Repeatability and reliability of semi-automated anterior segment-optical coherence tomography imaging compared to manual analysis in normal and keratoconus eyes
Purpose To assess the repeatability and reliability of semi-automated EyeMark Python program measurements compared to manual ImageJ image processing of anterior segment-optical coherence tomography (AS-OCT) structures in healthy and keratoconus eyes. Methods Heidelberg AS-OCT was used to image 25 ey...
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| Veröffentlicht in: | International ophthalmology Jg. 43; H. 12; S. 5063 - 5069 |
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01.12.2023
Springer Nature B.V |
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| Abstract | Purpose
To assess the repeatability and reliability of semi-automated EyeMark Python program measurements compared to manual ImageJ image processing of anterior segment-optical coherence tomography (AS-OCT) structures in healthy and keratoconus eyes.
Methods
Heidelberg AS-OCT was used to image 25 eyes from 14 healthy subjects and 25 eyes from 15 subjects with keratoconus between the ages of 20 and 80 years, collected prospectively, in this observational case–control study. Visual axis scan containing vertical fixation light beam was selected from the 15-line AS-OCT scan raster. Central corneal thickness (CCT), anterior corneal radius of curvature (ACRC), posterior corneal radius of curvature (PCRC), and truncated anterior vault (TAV) were measured using ImageJ software and the EyeMark Python program. MedCalc and R were used to calculate the intraclass correlation coefficient (ICC) and generate Bland–Altman plots (BAP).
Results
When comparing the measurements of CCT, ACRC, PCRC, and TAV between manual ImageJ analysis and the EyeMark Python program, ICC values were consistently greater than 0.9, indicating excellent agreement. BAPs comparing the ImageJ and Python measurements of anterior segment structures show no systematic proportional bias and the average differences were near zero and within 95% of the limits of agreement.
Conclusions
Semi-automated tools may provide the necessary efficiency for point-of-care quantitative corneal analysis of raw AS-OCT images. The semi-automated EyeMark Python program offers a repeatable and reliable tool compared to manual ImageJ analysis for measuring anterior segment structures from AS-OCT images among individuals with keratoconus. |
|---|---|
| AbstractList | PurposeTo assess the repeatability and reliability of semi-automated EyeMark Python program measurements compared to manual ImageJ image processing of anterior segment-optical coherence tomography (AS-OCT) structures in healthy and keratoconus eyes.MethodsHeidelberg AS-OCT was used to image 25 eyes from 14 healthy subjects and 25 eyes from 15 subjects with keratoconus between the ages of 20 and 80 years, collected prospectively, in this observational case–control study. Visual axis scan containing vertical fixation light beam was selected from the 15-line AS-OCT scan raster. Central corneal thickness (CCT), anterior corneal radius of curvature (ACRC), posterior corneal radius of curvature (PCRC), and truncated anterior vault (TAV) were measured using ImageJ software and the EyeMark Python program. MedCalc and R were used to calculate the intraclass correlation coefficient (ICC) and generate Bland–Altman plots (BAP).ResultsWhen comparing the measurements of CCT, ACRC, PCRC, and TAV between manual ImageJ analysis and the EyeMark Python program, ICC values were consistently greater than 0.9, indicating excellent agreement. BAPs comparing the ImageJ and Python measurements of anterior segment structures show no systematic proportional bias and the average differences were near zero and within 95% of the limits of agreement.ConclusionsSemi-automated tools may provide the necessary efficiency for point-of-care quantitative corneal analysis of raw AS-OCT images. The semi-automated EyeMark Python program offers a repeatable and reliable tool compared to manual ImageJ analysis for measuring anterior segment structures from AS-OCT images among individuals with keratoconus. Purpose To assess the repeatability and reliability of semi-automated EyeMark Python program measurements compared to manual ImageJ image processing of anterior segment-optical coherence tomography (AS-OCT) structures in healthy and keratoconus eyes. Methods Heidelberg AS-OCT was used to image 25 eyes from 14 healthy subjects and 25 eyes from 15 subjects with keratoconus between the ages of 20 and 80 years, collected prospectively, in this observational case–control study. Visual axis scan containing vertical fixation light beam was selected from the 15-line AS-OCT scan raster. Central corneal thickness (CCT), anterior corneal radius of curvature (ACRC), posterior corneal radius of curvature (PCRC), and truncated anterior vault (TAV) were measured using ImageJ software and the EyeMark Python program. MedCalc and R were used to calculate the intraclass correlation coefficient (ICC) and generate Bland–Altman plots (BAP). Results When comparing the measurements of CCT, ACRC, PCRC, and TAV between manual ImageJ analysis and the EyeMark Python program, ICC values were consistently greater than 0.9, indicating excellent agreement. BAPs comparing the ImageJ and Python measurements of anterior segment structures show no systematic proportional bias and the average differences were near zero and within 95% of the limits of agreement. Conclusions Semi-automated tools may provide the necessary efficiency for point-of-care quantitative corneal analysis of raw AS-OCT images. The semi-automated EyeMark Python program offers a repeatable and reliable tool compared to manual ImageJ analysis for measuring anterior segment structures from AS-OCT images among individuals with keratoconus. To assess the repeatability and reliability of semi-automated EyeMark Python program measurements compared to manual ImageJ image processing of anterior segment-optical coherence tomography (AS-OCT) structures in healthy and keratoconus eyes.PURPOSETo assess the repeatability and reliability of semi-automated EyeMark Python program measurements compared to manual ImageJ image processing of anterior segment-optical coherence tomography (AS-OCT) structures in healthy and keratoconus eyes.Heidelberg AS-OCT was used to image 25 eyes from 14 healthy subjects and 25 eyes from 15 subjects with keratoconus between the ages of 20 and 80 years, collected prospectively, in this observational case-control study. Visual axis scan containing vertical fixation light beam was selected from the 15-line AS-OCT scan raster. Central corneal thickness (CCT), anterior corneal radius of curvature (ACRC), posterior corneal radius of curvature (PCRC), and truncated anterior vault (TAV) were measured using ImageJ software and the EyeMark Python program. MedCalc and R were used to calculate the intraclass correlation coefficient (ICC) and generate Bland-Altman plots (BAP).METHODSHeidelberg AS-OCT was used to image 25 eyes from 14 healthy subjects and 25 eyes from 15 subjects with keratoconus between the ages of 20 and 80 years, collected prospectively, in this observational case-control study. Visual axis scan containing vertical fixation light beam was selected from the 15-line AS-OCT scan raster. Central corneal thickness (CCT), anterior corneal radius of curvature (ACRC), posterior corneal radius of curvature (PCRC), and truncated anterior vault (TAV) were measured using ImageJ software and the EyeMark Python program. MedCalc and R were used to calculate the intraclass correlation coefficient (ICC) and generate Bland-Altman plots (BAP).When comparing the measurements of CCT, ACRC, PCRC, and TAV between manual ImageJ analysis and the EyeMark Python program, ICC values were consistently greater than 0.9, indicating excellent agreement. BAPs comparing the ImageJ and Python measurements of anterior segment structures show no systematic proportional bias and the average differences were near zero and within 95% of the limits of agreement.RESULTSWhen comparing the measurements of CCT, ACRC, PCRC, and TAV between manual ImageJ analysis and the EyeMark Python program, ICC values were consistently greater than 0.9, indicating excellent agreement. BAPs comparing the ImageJ and Python measurements of anterior segment structures show no systematic proportional bias and the average differences were near zero and within 95% of the limits of agreement.Semi-automated tools may provide the necessary efficiency for point-of-care quantitative corneal analysis of raw AS-OCT images. The semi-automated EyeMark Python program offers a repeatable and reliable tool compared to manual ImageJ analysis for measuring anterior segment structures from AS-OCT images among individuals with keratoconus.CONCLUSIONSSemi-automated tools may provide the necessary efficiency for point-of-care quantitative corneal analysis of raw AS-OCT images. The semi-automated EyeMark Python program offers a repeatable and reliable tool compared to manual ImageJ analysis for measuring anterior segment structures from AS-OCT images among individuals with keratoconus. To assess the repeatability and reliability of semi-automated EyeMark Python program measurements compared to manual ImageJ image processing of anterior segment-optical coherence tomography (AS-OCT) structures in healthy and keratoconus eyes. Heidelberg AS-OCT was used to image 25 eyes from 14 healthy subjects and 25 eyes from 15 subjects with keratoconus between the ages of 20 and 80 years, collected prospectively, in this observational case-control study. Visual axis scan containing vertical fixation light beam was selected from the 15-line AS-OCT scan raster. Central corneal thickness (CCT), anterior corneal radius of curvature (ACRC), posterior corneal radius of curvature (PCRC), and truncated anterior vault (TAV) were measured using ImageJ software and the EyeMark Python program. MedCalc and R were used to calculate the intraclass correlation coefficient (ICC) and generate Bland-Altman plots (BAP). When comparing the measurements of CCT, ACRC, PCRC, and TAV between manual ImageJ analysis and the EyeMark Python program, ICC values were consistently greater than 0.9, indicating excellent agreement. BAPs comparing the ImageJ and Python measurements of anterior segment structures show no systematic proportional bias and the average differences were near zero and within 95% of the limits of agreement. Semi-automated tools may provide the necessary efficiency for point-of-care quantitative corneal analysis of raw AS-OCT images. The semi-automated EyeMark Python program offers a repeatable and reliable tool compared to manual ImageJ analysis for measuring anterior segment structures from AS-OCT images among individuals with keratoconus. |
| Author | Wei, Libby Maripudi, Snehaa Munir, Wuqaas M. Lin, Anna N. Munir, Saleha Z. Mohammed, Isa S. K. Alexander, Janet L. |
| Author_xml | – sequence: 1 givenname: Anna N. surname: Lin fullname: Lin, Anna N. organization: Department of Neurology, University of Maryland – sequence: 2 givenname: Isa S. K. surname: Mohammed fullname: Mohammed, Isa S. K. organization: Cole Eye Institute, Cleveland Clinic – sequence: 3 givenname: Wuqaas M. surname: Munir fullname: Munir, Wuqaas M. organization: Department of Ophthalmology and Visual Sciences, University of Maryland Eye Associates, University of Maryland – sequence: 4 givenname: Saleha Z. surname: Munir fullname: Munir, Saleha Z. organization: Department of Ophthalmology and Visual Sciences, University of Maryland Eye Associates, University of Maryland – sequence: 5 givenname: Snehaa surname: Maripudi fullname: Maripudi, Snehaa organization: Department of Ophthalmology, University of Texas Southwestern Medical Center – sequence: 6 givenname: Libby surname: Wei fullname: Wei, Libby organization: Department of Ophthalmology and Visual Sciences, University of Maryland Eye Associates, University of Maryland – sequence: 7 givenname: Janet L. surname: Alexander fullname: Alexander, Janet L. email: jalexander@som.umaryland.edu organization: Department of Ophthalmology and Visual Sciences, University of Maryland Eye Associates, University of Maryland, Department of Ophthalmology, University of Maryland School of Medicine |
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| Keywords | ImageJ Corneal image analysis EyeMark Python Keratoconus Anterior segment optical coherence tomography |
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To assess the repeatability and reliability of semi-automated EyeMark Python program measurements compared to manual ImageJ image processing of... To assess the repeatability and reliability of semi-automated EyeMark Python program measurements compared to manual ImageJ image processing of anterior... PurposeTo assess the repeatability and reliability of semi-automated EyeMark Python program measurements compared to manual ImageJ image processing of anterior... |
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| SubjectTerms | Automation Cornea Correlation coefficient Correlation coefficients Eye Image processing Keratoconus Light beams Medicine Medicine & Public Health Ophthalmology Optical Coherence Tomography Original Paper Radius of curvature Raster scanning Reliability Reproducibility Segments Tomography Visual observation |
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| Title | Repeatability and reliability of semi-automated anterior segment-optical coherence tomography imaging compared to manual analysis in normal and keratoconus eyes |
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