Optimization of psoriasis assessment system based on patch images.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
13 09 2021
13 09 2021
Historique:
received:
16
03
2021
accepted:
12
08
2021
entrez:
14
9
2021
pubmed:
15
9
2021
medline:
16
12
2021
Statut:
epublish
Résumé
Psoriasis is a chronic inflammatory skin disease that occurs in various forms throughout the body and is associated with certain conditions such as heart disease, diabetes, and depression. The psoriasis area severity index (PASI) score, a tool used to evaluate the severity of psoriasis, is currently used in clinical trials and clinical research. The determination of severity is based on the subjective judgment of the clinician. Thus, the disease evaluation deviations are induced. Therefore, we propose optimal algorithms that can effectively segment the lesion area and classify the severity. In addition, a new dataset on psoriasis was built, including patch images of erythema and scaling. We performed psoriasis lesion segmentation and classified the disease severity. In addition, we evaluated the best-performing segmentation method and classifier and analyzed features that are highly related to the severity of psoriasis. In conclusion, we presented the optimal techniques for evaluating the severity of psoriasis. Our newly constructed dataset improved the generalization performance of psoriasis diagnosis and evaluation. It proposed an optimal system for specific evaluation indicators of the disease and a quantitative PASI scoring method. The proposed system can help to evaluate the severity of localized psoriasis more accurately.
Identifiants
pubmed: 34518578
doi: 10.1038/s41598-021-97211-9
pii: 10.1038/s41598-021-97211-9
pmc: PMC8437948
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
18130Informations de copyright
© 2021. The Author(s).
Références
J Eur Acad Dermatol Venereol. 2020 Jun;34(6):1362-1368
pubmed: 31594034
Sci Rep. 2017 Dec 1;7(1):16744
pubmed: 29196632
Comput Methods Programs Biomed. 2017 Oct;150:9-22
pubmed: 28859832
J Eur Acad Dermatol Venereol. 2020 Mar;34(3):518-524
pubmed: 31541556
Adv Clin Exp Med. 2017 Aug;26(5):851-856
pubmed: 29068583
IEEE Trans Pattern Anal Mach Intell. 2017 Dec;39(12):2481-2495
pubmed: 28060704
J Med Imaging (Bellingham). 2017 Oct;4(4):044004
pubmed: 29152533
Med Phys. 2019 Jul;46(7):3180-3193
pubmed: 31071228
Comput Methods Programs Biomed. 2016 Apr;126:98-109
pubmed: 26830378
IEEE Trans Image Process. 2013 Jan;22(1):258-71
pubmed: 22910114
J Psoriasis Psoriatic Arthritis. 2020 Oct;5(4):147-159
pubmed: 33733038
J Med Eng Technol. 2009;33(6):426-36
pubmed: 19557605
Skin Res Technol. 2017 Nov;23(4):563-572
pubmed: 28543966
J Eur Acad Dermatol Venereol. 2018 Aug;32(8):1314-1319
pubmed: 29569769
Skin Res Technol. 2018 Aug;24(3):490-498
pubmed: 29468741
Sensors (Basel). 2020 Aug 11;20(16):
pubmed: 32796654
J Bioinform Comput Biol. 2005 Apr;3(2):185-205
pubmed: 15852500
J Am Acad Dermatol. 2014 Mar;70(3):512-6
pubmed: 24388724
Skin Res Technol. 2019 Jul;25(4):434-439
pubmed: 30657211
BMC Med Imaging. 2019 Aug 23;19(1):73
pubmed: 31443642
Comput Biol Med. 2011 Aug;41(8):726-35
pubmed: 21722886