Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Sep 1.
Published in final edited form as: Eye Contact Lens. 2021 Sep 1;47(9):494–499. doi: 10.1097/ICL.0000000000000818

Inter-rater reliability and repeatability of manual anterior segment-OCT image grading in keratoconus

Anna N Lin 1, Isa S K Mohammed 2, Wuqaas M Munir 2, Saleha Z Munir 2, Janet L Alexander 2
PMCID: PMC8384674  NIHMSID: NIHMS1709976  PMID: 34294643

Abstract

Objectives:

To determine the repeatability of corneal measurements from anterior segment optical coherence tomography (AS-OCT) images using ImageJ software in healthy compared to eyes with keratoconus.

Methods:

AS-OCT images of 25 eyes from 14 healthy subjects and 25 eyes from 15 subjects with keratoconus between the ages of 20 and 80 years were evaluated. Two trained observers used ImageJ to measure the central corneal cross-sectional area and anterior and posterior corneal arc lengths. MedCalc statistical software was used to generate the intraclass correlation coefficient (ICC) and Bland-Altman plots (BAP) for observer measurements.

Results:

Observer measurements of central corneal cross-sectional area and anterior and posterior corneal arc lengths yielded an ICC > 0.7. The ICC comparing the three parameters ranged from 0.75 – 0.84 for the control and 0.96 – 0.98 for the keratoconus group. No systematic proportional bias was detected by the BAPs. There were minimal differences between the two observer’s measurements, with a mean of the difference of 0.3 mm2, 0 mm, and 0 mm, for the three measurements respectively.

Conclusions:

This study suggests that ImageJ software is repeatable and reliable tool in the analysis of corneal parameters from AS-OCT images among patients with keratoconus and may be applicable to AS-OCT imaging protocol development, an area of active keratoconus research.

Keywords: ImageJ, Keratoconus, central corneal cross-sectional area, corneal arc length, Anterior segment optical coherence tomography

Introduction

Keratoconus is a progressive, frequently asymmetric eye disease that is characterized by corneal stromal ectasia resulting in a conical-shaped cornea.1,2 The corneal thinning in keratoconus may be associated with an increase in the anterior and posterior curvatures of the cornea, irregular astigmatism, and myopia, resulting in mild to severe visual impairment.1,2 Keratoconus typically manifests at puberty and progresses until the third to fourth decade in adulthood, but the duration and severity of progression varies.3,4,5 Patients younger than age 30 are the most likely targets of primary preventive efforts or early treatment, since these patients are more likely to have progression of keratoconus and may require more invasive treatment modalities such as corneal transplantation.2 The reported prevalence of keratoconus can vary based on geographic region, diagnostic criteria used, and the sample of patients selected. The most commonly cited prevalence in the United States is 0.054% (1:2000) in Minnesota, USA by Kennedy et al. using keratometry and scissor movement on retinoscopy for diagnosis.5 Around the globe, the lowest documented keratoconus is 0.050% in North America, Europe, and Russia, and the highest is noted at 0.230% in the Middle East, India, and China.6,7 A systematic review by Hashemi et al recorded 0.138% as the prevalence of keratoconus in the worlds’ population.8 Interestingly, the highest prevalence yet has been noted in the Saudi population at 4.789% followed by 3.590% in the rural Iranian population.9,10 Greater prevalence of keratoconus has been shown by several studies occurring in the immigrant Southeast Asian (Pakistani, Indian, Bangladeshi), Saudi Arabian, and Iranian populations when compared to native European Caucasian populations, thus suggesting a possible role of ethnicity on the etiology of keratoconus.9,11–14

Keratoconus is one of the most common contraindications for refractive surgery, accounting for 24% of cases in one study.15 Patients undergoing refractive surgery are at risk for iatrogenic keratectasia and patients with subclinical keratoconus are at a significantly higher risk. Retrospective studies developed a risk factor stratification scale to predict this complication with sensitivity of 96% and specificity of 91%.16 The most significant risk factors are abnormal topography, followed by residual stromal bed thickness, age, and preoperative corneal thickness. However, there is no single metric to universally determine patients at high risk for corneal ectasia. Identifying the early signs of keratoconus remains challenging.

Computer assisted videokeratography, such as placido disc topography and Scheimpflug tomography imaging, can be used for the detection of corneal ectatic disorders such as keratoconus.3 A sign of early keratoconus on placido disc topography is localized steepening of the corneal curvature.17–20 However, topography maps are an imperfect sole screening tool. Placido-based topography is limited to the anterior and central cornea, neglecting the posterior corneal changes that may be a more sensitive indicator of ectatic change.21,22 Topography-based methods do not differentiate between contact lens-induced corneal warpage and forme fruste keratoconus, thereby reducing its accuracy.23 Ocular surface irregularity or tear film disruption may also result in unsatisfactory quality of corneal topography maps.24 Scheimpflug imaging also has its limitations including the low resolution and relatively poor quality of anterior segment scans.25 Compared to anterior segment optical coherence tomography (AS-OCT), which produces images with higher definition, Scheimpflug imaging may not measure key corneal parameters such as corneal epithelial thickness.25–28 Though Scheimpflug tomography can detect some subclinical keratoconus cases, there is still a considerable fraction of subclinical cases that go undetected by this technology.29 Studies have found that the measurements provided by AS-OCT imaging showed good repeatability in eyes with keratoconus, while the agreement of those measurements provided by the Scheimpflug camera combined with Placido disc corneal topography was low.25,30

The Global Consensus on Keratoconus and Ectatic Disease defined corneal ectatic progression as consistent change in at least two of the following parameters: steepening of the anterior corneal surface, steepening of the posterior corneal surface, and thinning and/or an increase in the rate of corneal thickness change from the periphery to the thinnest point.31 This profile of parameters can be reliably measured from the AS-OCT high-resolution cross-sectional images of the cornea.24,32–36 AS-OCT has been shown to have excellent inter-observer and intra-observer variability and is known to be highly reproducible in terms of repeatable image acquisition.37,42 AS-OCT may improve the sensitivity of the screening methods for identifying patients with early signs of keratoconus and to monitor disease progression.

Particular corneal parameters of interest for the diagnosis of keratoconus include central corneal cross-sectional area, anterior corneal arc length, and posterior corneal arc length.43,44 Anterior corneal arc length and posterior corneal arc length measures the arc-distance of the anterior and posterior corneal border between the scleral spurs, respectively (Figure 1). Central corneal cross-sectional area is a measure of the area bound between the anterior and posterior corneal arc lengths (Figure 1). Using ImageJ (v1.52p NIH) software, central corneal cross-sectional area, anterior corneal arc length, and posterior corneal arc length can be measured from the AS-OCT images. ImageJ is a public domain processing program that has been used to measure various parameters of the anterior segment structures with high reliability.45–47 Standardized imaging and measurement protocols of anterior segment structures compared the reliability of ImageJ measurements from ultrasound biomicroscope (UBM) imaging and demonstrated overall good interobserver repeatability and intraobserver agreement for 45 anterior segment structures.45 Quantitative corneal structural analysis using ImageJ has also been described in detail.48 However, the interobserver agreement of ImageJ measurements for corneal parameters in keratoconus derived for AS-OCT has not yet been studied. This study investigates the repeatability and interobserver reliability of ImageJ corneal measurements derived from AS-OCT in both healthy eyes and eyes with keratoconus.

Figure 1. ImageJ analysis on keratoconus eye.

Figure 1.

Open in a new tab

Each plane section was chosen based on the level of the incident beam transecting the pupil. No other images contained the incident beam perpendicular to the cornea. Based on the horizontal base line measured at 570 pixels in length, the anterior and posterior corneal arc lengths were measured with 20 continuous points on ImageJ. The central corneal cross-sectional area, in the highlighted region, was calculated based on the area under the curve of the posterior corneal arc subtracted from the anterior corneal arc.

Methodology

Heidelberg AS-OCT (Carl Zeiss Meditec AG, Jena, Germany) was used to image 25 eyes of 14 healthy subjects and 25 eyes of 15 keratoconus subjects between the ages of 20 and 80 years old, collected prospectively, in an observational case-control study. Patients with keratoconus were initially diagnosed using characteristic clinical signs on slit lamp examination and/or characteristic patterns on Placido-based topography, such as inferior steepening, asymmetric bowtie pattern with skewed radial axes, or stimulated keratometry greater than 47 diopters. We predicted an approximately 10% reduction in corneal cross-sectional area between healthy control and subjects with keratoconus. Assuming a standard deviation of approximately 10% percent, a type 1 error rate of 0.05, and a power of 0.80, a sample size of at least 20 patients was determined. Criterion for exclusion from this study included other anterior segment pathology, and pregnancy and nursing, which can affect corneal thickness, curvature, and biomechanics due to elevated estrogen levels.49,50 Selection of the right and left eye images was randomized with a random number generator, with right eye designated “0” and left eye designated “1”. A 15 line AS-OCT scan raster was performed, and the one image that included the fixation light beam, and thus encompassed the visual axis, was then selected for further analysis. Details of the AS-OCT imaging technology have been described previously.33,37,38

ImageJ (v1.52p NIH, Maryland, USA) was then used to analyze the 50 AS-OCT images.51 Two observers were trained: Observer 1 was an ophthalmology resident physician and Observer 2 was a medical student. Each trained observer then used a horizontal straight line 570 pixels in length (equivalent to 6 mm) as a baseline perpendicular to the incident light beam in order to establish the bounds for further measurements (Figure 1). From the baseline bounds, the right and left perpendicular corneal thicknesses and subjective corneal orthogonal thinnest location were measured. The anterior and posterior corneal arc lengths were measured with the segmented line tool with 20 continuous rectilinear lines (Figure 1). Using the multi-point feature, the central corneal cross-sectional area was measured by first mapping 20 (x, y) coordinates on both the anterior and posterior corneal curvatures (Figure 1). A 4th degree polynomial was created to satisfy the best fit for each of the anterior and posterior corneal surfaces (Figure 2). The area under the curve between 0 and 6 mm bounds from the anterior cornea was subtracted from that of the posterior cornea to obtain the final central corneal cross-sectional area.

Figure 2. Polynomial model of posterior corneal arc outline (top) and anterior corneal arc outline (bottom) of keratoconus eye.

Figure 2.

Open in a new tab

The polynomial models were constructed from the (x,y) values obtained from the ImageJ measurements of the posterior and anterior arcs. The central corneal cross-sectional area was obtained from subtracting the area under the curve of the posterior cornea from that of the anterior cornea.

Statistical analysis was performed with SAS Statistical Software (9.4 SAS Institute Incorporated, Cary, NC, USA), Excel (Microsoft, Redmond, WA, USA), and MedCalc Statistical Software version 16.4.3 (MedCalc Software bvba, Ostend, Belgium). Excel was used to construct a two-tailed t-test for comparing the mean of the three parameters between the control and keratoconus eyes, with the level of significance (α) set to 0.01 and power (1-β) at 90%. MedCalc was used to determine the intraclass correlation coefficient (ICC) between the first and second observers for the central corneal cross-sectional area and anterior and posterior corneal arc lengths. The ICC assesses the degree of consistency or reproducibility of these measurements made by different observers. The 95% limits of agreement (LOA) was calculated following the methods of Bland and Altman.52–54 Using MedCalc, the Bland-Altman plots (BAP) for each set of measurements were used to assess the degree of agreement between observers as it relates to the average values.

Results

Significant differences were identified between the control and keratoconus groups for the measurements of central corneal cross-sectional area, anterior corneal arc length, and posterior corneal arc length as shown in Table 1. All of the parameters showed a significant difference between the control and keratoconus groups (p < 0.01).

Table 1. Two-sample t-test comparing the mean difference between control and keratoconus group.

For Observer 1, the keratoconus measurements of corneal cross-sectional area (p=0.0015), posterior corneal arc length (p=0.0011) and anterior corneal arc length (p=0.0070) were significantly different from that of the control. Similarly, for Observer 2, the keratoconus measurements of corneal cross-sectional area (p=0.0038), posterior corneal arc length (p=0.0017), and anterior corneal arc length (p=0.0026) were significantly different from that of the control.

Measurement Control Keratoconus P-value t-stat t-critical df
Observer 1 Cross Sectional Area (mm2) 3.22 ± 0.041 2.95 ± 0.110 0.0015 3.41 2.023 48
Posterior Corneal Arc Length (mm) 6.23 ± 0.001 6.30 ± 0.007 0.0011 −3.62 2.042 48
Anterior Corneal Arc Length (mm) 6.16 ± 0.0004 6.20 ± 0.003 0.0070 −2.89 2.039 48
Observer 2 Cross Sectional Area (mm2) 3.19 ± 0.055 2.94 ± 0.120 0.0038 3.06 2.018 48
Posterior Corneal Arc Length (mm) 6.23 ± 0.001 6.29 ± 0.007 0.0017 −3.44 2.037 48
Anterior Corneal Arc Length (mm) 6.16 ± 0.0003 6.19 ± 0.003 0.0026 −3.29 2.042 48
Open in a new tab

To understand the reliability of the measurements between observers, the mean difference for each measurement was calculated as shown in Table 2 and 3. Between both observers, there were minimal differences in the ImageJ measurements of corneal central cross-sectional area (mean difference=0.03 mm2), anterior corneal arc length (mean difference=0 mm), and posterior corneal arc length (mean difference=0 mm) in the control and keratoconus groups. The measurements of the parameters demonstrated an overall ICC > 0.7. The ICC comparing the control group measurements of the three parameters ranged between 0.75 – 0.84. The ICC comparing the keratoconus group measurements of the three parameters ranged between 0.96 – 0.98 (Table 2, 3).

Table 2. ICC measurements for control parameters.

ICC analysis of measurements for control group cross-sectional area, posterior corneal arc length, and anterior corneal arc length showed consistency and agreement with ICC > 0.7.

Measurement Single measures* ICC, 95% CI Average measures** ICC, 95% CI Number of images
Cross Sectional Area (mm2) 0.78, (0.57 - 0.90) 0.88, (0.73 - 0.95) 25
Posterior Corneal Arc Length (mm) 0.84, (0.67 - 0.93) 0.91, (0.80 - 0.96) 25
Anterior Corneal Arc Length (mm) 0.75, (0.51 - 0.88) 0.86, (0.67 - 0.94) 25
Open in a new tab
*

Estimates the reliability of single ratings.

**

Estimates the reliability of averages of k=2 ratings.

Table 3. ICC measurements for keratoconus parameters.

ICC analysis measurements for keratoconus group cross-sectional area, posterior corneal arc length, and anterior corneal arc length showed consistency and agreement with ICC > 0.7.

Measurement Single measures* ICC, 95% CI Average measures** ICC, 95% CI Number of images
Cross Sectional Area (mm2) 0.97, (0.93 - 0.98) 0.98, (0.96 - 0.99) 25
Posterior Corneal Arc Length (mm) 0.98, (0.96 - 0.99) 0.99, (0.98 - 1.00) 25
Anterior Corneal Arc Length (mm) 0.96, (0.91 - 0.98) 0.98, (0.95 - 0.99) 25
Open in a new tab
*

Estimates the reliability of single ratings.

**

Estimates the reliability of averages of k=2 ratings.

No systematic proportional bias was detected by the BAPs (Supplementary Figures 1–6). The differences between the two observers’ Image J measurements for all parameters were small and generally stayed within the upper and lower 95% limits of agreement. 95% of the measurements for corneal cross-sectional area by the two observers agreed within −0.260 to 0.307 for the control group and −0.163 to 0.187 for the keratoconus groups (SF 1, 4). For the posterior corneal arc length, 95% of the observers’ measurements agreed within −0.039 to 0.034 and −0.029 to 0.038 for control and keratoconus groups, respectively (SF 2, 5). 95% of the measurements for anterior corneal arc length agreed to within −0.026 to 0.027 in the control group and −0.032 to 0.027 in the keratoconus group (SF 3, 6).

Discussion

This study demonstrates that manual ImageJ measurements of corneal metrics attained from AS-OCT images may be reliably measured by trained observers. The use of ImageJ to analyze anterior segment structures from corneal imaging is a relatively novel method that has been used by various studies with high intraobserver reproducibility.47–49 Our results similarly showed that ImageJ measurements of central corneal cross-sectional area, anterior corneal arc length, and posterior corneal arc length had an overall good to excellent measurement repeatability. The BAPs in this study demonstrate minimal differences between the two observers’ measurements, with the mean of the differences near zero for all parameters. The average discrepancy between the ImageJ gradings for all parameters is −0.002 to 0.023, less than 1% of the associated measurement. Given that the mean of the difference between observers is 0.3 mm2, 0 mm, and 0 mm for corneal cross-sectional area, anterior and posterior corneal arc lengths, ImageJ can be a precise tool for these parameter measurements. Reliable measurements of the anterior segment and corneal parameters have been shown to be crucial for assessment of candidates for refractive surgery. Previous studies have shown that good repeatability and reproducibility were obtained for anterior segment structures of the eye, such as central corneal thickness, when using Scheimpflug and AS-OCT systems.55–57 However, although these Scheimpflug measurements of the anterior segment parameters were reliable, their agreement with the AS-OCT measurements was not sufficient to enable these devices to be used interchangeably. For the precision of high resolution OCT, it has been shown to have good precision outcomes to measure the anterior segment of the eye, such that the repeatability of the OCT used did not depend on the observer who takes the measurement.58

The overall repeatability of the ImageJ measurements is excellent (ICC > 0.7) and is higher in the keratoconus group (ICC 0.96 - 0.98) compared to controls (ICC 0.75 - 0.84). The increased variation in the measurement of control eyes for corneal arc length and cross-sectional area than that of the keratoconus eyes is likely because localized thinning is more easily identified subjectively from the AS-OCT images in keratoconus compared to healthy eyes, and possibly due to more accuracy of steeper curvature measurement than flatter curvature using ImageJ. The excellent ICC scores for the keratoconus group show low variability between observers and within the group overall. The best agreement in measurement was for posterior corneal arc length in both the control and keratoconus groups. These findings demonstrate that posterior corneal arc length may serve as a reliable metric for the identification of forme fruste keratoconus eyes. This may suggest that high levels of reliability and repeatability may be less likely when applying this measurement methodology to disorders associated with corneal flattening, such as cornea plana. Results should be applied with caution or evaluated directly in the clinical use case of corneal flattening.

Determining the repeatability and reliability of this method can benefit the target population of forme fruste keratoconus patients who may be eligible for refractive surgery. This study established good reliability, but future studies will be needed to evaluate the specificity and sensitivity of this methodology in early keratoconus detection. Earlier detection of keratoconus will allow for improved screening for refractive surgery and may identify patients most likely to benefit from early intervention with corneal cross-linking to decrease the incidence of advanced keratoconus.59

Limitations in the study include the small sample size and the limited number of observers. Despite the limited sample size of this pilot study, statistically significant differences existed in all three metrics. To address the potential for observer bias, observers were masked, received the same instructional training, and conducted measurements independently.

This study expands upon the novel use of ImageJ as a reliable and reproducible tool to measure various corneal metrics, further refining future detection of keratoconus. It may be useful for future studies to compare the manual measurements of ImageJ to semi-automated or automated program measurements of anterior segment structures in a larger population of keratoconus patients. Automated analysis derived from AS-OCT has the potential to reliably measure corneal parameters that may be clinically relevant in the detection of keratoconus.

Supplementary Material

Supplemental Data File (.doc, .tif, pdf, etc.)_1
Supplemental Data File (.doc, .tif, pdf, etc.)_2
Supplemental Data File (.doc, .tif, pdf, etc.)_3
Supplemental Data File (.doc, .tif, pdf, etc.)_4
Supplemental Data File (.doc, .tif, pdf, etc.)_5
Supplemental Data File (.doc, .tif, pdf, etc.)_6
Supplemental Data File (.doc, .tif, pdf, etc.)_7

Acknowledgements:

This research was funded in part by the Program for Research Initiated by Students and Mentors (PRISM), University of Maryland School of Medicine Office of Student Research.

We acknowledge the support of the University of Maryland, Baltimore, Institute for Clinical & Translational Research (ICTR) and the National Center for Advancing Translational Sciences (NCATS) Clinical Translational Science Award (CTSA) grant number IUL1TR003098, grant 1KL2TR003099-01.

References

  • 1.Ambekar R, Toussaint KC, Johnson AW. The effect of keratoconus on the structural, mechanical, and optical properties of the cornea, J Mech Behav Biomed Mater. 2011;4(3):223–236. 10.1016/j.jmbbm.2010.09.014. [DOI] [PubMed] [Google Scholar]
  • 2.Reeves SW, Stinnett S, Adelman RA, et al. Risk factors for progression to penetrating keratoplasty in patients with keratoconus. Am J Ophthalmol. 2005;140(4):607–611. 10.1016/j.ajo.2005.05.029 [DOI] [PubMed] [Google Scholar]
  • 3.Choi JA, Kim MS. Progression of keratoconus by longitudinal assessment with corneal topography. Invest Ophthalmol Vis Sci. 2012;53(2):927–935. 10.1167/iovs.11-8118 [DOI] [PubMed] [Google Scholar]
  • 4.Romero-Jiménez M, Santodomingo-Rubido J, Wolffsohn JS. Keratoconus: A review. Cont Lens Anterior Eye. 2010;33(4):157–166. 10.1016/j.clae.2010.04.006 [DOI] [PubMed] [Google Scholar]
  • 5.Kennedy RH, Bourne WM, Dyer JA. A 48-year clinical and epidemiologic study of keratoconus. Am J Ophthalmol. 1986;101(3):267–273. 10.1016/0002-9394(86)90817-2 [DOI] [PubMed] [Google Scholar]
  • 6.Woodward MA, Blachley TS, Stein JD. The Association Between Sociodemographic Factors, Common Systemic Diseases, and Keratoconus. Ophthalmology 2016;123:457–465.e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 7.Gordon-Shaag A, Millodot M, Shneor E, Liu Y. The genetic and environmental factors for keratoconus. BioMed Res Int 2015;2015:1–19. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Hashemi H, Heydarian S, Hooshmand E, et al. The prevalence and risk factors for keratoconus: A systematic review and meta-analysis. Cornea. 2019;39(2):263–270. 10.1097/ICO.0000000000002150 [DOI] [PubMed] [Google Scholar]
  • 9.Torres Netto EA, Al-Otaibi WM, Hafezi NL, et al. Prevalence of keratoconus in paediatric patients in Riyadh, Saudi Arabia. Br J Ophthalmol 2018;102:1436–1441. [DOI] [PubMed] [Google Scholar]
  • 10.Hashemi H, Heydarian S, Yekta A, et al. High prevalence and familial aggregation of keratoconus in an Iranian rural population: a population-based study. Ophthalmic Physiol Opt J Br Coll Ophthalmic Opt Optom 2018;38:447–455. [DOI] [PubMed] [Google Scholar]
  • 11.Pearson AR, Soneji B, Sarvananthan N, Sandford-Smith JH. Does ethnic origin influence the incidence or severity of keratoconus? Eye Lond Engl 2000;14 ( Pt 4):625–628. [DOI] [PubMed] [Google Scholar]
  • 12.Bak-Nielsen S, Ramlau-Hansen CH, Ivarsen A, et al. A nationwide population-based study of social demographic factors, associated diseases and mortality of keratoconus patients in Denmark from 1977 to 2015. Acta Ophthalmol (Copenh) 2018;97:497–504. [DOI] [PubMed] [Google Scholar]
  • 13.Ziaei H, Jafarinasab MR, Javadi MA, et al. Epidemiology of keratoconus in an Iranian population. Cornea 2012;31:1044–1047. [DOI] [PubMed] [Google Scholar]
  • 14.Godefrooij DA, de Wit GA, Uiterwaal CS, et al. Age-specific Incidence and Prevalence of Keratoconus: A Nationwide Registration Study. Am J Ophthalmol 2017;175:169–172. [DOI] [PubMed] [Google Scholar]
  • 15.Al-Amri AM. Prevalence of keratoconus in a refractive surgery population. Ophthalmology. 2018;2018:1–5. 10.1155/2018/5983530. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 16.Randleman JB, Woodward M, Lynn MJ, et al. Risk assessment for ectasia after corneal refractive surgery. Ophthalmology. 2008;115(1):37–50. 10.1016/j.ophtha.2007.03.073 [DOI] [PubMed] [Google Scholar]
  • 17.Rabinowitz YS, McDonnell PJ. Computer-assisted corneal topography in keratoconus. Refract Corneal Surg. 1989;5(6):400–408. [PubMed] [Google Scholar]
  • 18.Schlegel Z, Hoang-Xuan T, Gatinel D. Comparison of and correlation between anterior and posterior corneal elevation maps in normal eyes and keratoconus-suspect eyes. J Cataract Refract Surg. 2008;34(5):789–795. 10.1016/j.jcrs.2007.12.036 [DOI] [PubMed] [Google Scholar]
  • 19.Rabinowitz YS, Nesburn AB, McDonnell PJ. Videokeratography of the fellow eye in unilateral keratoconus. Ophthalmology. 1993;100(2):181–186. 10.1016/S0161-6420(93)31673-8 [DOI] [PubMed] [Google Scholar]
  • 20.Rabinowitz YS, Rasheed K. KISA% index: a quantitative videokeratography algorithm embodying minimal topographic criteria for diagnosing keratoconus. J Cataract Refract Surg. 1999;25(10):1327–1335. 10.1016/S0886-3350(99)00195-9 [DOI] [PubMed] [Google Scholar]
  • 21.Belin MW, Ratliff CD. Evaluating data acquisition and smoothing functions of currently available videokeratoscopes. J Cataract Refract Surg. 1996;22(4):421–426. [DOI] [PubMed] [Google Scholar]
  • 22.Ambrósio R, Caiado ALC, Guerra FP, et al. Novel pachymetric parameters based on corneal tomography for diagnosing keratoconus. J Refract Surg. 2011;27(10):753–758. 10.3928/1081597X-20110721-01 [DOI] [PubMed] [Google Scholar]
  • 23.Wilson SE, Lin DTC, Klyce SD. Corneal topography of keratoconus. Cornea. 1991;10(1):2–8. [PubMed] [Google Scholar]
  • 24.Li Y, Meisler DM, Tang M, et al. Keratoconus diagnosis with optical coherence tomography pachymetry mapping. Ophthalmology. 2008;115(12):2159–66. 10.1016/j.ophtha.2008.08.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.Schiano-Lomoriello D, Bono V, Abicca I, et al. Repeatability of anterior segment measurements by optical coherence tomography combined with Placido disk corneal topography in eyes with keratoconus. Sci Rep. 2020;10(1124). 10.1038/s41598-020-57926-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 26.Li Y, Tan O, Brass R, et al. Corneal epithelial thickness mapping by Fourier-domain optical coherence tomography in normal and keratoconic eyes. Ophthalmology. 2012;119(12):2425–2433. 10.1016/j.ophtha.2012.06.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 27.Ma XJ, Wang L, Koch DD. Repeatability of corneal epithelial thickness measurements using Fourier-domain optical coherence tomography in normal and post-LASIK eyes. Cornea. 2013;32(12):1544–1548. 10.1097/ICO.0b013e3182a7f39d. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 28.Rocha KM, Perez-Straziota CE, Stulting RD, et al. SD-OCT analysis of regional epithelial thickness profiles in keratoconus, postoperative corneal ectasia, and normal eyes. J. Refract. Surg 2013;29(3):173–179. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 29.Vazquez PRR, Galletti JD, Minguez N, et al. Pentacam Scheimpflug tomography findings in topographically normal patients and subclinical keratoconus cases. Am J Ophthalmol. 2014;158(1):32–40. 10.1016/j.ajo.2014.03.018 [DOI] [PubMed] [Google Scholar]
  • 30.Shetty R, Arora V, Jayadev C, et al. Repeatability and agreement of three Scheimpflug-based imaging systems for measuring anterior segment parameters in keratoconus. Investig Ophthalmol Vis Sci. 2014;55(8):5263–5268. 10.1167/iovs.14-15055 [DOI] [PubMed] [Google Scholar]
  • 31.Gomes JAP, Tan D, Rapuano CJ, et al. Global consensus on keratoconus and ectatic diseases. Cornea. 2015;34(4):359–369. 10.1097/ICO.0000000000000408 [DOI] [PubMed] [Google Scholar]
  • 32.Fukuda S, Beheregaray S, Hoshi S, et al. Comparison of three-dimensional optical coherence tomography and combining a rotating Scheimpflug camera with a placido topography system for forme fruste keratoconus diagnosis. Br J Ophthalmol. 2013;97(12):1554–9. 10.1136/bjophthalmol-2013-303477 [DOI] [PubMed] [Google Scholar]
  • 33.Tan DK, Chong W, Tay WT, et al. Anterior chamber dimensions and posterior corneal arc length in Malay eyes. Invest Ophthalmol Vis Sci. 2012;53(8):4860–4867. 10.1167/iovs.12-9787 [DOI] [PubMed] [Google Scholar]
  • 34.Doors M, Tahzib NG, Eggink FA, et al. Use of anterior segment optical coherence tomography to study corneal changes after collagen cross-linking. Am J Ophthalmol. 2009;148(6):844–851 [DOI] [PubMed] [Google Scholar]
  • 35.Kanellopoulos AJ, Asimellis G. Comparison of high-resolution Scheimpflug and high-frequency ultrasound biomicroscopy to anterior-segment OCT corneal thickness measurements. Clin Ophthalmol. 2013;7:2239–47. 10.2147/OPTH.S53718 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Ang M, Baskaran M, Werkmeister RM, et al. Anterior segment optical coherence tomography. Prog Retin Eye Res. 2018;66:132–156. 10.1016/j.preteyeres.2018.04.002 [DOI] [PubMed] [Google Scholar]
  • 37.Müler M, Dahman G, Pörksen E. Anterior chamber angle measurement with optical coherence tomography: intraobserver and interobserver variability. J Refract Cataract Surg. 2006;32:1803–1808 [DOI] [PubMed] [Google Scholar]
  • 38.Dorairaj S, Liebmann JM, Ritch R. Quantitative evaluation of anterior segment parameters in the era of imaging. Trans Am Ophthalmol Soc. 2007;105:99–108, 110 [PMC free article] [PubMed] [Google Scholar]
  • 42.Yeung D, Sorbara L. Estimation of apical axial curvature using anterior segment optical coherent tomography compared to corneal topography. Cont Lens Anterior Eye. 2018;41(1):S56. 10.1016/j.clae.2018.03.043 [DOI] [Google Scholar]
  • 43.Burger W, Burge MJ. Digital image processing: An algorithmic approach using java. Berlin, Germany: Springer; 2007. [Google Scholar]
  • 44.Dougherty G Digital image processing for medical applications. Cambridge, UK: Cambridge University Press; 2009 [Google Scholar]
  • 45.Qureshi A, Chen H, Saeedi O, et al. Anterior segment ultrasound biomicroscopy image analysis using ImageJ software: Intra-observer repeatability and inter-observer agreement. Int Ophthalmol. 2019;39(4):829–837. 10.1007/s10792-018-0882-6 [DOI] [PubMed] [Google Scholar]
  • 46.Sakata LM, Lavanya R, Friedman DS, et al. Assessment of the scleral spur in anterior segment optical coherence tomography images. Arch Ophthalmol 2008;126(2):181–185. 10.1001/archophthalmol.2007.46 [DOI] [PubMed] [Google Scholar]
  • 47.Seager FE, Wang J, Arora KS, et al. The effect of scleral spur identification methods on structural measurements by anterior segment optical coherence tomography. J Glaucoma. 2014;23(1):e29–38. 10.1097/IJG.0b013e31829e55ae [DOI] [PubMed] [Google Scholar]
  • 48.Maripudi S, Byrd J, Qureshi A, et al. Pediatric corneal structural development during childhood characterized by ultrasound biomicroscopy. J Pediatr Ophthalmol Strabismus. 2020;57(4):238–245. 10.3928/01913913-20200506-01 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 49.Spoerl E, Zubaty V, Raiskup-Wolf F, et al. Oestrogen-induced changes in biomechanics in the cornea as a possible reason for keratectasia. Br J Ophthalmol. 2007;91(11):1547–1550. 10.1136/bjo.2007.124388 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Aydin E, Demir HD, Demirturk F, et al. Corneal topographic changes in premenopausal and postmenopausal women. BMC Ophthalmol. 2007;7(9). 10.1186/1471-2415-7-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 51.Rasband WS. ImageJ. 1997. Available at: https://imagej.nih.gov/ij.Accessed June 17, 2019
  • 52.Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1(8476):307–310. 10.1016/S0140-6736(86)90837-8 [DOI] [PubMed] [Google Scholar]
  • 53.Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8(2):135–160. 10.1177/096228029900800204 [DOI] [PubMed] [Google Scholar]
  • 54.Bland JM, Altman DG. Applying the right statistics: analyses of measurement studies. Ultrasound Obstet Gynecol. 2003;22(1):85–93. 10.1002/uog.122 [DOI] [PubMed] [Google Scholar]
  • 55.O’Donnell C, Maldonado-Codina C. Agreement and repeatability of central thickness measurement in normal corneas using ultrasound pachymetry and the OCULUS Pentacam. Cornea. 2005;24(8):920–4. 10.1097/01.ico.0000157422.01146.e9 [DOI] [PubMed] [Google Scholar]
  • 56.Li Y, Shekhar R, Huang D. Corneal pachymetry mapping with high-speed optical coherence tomography. Ophthalmology. 2006;113(5):792–9. 10.1016/j.ophtha.2006.01.048 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 57.Fu J, Wang X, Li S, et al. Comparative study of anterior segment measurement with Pentacam and anterior segment optical coherence tomography. Can J Ophthalmol. 2010;45(6):627–31 10.3129/i10-068 [DOI] [PubMed] [Google Scholar]
  • 58.Domínguez-Vicent A, Trussardi C, Wogatai U, et al. Precision of high-resolution OCT for anterior chamber measurement: Agreement with Scheimpflug imaging. J Refract Surg. 2016;32(11):766–772. 10.3928/1081597X-20160721-03 [DOI] [PubMed] [Google Scholar]
  • 59.Raiskup F, Theuring A, Pillunat LE, et al. Corneal collagen crosslinking with riboflavin and ultraviolet-A light in progressive keratoconus: ten-year results. J Cataract Refract Surg. 2015;41(1):41–46. 10.1016/j.jcrs.2014.09.033 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplemental Data File (.doc, .tif, pdf, etc.)_1
NIHMS1709976-supplement-Supplemental_Data_File___doc___tif__pdf__etc___1.jpg (167.2KB, jpg)
Supplemental Data File (.doc, .tif, pdf, etc.)_2
NIHMS1709976-supplement-Supplemental_Data_File___doc___tif__pdf__etc___2.jpg (142.8KB, jpg)
Supplemental Data File (.doc, .tif, pdf, etc.)_3
NIHMS1709976-supplement-Supplemental_Data_File___doc___tif__pdf__etc___3.jpg (174.2KB, jpg)
Supplemental Data File (.doc, .tif, pdf, etc.)_4
NIHMS1709976-supplement-Supplemental_Data_File___doc___tif__pdf__etc___4.jpg (134.6KB, jpg)
Supplemental Data File (.doc, .tif, pdf, etc.)_5
NIHMS1709976-supplement-Supplemental_Data_File___doc___tif__pdf__etc___5.jpg (153.8KB, jpg)
Supplemental Data File (.doc, .tif, pdf, etc.)_6
NIHMS1709976-supplement-Supplemental_Data_File___doc___tif__pdf__etc___6.jpg (155.8KB, jpg)
Supplemental Data File (.doc, .tif, pdf, etc.)_7
NIHMS1709976-supplement-Supplemental_Data_File___doc___tif__pdf__etc___7.docx (15.8KB, docx)

RESOURCES