extracted signal features were correlated with the subject's glucose level, which was measured at the same time using a commercial glucometer, and several regression models were constructed. The models were evaluated using signals from up to 110 subjects across three datasets, and the most optimized model was implemented in the device to predict the subject's blood glucose level solely based on PPG in real-time. The device with the built-in model has been subjected to extensive testing to gauge its efficacy. The device's clinical accuracy is encouraging. The pricey strips and needles that must be purchased along with the hardware in the conventional method will no longer be necessary with this device.

Identifiers

PMID37810055
PMCPMC10558790
OpenAlexW4386314704

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.