Evidence map›Paper›PMID 37794995›Full record

ArticleJournal of diabetes research2023

Type 1 and Type 2 Diabetes Measurement Using Human Face Skin Region.

L Aneesh Euprazia, A Rajeswari, K K Thyagharajan, N R Shanker

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of diabetes research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 28% 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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

4 authors at 1 institution.

L Aneesh EupraziaComputer Science and Engineering, Velammal Engineering College, Chennai, India.ORCID https://orcid.org/0009-0002-6505-8804
A RajeswariComputer Science and Engineering, Velammal Engineering College, Chennai, India.ORCID https://orcid.org/0000-0002-4948-0688
K K ThyagharajanElectronics and Communication Engineering, R.M.D Engineering College, Chennai, India.ORCID https://orcid.org/0000-0002-3666-6619
N R ShankerDepartment of Computer Science and Engineering, Aalim Muhammed Salegh College of Engineering, Chennai, India.ORCID https://orcid.org/0000-0001-7821-3178
R.M.D. Engineering College

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Aim: Analyse the diabetes mellitus (DM) of a person through the facial skin region using vision diabetology. Diabetes mellitus is caused by persistent high blood glucose levels and related complications, which show variation in facial skin regions due to reduced blood flow in the facial arteries. Results: The existing deep learning models are compared with the proposed MWTCNN model, which provides the highest accuracy of 98.3%. Conclusion: The facial skin region-based diabetic measurement avoids pricking of the serum and is used for continuous glucose monitoring.

Indexed as

Diabetes Mellitus, Type 2Blood GlucoseBlood Glucose Self-MonitoringFaceHumansSkinBlood Glucose

Identifiers

PMID37794995
PMCPMC10547572
OpenAlexW4387073778

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.