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INTERRATER RELIABILITY OF AN ADVANCED NONCONTACT WOUND MEASUREMENT SYSTEM USING ARTIFICIAL INTELLIGENCE
Venous leg ulcers (VLU) are recurring, disabling wounds that develop primarily in older adults with comorbidities. In an ongoing clinical trial testing a nutritional intervention researchers use advanced technology (Swift Skin and Wound) to measure VLU healing over time. This noncontact wound measur...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9766476/ http://dx.doi.org/10.1093/geroni/igac059.1658 |
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author | McDaniel, Jodi Tan, Alai Parinandi, Narasimham |
author_facet | McDaniel, Jodi Tan, Alai Parinandi, Narasimham |
author_sort | McDaniel, Jodi |
collection | PubMed |
description | Venous leg ulcers (VLU) are recurring, disabling wounds that develop primarily in older adults with comorbidities. In an ongoing clinical trial testing a nutritional intervention researchers use advanced technology (Swift Skin and Wound) to measure VLU healing over time. This noncontact wound measurement system uses artificial intelligence to automatically calculate wound measurements and track healing progress. However, a subjective component of the Swift system involves delineating wound perimeters on photographs captured with a tablet or smartphone. To evaluate interrater reliability of the system in the current study, measurements of 11 wounds by two independent raters were assessed using an intraclass correlation coefficient (ICC). ICC estimates and their 95% confidence intervals were calculated using SPSS statistical package version 27 based on a mean-rating (k = 2), consistency, 2-way mixed-effects model. For area measures, ICC = 0.99 with 95% confidence interval = 0.998-1.0, indicating the Swift measurement system has excellent interrater reliability. |
format | Online Article Text |
id | pubmed-9766476 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-97664762022-12-20 INTERRATER RELIABILITY OF AN ADVANCED NONCONTACT WOUND MEASUREMENT SYSTEM USING ARTIFICIAL INTELLIGENCE McDaniel, Jodi Tan, Alai Parinandi, Narasimham Innov Aging Abstracts Venous leg ulcers (VLU) are recurring, disabling wounds that develop primarily in older adults with comorbidities. In an ongoing clinical trial testing a nutritional intervention researchers use advanced technology (Swift Skin and Wound) to measure VLU healing over time. This noncontact wound measurement system uses artificial intelligence to automatically calculate wound measurements and track healing progress. However, a subjective component of the Swift system involves delineating wound perimeters on photographs captured with a tablet or smartphone. To evaluate interrater reliability of the system in the current study, measurements of 11 wounds by two independent raters were assessed using an intraclass correlation coefficient (ICC). ICC estimates and their 95% confidence intervals were calculated using SPSS statistical package version 27 based on a mean-rating (k = 2), consistency, 2-way mixed-effects model. For area measures, ICC = 0.99 with 95% confidence interval = 0.998-1.0, indicating the Swift measurement system has excellent interrater reliability. Oxford University Press 2022-12-20 /pmc/articles/PMC9766476/ http://dx.doi.org/10.1093/geroni/igac059.1658 Text en © The Author(s) 2022. Published by Oxford University Press on behalf of The Gerontological Society of America. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Abstracts McDaniel, Jodi Tan, Alai Parinandi, Narasimham INTERRATER RELIABILITY OF AN ADVANCED NONCONTACT WOUND MEASUREMENT SYSTEM USING ARTIFICIAL INTELLIGENCE |
title | INTERRATER RELIABILITY OF AN ADVANCED NONCONTACT WOUND MEASUREMENT SYSTEM USING ARTIFICIAL INTELLIGENCE |
title_full | INTERRATER RELIABILITY OF AN ADVANCED NONCONTACT WOUND MEASUREMENT SYSTEM USING ARTIFICIAL INTELLIGENCE |
title_fullStr | INTERRATER RELIABILITY OF AN ADVANCED NONCONTACT WOUND MEASUREMENT SYSTEM USING ARTIFICIAL INTELLIGENCE |
title_full_unstemmed | INTERRATER RELIABILITY OF AN ADVANCED NONCONTACT WOUND MEASUREMENT SYSTEM USING ARTIFICIAL INTELLIGENCE |
title_short | INTERRATER RELIABILITY OF AN ADVANCED NONCONTACT WOUND MEASUREMENT SYSTEM USING ARTIFICIAL INTELLIGENCE |
title_sort | interrater reliability of an advanced noncontact wound measurement system using artificial intelligence |
topic | Abstracts |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9766476/ http://dx.doi.org/10.1093/geroni/igac059.1658 |
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