Literature DB >> 21518090

Concurrent validation and reliability of digital image analysis of granulation tissue color for clinical pressure ulcers.

Shinji Iizaka1, Junko Sugama, Gojiro Nakagami, Toshiko Kaitani, Ayumi Naito, Hiroe Koyanagi, Junko Matsuo, Takafumi Kadono, Chizuko Konya, Hiromi Sanada.   

Abstract

Granulation tissue color is one indicator for pressure ulcer (PU) assessment. However, it entails a subjective evaluation only, and quantitative methods have not been established. We developed color indicators from digital image analysis and investigated their concurrent validity and reliability for clinical PUs. A cross-sectional study was conducted on 47 patients with 55 full-thickness PUs. After color calibration, a wound photograph was converted into three images representing red color: erythema index (EI), modified erythema index with additional color calibration (granulation red index [GRI]), and , which represents the artificially created red-green axis of L(*) a(*) b(*) color space. The mean intensity of the granulation tissue region and the percentage of pixels exceeding the optimal cutoff intensity (% intensity) were calculated. Mean GRI (ρ=0.39, p=0.007) and (ρ=0.55, p<0.001), as well as their % intensity indicators, showed positive correlations with a(*) measured by tristimulus colorimeter, but erythema index did not. They were correlated with hydroxyproline concentration in wound fluid, healthy granulation tissue area, and blood hemoglobin level. Intra- and interrater reliability of the indicator calculation using both GRI and had an intraclass correlation coefficient >0.9. GRI and from digital image analysis can quantitatively evaluate granulation tissue color of clinical PUs.
© 2011 by the Wound Healing Society.

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Year:  2011        PMID: 21518090     DOI: 10.1111/j.1524-475X.2011.00686.x

Source DB:  PubMed          Journal:  Wound Repair Regen        ISSN: 1067-1927            Impact factor:   3.617


  2 in total

Review 1.  Points to consider for skin ulcers in systemic sclerosis.

Authors:  Felice Galluccio; Yannick Allanore; Lázló Czirjak; Daniel E Furst; Dinesh Khanna; Marco Matucci-Cerinic
Journal:  Rheumatology (Oxford)       Date:  2017-09-01       Impact factor: 7.580

2.  Development of a Method for Clinical Evaluation of Artificial Intelligence-Based Digital Wound Assessment Tools.

Authors:  Raelina S Howell; Helen H Liu; Aziz A Khan; Jon S Woods; Lawrence J Lin; Mayur Saxena; Harshit Saxena; Michael Castellano; Patrizio Petrone; Eric Slone; Ernest S Chiu; Brian M Gillette; Scott A Gorenstein
Journal:  JAMA Netw Open       Date:  2021-05-03
  2 in total

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