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Automated vs. human evaluation of corneal staining
BACKGROUND AND PURPOSE: Corneal fluorescein staining is one of the most important diagnostic tests in dry eye disease (DED). Nevertheless, the result of this examination is depending on the grader. So far, there is no method for an automated quantification of corneal staining commercially available....
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer Berlin Heidelberg
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325848/ https://www.ncbi.nlm.nih.gov/pubmed/35357547 http://dx.doi.org/10.1007/s00417-022-05574-0 |
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author | Kourukmas, R. Roth, M. Geerling, G. |
author_facet | Kourukmas, R. Roth, M. Geerling, G. |
author_sort | Kourukmas, R. |
collection | PubMed |
description | BACKGROUND AND PURPOSE: Corneal fluorescein staining is one of the most important diagnostic tests in dry eye disease (DED). Nevertheless, the result of this examination is depending on the grader. So far, there is no method for an automated quantification of corneal staining commercially available. Aim of this study was to develop a software-assisted grading algorithm and to compare it with a group of human graders with variable clinical experience in patients with DED. METHODS: Fifty images of eyes stained with 2 µl of 2% fluorescein presenting different severity of superficial punctate keratopathy in patients with DED were taken under standardized conditions. An algorithm for detecting and counting superficial punctate keratitis was developed using ImageJ with a training dataset of 20 randomly picked images. Then, the test dataset of 30 images was analyzed (1) by the ImageJ algorithm and (2) by 22 graders, all ophthalmologists with different levels of experience. All graders evaluated the images using the Oxford grading scheme for corneal staining at baseline and after 6–8 weeks. Intrarater agreement was also evaluated by adding a mirrored version of all original images into the set of images during the 2nd grading. RESULTS: The count of particles detected by the algorithm correlated significantly (n = 30; p < 0.01) with the estimated true Oxford grade (Sr = 0,91). Overall human graders showed only moderate intrarater agreement (K = 0,426), while software-assisted grading was always the same (K = 1,0). Little difference was found between specialists and non-specialists in terms of intrarater agreement (K = 0,436 specialists; K = 0,417 non-specialists). The highest interrater agreement was seen with 75,6% in the most experienced grader, a cornea specialist with 29 years of experience, and the lowest was seen in a resident with 25,6% who had only 2 years of experience. CONCLUSION: The variance in human grading of corneal staining - if only small - is likely to have only little impact on clinical management and thus seems to be acceptable. While human graders give results sufficient for clinical application, software-assisted grading of corneal staining ensures higher consistency and thus is preferrable for re-evaluating patients, e.g., in clinical trials. [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00417-022-05574-0. |
format | Online Article Text |
id | pubmed-9325848 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Springer Berlin Heidelberg |
record_format | MEDLINE/PubMed |
spelling | pubmed-93258482022-07-28 Automated vs. human evaluation of corneal staining Kourukmas, R. Roth, M. Geerling, G. Graefes Arch Clin Exp Ophthalmol Cornea BACKGROUND AND PURPOSE: Corneal fluorescein staining is one of the most important diagnostic tests in dry eye disease (DED). Nevertheless, the result of this examination is depending on the grader. So far, there is no method for an automated quantification of corneal staining commercially available. Aim of this study was to develop a software-assisted grading algorithm and to compare it with a group of human graders with variable clinical experience in patients with DED. METHODS: Fifty images of eyes stained with 2 µl of 2% fluorescein presenting different severity of superficial punctate keratopathy in patients with DED were taken under standardized conditions. An algorithm for detecting and counting superficial punctate keratitis was developed using ImageJ with a training dataset of 20 randomly picked images. Then, the test dataset of 30 images was analyzed (1) by the ImageJ algorithm and (2) by 22 graders, all ophthalmologists with different levels of experience. All graders evaluated the images using the Oxford grading scheme for corneal staining at baseline and after 6–8 weeks. Intrarater agreement was also evaluated by adding a mirrored version of all original images into the set of images during the 2nd grading. RESULTS: The count of particles detected by the algorithm correlated significantly (n = 30; p < 0.01) with the estimated true Oxford grade (Sr = 0,91). Overall human graders showed only moderate intrarater agreement (K = 0,426), while software-assisted grading was always the same (K = 1,0). Little difference was found between specialists and non-specialists in terms of intrarater agreement (K = 0,436 specialists; K = 0,417 non-specialists). The highest interrater agreement was seen with 75,6% in the most experienced grader, a cornea specialist with 29 years of experience, and the lowest was seen in a resident with 25,6% who had only 2 years of experience. CONCLUSION: The variance in human grading of corneal staining - if only small - is likely to have only little impact on clinical management and thus seems to be acceptable. While human graders give results sufficient for clinical application, software-assisted grading of corneal staining ensures higher consistency and thus is preferrable for re-evaluating patients, e.g., in clinical trials. [Image: see text] SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00417-022-05574-0. Springer Berlin Heidelberg 2022-03-31 2022 /pmc/articles/PMC9325848/ /pubmed/35357547 http://dx.doi.org/10.1007/s00417-022-05574-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Cornea Kourukmas, R. Roth, M. Geerling, G. Automated vs. human evaluation of corneal staining |
title | Automated vs. human evaluation of corneal staining |
title_full | Automated vs. human evaluation of corneal staining |
title_fullStr | Automated vs. human evaluation of corneal staining |
title_full_unstemmed | Automated vs. human evaluation of corneal staining |
title_short | Automated vs. human evaluation of corneal staining |
title_sort | automated vs. human evaluation of corneal staining |
topic | Cornea |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9325848/ https://www.ncbi.nlm.nih.gov/pubmed/35357547 http://dx.doi.org/10.1007/s00417-022-05574-0 |
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