Evidence map›Paper›PMID 37736396›Full record

ArticleClinical, cosmetic and investigational dermatology2023

A Cluster Validity Index-Based Objective Criteria for Aesthetic Evaluation of Periorbital Treatment.

Çağlar Cengizler, Ayse Gul Kabakci, Dursun Murat Bozkır, Dilek Sire Eren, Memduha Gülhal Bozkır

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In one paragraph

Article in Clinical, cosmetic and investigational dermatology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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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

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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.

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

5 authors.

Çağlar Cengizler *Biomedical Device Technology Program, Vocational School of Health Services, Izmir Democracy University, Izmir, Turkey.
Ayse Gul Kabakci *Department of Anatomy, Faculty of Medicine, Cukurova University, Adana, Turkey.ORCID 0000-0001-7144-8759
Dursun Murat Bozkır *Adana Sevgi Eye Center, Adana, Turkey.
Dilek Sire Eren *Dr Dilek Eren Medical Aesthetic Clinic, Adana, Turkey.
Memduha Gülhal Bozkır *Department of Anatomy, Faculty of Medicine, Cukurova University, Adana, Turkey.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Dark circles and pigmentation around the eyes are common reasons people see dermatologists. An effective assessment of the severity of infraorbital color and texture differences is critical for determining appropriate treatment. Evaluation of the visual severity of cases is mostly based on visual inspection. Treatment efficiency is often measured using patient questionnaires in many cases. The subjectivity of assessments may lead to a prolonged healing process, misdiagnosis, and excessive use of drugs or chemicals. Patients and Methods: In this study, a computer-aided objective evaluation approach was proposed for grading periorbital facial rejuvenation. This approach is based on the analysis of numerical features extracted from different facial regions in digital images. Healing was objectively graded by evaluating data clusters formed using the extracted features. Accordingly, an increase in the visual similarity between paired facial regions is accepted as an indicator of healing, which directly affects the form of data clusters. An intracluster validity index was measured to evaluate the clusters as dense and well separated. A total of 144 facial regions were extracted and examined, and the automatically calculated grades were compared with expert evaluations. Results: The cosmetic effects of the experimental drug were evaluated during the experiments, and expert grades were accepted as the ground truth. The results show that the proposed automated grading approach can evaluate rejuvenation with an accuracy of up to 0.91 accuracy in the upper orbital region. Conclusion: This study concluded that the proposed data-clustering-based approach is promising and can be functional without any special instruments.

Indexed as

colour cosmeticscomputer modelinghealingskin structure

Identifiers

PMID37736396
PMCPMC10509608

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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.