Evidence map›Paper›PMID 32140183›Full record

ArticleThe EPMA journal2020

Implementation of artificial intelligence and non-contact infrared thermography for prediction and personalized automatic identification of different stages of cellulite.

Joanna Bauer, Md Nazmul Hoq, John Mulcahy, Syed A M Tofail, Fahmida Gulshan, Christophe Silien, Halina Podbielska, Md Mostofa Akbar

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

Article in The EPMA journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed
2.4field-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

8 citing papers in PubMed, 39 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. 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

8 authors at 3 institutions in 3 countries.

Joanna Bauer1Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, Wybrzeze Wyspiańskiego 27, 50-370 Wrocław, Poland.ORCID 0000-0002-8521-1979
Md Nazmul Hoq2Department of Physics and Bernal Institute, University of Limerick, Limerick, Ireland.ORCID https://orcid.org/0000-0002-1846-0537
John Mulcahy2Department of Physics and Bernal Institute, University of Limerick, Limerick, Ireland.ORCID https://orcid.org/0000-0001-8281-577X
Syed A M Tofail2Department of Physics and Bernal Institute, University of Limerick, Limerick, Ireland.ORCID https://orcid.org/0000-0003-4212-3351
Fahmida Gulshan4Department of Materials and Metallurgical Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.ORCID https://orcid.org/0000-0001-8803-259X
Christophe Silien2Department of Physics and Bernal Institute, University of Limerick, Limerick, Ireland.ORCID https://orcid.org/0000-0003-0426-2166
Halina Podbielska1Department of Biomedical Engineering, Faculty of Fundamental Problems of Technology, Wrocław University of Science and Technology, Wybrzeze Wyspiańskiego 27, 50-370 Wrocław, Poland.ORCID https://orcid.org/0000-0001-7162-8530
Md Mostofa Akbar3Department of Computer Science and Engineering, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh.ORCID https://orcid.org/0000-0002-5307-157X
Bangladesh University of Engineering and Technology · BDUniversity of Limerick · IEWrocław University of Science and Technology · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCellulite is a common physiological condition of dermis, epidermis, and subcutaneous tissues experienced by 85 to 98% of the post-pubertal females in developed countries. Infrared (IR) thermography combined with artificial intelligence (AI)-based automated image processing can detect both early and advanced cellulite stages and open up the possibility of reliable diagnosis. Although the cellulite lesions may have various levels of severity, the quality of life of every woman, both in the physical and emotional sphere, is always an individual concern and therefore requires patient-oriented approach.

objectivesThe purpose of this work was to elaborate an objective, fast, and cost-effective method for automatic identification of different stages of cellulite based on IR imaging that may be used for prescreening and personalization of the therapy. MATERIALS AND

methodsIn this study, we use custom-developed image preprocessing algorithms to automatically select cellulite regions and combine a total of 9 feature extraction methods with 9 different classification algorithms to determine the efficacy of cellulite stage recognition based on thermographic images taken from 212 female volunteers aged between 19 and 22.

resultsA combination of histogram of oriented gradients (HOG) and artificial neural network (ANN) enables determination of all stages of cellulite with an average accuracy higher than 80%. For primary stages of cellulite, the average accuracy achieved was more than 90%.

conclusionsThe implementation of computer-aided, automatic identification of cellulite severity using infrared imaging is feasible for reliable diagnosis. Such a combination can be used for early diagnosis, as well as monitoring of cellulite progress or therapeutic outcomes in an objective way. IR thermography coupled to AI sets the vision towards their use as an effective tool for complex assessment of cellulite pathogenesis and stratification, which are critical in the implementation of IR thermographic imaging in predictive, preventive, and personalized medicine (PPPM).

Indexed as

Artificial intelligenceCelluliteInfrared thermographyPrediction and health monitoringPredictive preventive personalized medicine

Identifiers

PMID32140183
PMCPMC7028894
OpenAlexW3006442352

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.