Skin tear classification using machine learning from digital RGB image.
Skin tears are traumatic wounds characterised by separation of the skin layers. Severity evaluation is important in the management of skin tears. To support the assessment and management of skin tears, this study aimed to develop an algorithm to estimate a category of the Skin Tear Audit Research cl...
| Publicado en: | Journal of Tissue Viability Vol. 30; no. 4; pp. 588 - 594 |
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| Autores principales: | , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Elsevier B.V.
Nov2021
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=153682676&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 153682676 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 0965206X YZQ jtl: Journal of Tissue Viability issn: 0965206X maglogo: N pubinfo: dt: Nov2021 vid: 30 iid: 4 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 153682676 153682676 153682676 10.1016/j.jtv.2021.01.004 153682676 ppf: 588 ppct: 6 formats: tig: atl: Skin tear classification using machine learning from digital RGB image. aug: au: Nagata, Takuro Noyori, Shuhei S. Noguchi, Hiroshi Nakagami, Gojiro Kitamura, Aya Sanada, Hiromi affil: School of Public Health, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan sug: subj: Machine Learning Tears and Lacerations Classification Skin Pathology Algorithms Digital Imaging Human Image Processing, Computer Assisted Random Forest Support Vector Machine Descriptive Statistics Wound Care Wound Measurement ab: Skin tears are traumatic wounds characterised by separation of the skin layers. Severity evaluation is important in the management of skin tears. To support the assessment and management of skin tears, this study aimed to develop an algorithm to estimate a category of the Skin Tear Audit Research classification system (STAR classification) using digital images via machine learning. This was achieved by introducing shape features representing complicated shape of the skin tears. A skin tear image was separated into small segments, and features of each segment were estimated. The segments were then classified into different classes by machine learning algorithms, namely support vector machine and random forest. Their performance in classifying wound segments and STAR categories was evaluated with 31 images using the leave-one-out cross validation. Support vector machine showed an accuracy of 74% and 69% in classifying wound segments and STAR categories, respectively. The corresponding accuracy using random forest were 71% and 63%. Machine learning algorithms revealed capable of classifying categories of skin tears. This could offer the potential to aid nurses in their management of skin tears, even if they are not specialised in wound care. • Skin tear assessment is difficult for nurses who are not specialised in wound care. • The STAR estimation algorithm showed moderate agreement to experts' classification. • Standardised evaluation by the algorithm will contribute to precise care. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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