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

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dc.contributor.author Bauer, Joanna
dc.contributor.author Hoq, Nazmul
dc.contributor.author Mulcahy, John
dc.contributor.author Tofail, Syed A.M.
dc.contributor.author Gulshan, Fahmida
dc.contributor.author Silien, Christophe
dc.contributor.author Podbielska, Halina
dc.contributor.author Akbar, Mostofa
dc.date.accessioned 2020-04-07T07:47:54Z
dc.date.available 2020-04-07T07:47:54Z
dc.date.issued 2020
dc.identifier.issn 1878-5077
dc.identifier.uri http://hdl.handle.net/10344/8699
dc.description peer-reviewed en_US
dc.description.abstract Background: Cellulite 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. Objectives: The 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 methods: In 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. Results: A 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%. Conclusions: The 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). en_US
dc.language.iso eng en_US
dc.publisher BMC en_US
dc.relation.ispartofseries EPMA Journal;11, pp. 17-29
dc.subject artificial intelligence en_US
dc.subject cellulite en_US
dc.subject infrared thermography en_US
dc.subject prediction and health monitoring en_US
dc.subject predictive preventive personalized medicine en_US
dc.title Implementation of artificial intelligence and non-contact infrared thermography for prediction and personalized automatic identification of different stages of cellulite en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.date.updated 2020-04-06T14:53:36Z
dc.description.version PUBLISHED
dc.identifier.doi 10.1007/s13167-020-00199-x
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US
dc.internal.rssid 2945093
dc.internal.copyrightchecked Yes
dc.identifier.journaltitle Epma Journal
dc.description.status peer-reviewed


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