Implementation of a Quality Index for Improvement of Quantification of Corneal Nerves in Corneal Confocal Microcopy Images

A Multicenter Study

Dietrich Sturm, Jan Vollert, Tineke Greiner, Andrew S.C. Rice, Harriet Kemp, Rolf Detlef Treede, Sigrid Schuh-Hofer, Stine E. Nielsen, Lynn Eitner, Martin Tegenthoff, Ioannis N. Petropoulos, Rayaz Malik, Christoph Maier, Tobias Schmidt-Wilcke, Marc Schargus

Research output: Contribution to journalArticle

Abstract

PURPOSE: Corneal confocal microscopy (CCM) is an imaging method to detect loss of nerve fibers in the cornea. The impact of image quality on the CCM parameters has not been investigated. We developed a quality index (QI) with 3 stages for CCM images and compared the influence of the image quality on the quantification of corneal nerve parameters using 2 modes of analysis in healthy volunteers and patients with known peripheral neuropathy. METHODS: Images of 75 participants were a posteriori analyzed, including 25 each in 3 image quality groups (QI 1-QI 3). Corneal nerve fiber length (CNFL) was analyzed using automated and semiautomated software, and corneal nerve fiber density and corneal nerve branch density were quantified using automated image analysis. Three masked raters assessed CCM image quality (QI) independently and categorized images into groups QI 1-QI 3. In addition, statistical analysis was used to compare interrater reliability. Analysis of variance was used for analysis between the groups. Interrater reliability analysis between the image ratings was performed by calculating Fleiss' kappa and its 95% confidence interval. RESULTS: CNFL, corneal nerve fiber density, and corneal nerve branch density increased significantly with QI (P < 0.001, all post hoc tests P < 0.05). CNFL was higher using semiautomated compared with automated nerve analysis, independent of QI. Fleiss kappa coefficient for interrater reliability of QI was 0.72. CONCLUSIONS: The quantification of corneal nerve parameters depends on image quality, and poorer quality images are associated with lower values for corneal nerve parameters. We propose the QI as a tool to reduce variability in quantification of corneal nerve parameters.

Original languageEnglish
Pages (from-to)921-926
Number of pages6
JournalCornea
Volume38
Issue number7
DOIs
Publication statusPublished - 1 Jul 2019

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Quality Improvement
Nerve Fibers
Multicenter Studies
Confocal Microscopy
Peripheral Nervous System Diseases
Cornea
Analysis of Variance
Healthy Volunteers
Software
Confidence Intervals

ASJC Scopus subject areas

  • Ophthalmology

Cite this

Implementation of a Quality Index for Improvement of Quantification of Corneal Nerves in Corneal Confocal Microcopy Images : A Multicenter Study. / Sturm, Dietrich; Vollert, Jan; Greiner, Tineke; Rice, Andrew S.C.; Kemp, Harriet; Treede, Rolf Detlef; Schuh-Hofer, Sigrid; Nielsen, Stine E.; Eitner, Lynn; Tegenthoff, Martin; Petropoulos, Ioannis N.; Malik, Rayaz; Maier, Christoph; Schmidt-Wilcke, Tobias; Schargus, Marc.

In: Cornea, Vol. 38, No. 7, 01.07.2019, p. 921-926.

Research output: Contribution to journalArticle

Sturm, D, Vollert, J, Greiner, T, Rice, ASC, Kemp, H, Treede, RD, Schuh-Hofer, S, Nielsen, SE, Eitner, L, Tegenthoff, M, Petropoulos, IN, Malik, R, Maier, C, Schmidt-Wilcke, T & Schargus, M 2019, 'Implementation of a Quality Index for Improvement of Quantification of Corneal Nerves in Corneal Confocal Microcopy Images: A Multicenter Study', Cornea, vol. 38, no. 7, pp. 921-926. https://doi.org/10.1097/ICO.0000000000001949
Sturm, Dietrich ; Vollert, Jan ; Greiner, Tineke ; Rice, Andrew S.C. ; Kemp, Harriet ; Treede, Rolf Detlef ; Schuh-Hofer, Sigrid ; Nielsen, Stine E. ; Eitner, Lynn ; Tegenthoff, Martin ; Petropoulos, Ioannis N. ; Malik, Rayaz ; Maier, Christoph ; Schmidt-Wilcke, Tobias ; Schargus, Marc. / Implementation of a Quality Index for Improvement of Quantification of Corneal Nerves in Corneal Confocal Microcopy Images : A Multicenter Study. In: Cornea. 2019 ; Vol. 38, No. 7. pp. 921-926.
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abstract = "PURPOSE: Corneal confocal microscopy (CCM) is an imaging method to detect loss of nerve fibers in the cornea. The impact of image quality on the CCM parameters has not been investigated. We developed a quality index (QI) with 3 stages for CCM images and compared the influence of the image quality on the quantification of corneal nerve parameters using 2 modes of analysis in healthy volunteers and patients with known peripheral neuropathy. METHODS: Images of 75 participants were a posteriori analyzed, including 25 each in 3 image quality groups (QI 1-QI 3). Corneal nerve fiber length (CNFL) was analyzed using automated and semiautomated software, and corneal nerve fiber density and corneal nerve branch density were quantified using automated image analysis. Three masked raters assessed CCM image quality (QI) independently and categorized images into groups QI 1-QI 3. In addition, statistical analysis was used to compare interrater reliability. Analysis of variance was used for analysis between the groups. Interrater reliability analysis between the image ratings was performed by calculating Fleiss' kappa and its 95{\%} confidence interval. RESULTS: CNFL, corneal nerve fiber density, and corneal nerve branch density increased significantly with QI (P < 0.001, all post hoc tests P < 0.05). CNFL was higher using semiautomated compared with automated nerve analysis, independent of QI. Fleiss kappa coefficient for interrater reliability of QI was 0.72. CONCLUSIONS: The quantification of corneal nerve parameters depends on image quality, and poorer quality images are associated with lower values for corneal nerve parameters. We propose the QI as a tool to reduce variability in quantification of corneal nerve parameters.",
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AU - Greiner, Tineke

AU - Rice, Andrew S.C.

AU - Kemp, Harriet

AU - Treede, Rolf Detlef

AU - Schuh-Hofer, Sigrid

AU - Nielsen, Stine E.

AU - Eitner, Lynn

AU - Tegenthoff, Martin

AU - Petropoulos, Ioannis N.

AU - Malik, Rayaz

AU - Maier, Christoph

AU - Schmidt-Wilcke, Tobias

AU - Schargus, Marc

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N2 - PURPOSE: Corneal confocal microscopy (CCM) is an imaging method to detect loss of nerve fibers in the cornea. The impact of image quality on the CCM parameters has not been investigated. We developed a quality index (QI) with 3 stages for CCM images and compared the influence of the image quality on the quantification of corneal nerve parameters using 2 modes of analysis in healthy volunteers and patients with known peripheral neuropathy. METHODS: Images of 75 participants were a posteriori analyzed, including 25 each in 3 image quality groups (QI 1-QI 3). Corneal nerve fiber length (CNFL) was analyzed using automated and semiautomated software, and corneal nerve fiber density and corneal nerve branch density were quantified using automated image analysis. Three masked raters assessed CCM image quality (QI) independently and categorized images into groups QI 1-QI 3. In addition, statistical analysis was used to compare interrater reliability. Analysis of variance was used for analysis between the groups. Interrater reliability analysis between the image ratings was performed by calculating Fleiss' kappa and its 95% confidence interval. RESULTS: CNFL, corneal nerve fiber density, and corneal nerve branch density increased significantly with QI (P < 0.001, all post hoc tests P < 0.05). CNFL was higher using semiautomated compared with automated nerve analysis, independent of QI. Fleiss kappa coefficient for interrater reliability of QI was 0.72. CONCLUSIONS: The quantification of corneal nerve parameters depends on image quality, and poorer quality images are associated with lower values for corneal nerve parameters. We propose the QI as a tool to reduce variability in quantification of corneal nerve parameters.

AB - PURPOSE: Corneal confocal microscopy (CCM) is an imaging method to detect loss of nerve fibers in the cornea. The impact of image quality on the CCM parameters has not been investigated. We developed a quality index (QI) with 3 stages for CCM images and compared the influence of the image quality on the quantification of corneal nerve parameters using 2 modes of analysis in healthy volunteers and patients with known peripheral neuropathy. METHODS: Images of 75 participants were a posteriori analyzed, including 25 each in 3 image quality groups (QI 1-QI 3). Corneal nerve fiber length (CNFL) was analyzed using automated and semiautomated software, and corneal nerve fiber density and corneal nerve branch density were quantified using automated image analysis. Three masked raters assessed CCM image quality (QI) independently and categorized images into groups QI 1-QI 3. In addition, statistical analysis was used to compare interrater reliability. Analysis of variance was used for analysis between the groups. Interrater reliability analysis between the image ratings was performed by calculating Fleiss' kappa and its 95% confidence interval. RESULTS: CNFL, corneal nerve fiber density, and corneal nerve branch density increased significantly with QI (P < 0.001, all post hoc tests P < 0.05). CNFL was higher using semiautomated compared with automated nerve analysis, independent of QI. Fleiss kappa coefficient for interrater reliability of QI was 0.72. CONCLUSIONS: The quantification of corneal nerve parameters depends on image quality, and poorer quality images are associated with lower values for corneal nerve parameters. We propose the QI as a tool to reduce variability in quantification of corneal nerve parameters.

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